Effective Factory Scheduling with a Simio Digital Twin

Introduction

In today’s world, companies compete not only on price and quality, but on their ability to reliably deliver product on time.   A good production schedule, therefore, influences a company’s throughput, sales and customer satisfaction.  Although companies have invested millions in information technology for Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES), the investment has fallen short on detailed production scheduling, causing most companies to fall back on manual methods involving Excel and planning boards.  Meanwhile, industry trends towards reduced inventory, shorter lead times, increased product customization, SKU proliferation, and flexible manufacturing are making the task more complicated.  Creating a feasible plan requires simultaneous consideration of materials, labor, equipment, and demand.  This bar is simply too high for any manual planning method.  The challenge of creating a reliable plan requires a digital transformation which can support automated and reliable scheduling.

Central to the idea of effective factory scheduling is the concept of an actionable schedule.  An actionable schedule is one that fully accounts for the detailed constraints and operating rules in the system and can therefore be executed in the factory by the production staff.   An issue with many scheduling solutions is that they ignore one or more detailed constraints, and therefore cannot be executed as specified on the factory floor.  A non-actionable schedule requires the operators to step in and override the planned schedule to accommodate the actual constraints of the system.   At this point the schedule is no longer being followed, and local decisions are being made that impact the system KPIs in ways that are not visible to the operators.

A second central idea of effective scheduling is properly accounting for variability and unplanned events in the factory and the corresponding detrimental impact on throughput and on-time delivery.  Most scheduling approaches completely ignore this critical element of the system, and therefore produce optimistic schedules that cannot be met in practice.   What starts off looking like a feasible schedule degrades overtime as machines break, workers call off sick, materials arrive late, rework is required, etc.  The optimistic promises that were made cannot be kept.

A third consideration is the effect of an infeasible schedule on the supply chain plan.  Factory scheduling is only the final step in the production planning process, which begins with supply chain planning based on actual and/or forecast demand.   The supply chain planning process generates production orders and typically establishes material requirements for each planning period across the entire production network.  The production orders that are generated for each factory in the network during this process are based on a rough-cut model of the production capacity.  The supply chain planning process has very limited visibility of the true constraints of the factory, and the resulting production requirements often overestimate the capacity of the factory.  Subsequently, the factory schedulers must develop a detailed plan to meet these production requirements given the actual constraints of the equipment, workforce, etc.  The factory adjustments to make the plan actionable will not be transparent to the supply chain planners.  This creates a disconnect in a core business planning function where enormous spending occurs. 

In this paper we will discuss the solution to these challenges, the Process Digital Twin, and the path to get there.  The Simio Digital Twin solution is built on the patented Simio Risk-based Planning and Scheduling (RPS) software.   We will begin by describing and comparing the three common approaches to factory scheduling.  We will then discuss in detail the advantages of a process Digital Twin for factory scheduling built on Simio RPS.  

Factory Scheduling Approaches

Let’s begin by discussion the three most common approaches to solving the scheduling problem in use today:  1) manual methods using planning boards or spreadsheets, 2) resource models, and 3) process Digital Twin.

Manual Methods

The most common method in use today for factory scheduling is the manual method, typically augmented with spreadsheets or planning boards.   The use of manual scheduling is typically not the companies first choice but is the result of failure to succeed with automated systems.

Manually generating a schedule for a complex factory is a very challenging task, requiring a detailed understanding of all the equipment, workforce, and operational constraints.  Five of the most frustrating drawbacks include:

  • It is difficult for a scheduler to consider all the critical constraints.   While schedulers can typically focus on primary constraints, they are often unaware – or must ignore – secondary constraints, and these omissions lead to a non-actionable schedule.
  • Manual scheduling typically takes hours to complete, and the moment any change occurs the schedule becomes non-actionable. 
  • The quality of the schedule is entirely dependent on the knowledge and skill of the scheduler.  If the scheduler retires is out for vacation or illness, the backup scheduler may be less skilled and the KPIs may degrade.
  • It is virtually impossible for the scheduler to account for the degrading effect of variation on the schedule and therefore provide confident completion times for orders. 
  • As critical jobs become late, manual schedulers resort to bumping other jobs to accommodate these “hot” jobs, disrupting the flow and creating more “hot” jobs.  The system becomes jerky and the system dissolves into firefighting.

Resource Model

Companies that utilize an automated method for factory scheduling typically use an approach based on a resource model of the factory.   A resource model is comprised of a list of critical resources with time slots allocated to tasks that must be processed by the resource based on estimated task times.   The resource list includes machines, fixtures, workers, etc., that are required for production.   The following is a Gantt chart depicting simple resource model with four resources (A, B, C, D) and two jobs (blue, red).  The blue job has task sequence A, D, and B, and the red job has task sequence A and B.

The resources in a resource model are defined by a state that can be busy, idle, or off-shift.  When a resource is busy with one task or off-shift, other tasks must wait to be allocated to the resource (e.g. red waits for blue on resource A).  The scheduling tools that are based on a resource model all share this same representation of the factory capacity and differ only in how tasks are assigned to the resources.

The problem that all these tools share is an overly simplistic constraint model.   Although this model may work in some simple applications, there are many constraints in factories that can’t be represented by a simple busy, idle, off-shift state for a resource.  Consider the following examples:

  • A system has two cranes (A and B) on a runway that are used to move aircraft components to workstations.   Although crane A is currently idle, it is blocked by crane B and therefore cannot be assigned the task.
  • A workstation on production line 1 is currently idle and ready to begin a new task.   However, this workstation has only limited availability when a complex operation is underway on adjacent line 2.
  • An assembly operator is required for completing assembly.   There are assembly operators currently idle, but the same operator that was assigned to the previous task must also be used on this task, and that operator is currently busy.
  • A setup operator is required for this task.  The operator is idle but is in the adjacent building and must travel to this location before setup can start.
  • The tasks involve the flow of fluid through pipes, valves, and storage/mixing tanks, and the flow is limited by complex rules.
  • A job requires treatment in an oven, the oven is idle but not currently at the required temperature.

This is just a few examples of typical constraints for which a simple busy, idle, off-shift resource model is inadequate.  Every factory has its own set of such constraints that limit the capacity of the facility.  

The scheduling tools that utilize a simple resource model allocate tasks to the resources using one of three basic approaches; heuristics, optimization, and simulation.

One common heuristic is job-sequencing that begins with the highest priority job, and assigns all tasks for that job, and repeats this process for each job until all jobs are scheduled (in the previous example blue is sequenced, then red).  This simple approach to job sequencing can be done in either a forward direction starting with the release date, or a backward direction starting with the due date.   Note that backward sequencing (while useful in master planning) is typically problematic in detailed scheduling because the resulting schedule is fragile and any disruption in the flow of work will create a tardy job.  This simple one-job-at-a-time sequencing heuristic cannot accommodate complex operating rules such as minimizing changeovers or running production campaigns based on attributes such as size or color.  However, there have been many different heuristics developed over time to accommodate special application requirements.  Examples of scheduling tools that utilize heuristics include Preactor from Siemens and PP/DS from SAP.

The second approach to assigning tasks to resources in the resource model is optimization, in which the task assignment problem is formulated as a set of sequencing constraints that must be satisfied while meeting an objective such as minimizing tardiness or cost.   The mathematical formulation is then “solved” using a Constraint Programming (CP) solver.  The CP solver uses heuristic rules for searching for possible task assignments that meet the sequencing constraints and improve the objective.  Note that there is no algorithm that can optimize the mathematical formulation of the task assignment for the resource model in a reasonable time (this problem is technically classified as NP Hard), and hence the available CP solvers rely on heuristics to find a “practical” but not optimal solution.   In practice, the optimization approach has limited application because often long run times (hours) are required to get to a good solution.   Although PP/DS incorporates the CP solver from ILOG to assign tasks to resources, most installations of PP/DS rely on the available heuristics for task assignments.

The third approach to assigning tasks in the simple resource model is a simulation approach.   In this case we simulate the flow of jobs through the resource model of the factory and assign tasks to available resources using dispatching rules such as smallest changeover or earliest completion.   This approach has several advantages over the optimization approach.   First, it executes much faster, producing a schedule in minutes instead of hours.  Another key advantage is that it can support custom decision logic for allocating tasks to resources.  An example of tool that utilizes this approach is Preactor 400 from Siemens. 

Regardless which approach is used to assign tasks to resources, the resulting schedule assumes away all random events and variation in the system.  Hence the resulting schedules are optimistic and lead to overpromising of delivery times to customers.  These tools provide no mechanism for assessing the related risk with the schedule.

Digital Twin

The third and latest approach to factory scheduling is a process Digital Twin of the factory.  A Digital Twin is a digital replica of the processes, equipment, people, and devices that make up the factory and can be used for both system design and operation.  The resources in the system not only have a busy, idle, and off-shift state, but they are objects that have behaviors and can move around the system and interact with the other objects in the model to replicate the behavior and detailed constraints of the real factory. The Digital Twin brings a new level of fidelity to scheduling that is not available in the existing resource-based modeling tools.

Simio Digital Twin

The Simio Digital Twin is an object-based, data driven, 3D animated model of the factory that is connected to real time data from the ERP, MES, and related data sources.   We will now summarize the key advantages of the Simio Digital Twin as a factory scheduling solution.

Dual Use: System Design and Operation

Although the focus here is on enhancing throughput and on-time delivery by better scheduling using the existing factory design, unlike traditional scheduling tools, the Simio Digital Twin can also be used to optimize the factory deign.  The same Simio model that is used for factory scheduling can be used to test our changes to the facility such as adding new equipment, changing staffing levels, consolidating production steps, adding buffer inventory, etc.                 

Actionable Schedules

A basic requirement of any scheduling solution is that it provide actionable schedules that can implemented in the real factory.   If a non-actionable production schedule is sent to the factory floor, the production staff have no choice to be ignore the schedule and make their own decisions based on local information.

For a schedule to be actionable, it must capture all the detailed constraints of the system.  Since the foundation of the Simio Digital Twin is an object-based modeling tool, the factory model can capture all these constraints in as much detail as necessary.  This includes complex constraints such as material handling devices, complex equipment, workers with different skill sets, and complex sequencing requirements,          

In many systems there are operating rules that have been developed over time to control the production processes.  These operating rules are just as important to capture as the key system constraints; any schedule that ignores these operating rules is non-actionable.  The Simio modeling framework has flexible rule-based decision logic for implementing these operating rules.  The result is an actionable schedule that respects both the physical constraints of the system as well as the standard operating rules.    

Fast Execution

In most organizations, the useful life of a schedule is short because unplanned events and variation occur that make the current schedule invalid.   When this occurs, a new schedule must be regenerated and distributed as immediately as possible, to keep the production running smoothly.  A manual or optimization-based approach to schedule regeneration that takes hours to complete is not practical; in this case the shop floor operators will take over and implement their own local scheduling decisions that may not aligned with the system-wide KPIs.  When random events occur, the Simio Digital Twin can quickly respond and generate and distribute a new actionable schedule.  Schedule regeneration can either be manually triggered by the scheduler, or automatically triggered by events in the system.

3D Animated Model and Schedule

In other scheduling systems the only graphical view of the model and schedule is the resource Gantt chart.  In contrast, the Simio Digital Twin provides a powerful communication and visualization of both the model structure and resulting schedule.  Ideally, anyone in the organization – from the shop floor to the top floor – should be able to view and understand the model well enough to validate its structure.  A good solution improves not only the ability to generate an actionable schedule, but to visualize it and explain it across all levels of the organization. 

The Simio Gantt chart has direct link to the 3D animated facility; right click on a resource along the time scale in the Gantt view and you instantly jump to an animated view of that portion of facility – showing the machines, workers, and work in process at that point in time in the schedule.  From that point you can simulate forward in time and watch the schedule unfold as it will in the real the system.  The benefits of the Simio Digital Twin begin with its accurate and fast generation of an actionable schedule.  But the benefits culminate in the Digital Twins ability to communicate its structure, its model logic, and its resulting schedules to anyone that needs to know.

Risk Analysis

One of the key shortcomings of scheduling tools is their inability to deal with unplanned events and variation.   In contrast, the Simio Digital Twin can accurately model these unplanned events and variations to not only provide a detailed schedule, but also analyze the risk associated with the schedule.

When generating a schedule, the random events/variations are automatically disabled to generate a deterministic schedule.  Like other deterministic schedules it is optimistic in terms of on time completions.  However, once this schedule is generated, the same model is executed multiple times with the events/variation enabled, to generate a random sampling of multiple schedules based on the uncertainty in the system.   The set of randomly generated schedules is then used to derive risk measures – such as the likelihood that each order will ship on time.  These risk measures are directly displayed on the Gantt Gannt chart and in related reports.   This let’s the scheduler know in advance which orders are risky and take action to make sure important orders have a high likelihood of shipping on time.

Constraint Analysis

It’s not uncommon that the supply chain planning process which is based on a rough-cut capacity model of the factory sends more work to a production facility than can be easily produced given the true capacity and operational constraints of the facility.   When this occurs, the resulting detailed schedule will have one or more late jobs and/or jobs with high risk of being late.   The question then arises as to what actions can be taken by the scheduler to ensure that the important jobs all delivered on schedule.

Although other scheduling approaches generate a schedule, the Simio Digital Twin goes one step further by also providing a constraint analysis detailing all the non-value added (NVA) time that is spent by each job in the system.  This includes time waiting for a machine, an operator, material, a material handling device, or any other constraint that is impeding the production of the item.   Hence if the schedule shows that an item is going to be late, the constraint analysis shows what actions might be taken to reduce the NVA time and ship the product on time.  For example, if the item spends a significant time waiting for a setup operation, scheduling overtime for that operator may be warranted. 

Multi-Industry

Although scheduling within the four walls of a discrete production facility is an important application area, there are many scheduling applications beyond discrete manufacturing.   Many manufacturing applications involve fluid flows with storage/mixing tanks, batch processing, as well as discrete part production.  In contrast to other scheduling tools that are limited in scope to discrete manufacturing, the Simio Digital Twin has been applied across many different application areas including mixed-mode manufacturing, and areas outside of manufacturing such as logistics and healthcare.  These applications are made possible by the flexible modeling framework of Simio RPS.

Flexible Integration

A process Digital Twin is a detailed simulation model that is directly connected to real time system data. Traditional simulation modeling tools have limited ability to connect to real time data from ERP, MES, and other data sources.  In contrast, Simio RPS is designed from the ground up with data integration as a primary requirement.

Simio RPS supports a Digital Twin implementation by providing a flexible relational in-memory data set that can directly map to both model components and to external data sources.  This approach allows for direct integration with a wide range of data sources while enabling fast execution of the Simio RPS model.    

Data Generated Models

In global applications there are typically multiple production facilities located around the world that produce the same products.  Although each facility has its own unique layout there is typically significant overlap in terms of resources (equipment, workers, etc.) and processes.   In this case Simio RPS provides special features to allow the Digital Twin for each facility to be automatically generated from data tables that map to modeling components that describe the resources and processes.   This greatly simplifies the development of multiple Digital Twins across the enterprise and also supports the reconfiguring of each Digital Twin via data table edits to accommodate ongoing changes in resources and/or processes.

Integrating Digital Transformation to Enhance Overall Equipment and Facility Efficiency

The digital transformation of traditional business process and the assets that run them have become one of the raves of the moment. A Forbes-backed research highlights just how popular the topic of digital transformation and the tools needed to accomplish it has become. Statistics like the fact that 55% of business intended to adopt digitization strategies in 2018 which grew to 91% in 2019 highlights just how popular this transformation has become.

The reason for its increased adoption rate is the ease it brings to managing business operations, facilitating growth, and a healthy return on investments made on digital transformation. The numbers from the 2019 digital business survey prove these benefits outlined earlier to be true. 35% of organizations have experienced revenue growth while 67% believe it has helped them deliver better services to customers. But despite its popularity, the adoption of digital transformation brings up a multitude of question many enterprises still struggle to answer. This post will answer some of the more important questions with special emphasis on facility management and efficiency.

What is Digital Transformation?

Digital transformation refers to the integration of digital technologies into business operations to change how an enterprise operates and delivers value to its customers or clients. Digital technologies generally refer to devices and tools that enable access to the internet thus its use allows organizations to bring operational processes to cyberspace.

The above definition is a simpler version of what digital transformation is about but because digital transformation looks different for every company and industrial niche, other definitions exist. In terms of enhancing equipment and facility efficiency levels, the definition by the Agile Elephant better encapsulates its meaning. Here, digital transformation is defined as digital practices that ‘involve a change in leadership thinking, the encouragement of innovation and new business models, incorporating digitization of assets, and increased use of technology to improve an organizations entire operations.’

In facility management, assets refer to the equipment, tools, and operation stations within the facility while new business models and innovation refer to the integration of digital technology concepts. These concepts can be the digital twin, discrete event simulation or predictive analysis.

What is Overall Equipment and Facility Efficiency?

Productivity within manufacturing facilities and warehouses are generally measured using the overall equipment effectiveness (OEE) concept. This concept measures the maximum output machines can achieve and compares subsequent output to the optimized value. In cases where the machine or equipment falls short, the OEE falls from 100% and the production cycle may be termed unproductive.

The OEE is calculated using three separate components within facilities and these are:

  • Availability – This focuses on the percentage of scheduled time an operation is available to function.
  • Performance – This refers to the speed at which work centers compared to the actual speed it was designed to achieve
  • Quality – This refers to the number of goods produced and the quality levels compared to optimal production qualities.

Although the OEE process is quite popular and has proved to be efficient, a critical analysis shows that it does not take into consideration some important metrics. OEE calculations do not include the state of the shop floor, material handling processes, and connections to both upstream and downstream performances. This is why its effectiveness as a measuring tool has been lampooned by a plethora of manufacturers with skin in the game.

Criticism of OEE as a performance measurement tool include its lack of ability to breakdown or access granular information in facilities and its lack of multi-dimensionality. The fact that it struggles with identifying real areas that require improvement within facilities is also a deterrent to its efficiency in analyzing factory performances. And this is where digital transformation comes into play.

Digital Transformation and its Ability to Enhance Facility Efficiency

The ability to digitize assets within manufacturing shop floors have created an environment where granular data can be collected from the deepest parts of today’s facilities. With the data collected due to digital transformation, a clearer picture of how a facility function can be gotten. But the digitization of traditional manufacturing processes and operations have also been a source of debate for diverse professionals due to certain difficulties. These difficulties include assessing data from legacy or dumb assets, managing communications across diverse supply chains, and bringing captured data together to make sense of complex facility operations.

To manage these challenges, diverse emerging technologies have been built around each of them. In terms of capturing data from legacy assets, the use of smart edge technologies that can be attached to assets is currently eliminating this challenge. While standards and communication protocols such as those from the OPC foundation is solving the issue of communication across both smart and dumb assets. Finally, to make sense from the captured data in order to enhance shop floor activities, digital twin technology provides a streamlined approach to monitoring and managing facilities using captured data.

With these emerging technologies, detailed insight at the granular level can be assessed about a particular facility. More importantly, these technologies attached to digital transformation can be used to enhance operational processes by delivering real-time scheduling, analyzing complex processes, and simulating applicable solutions to manufacturing shortcomings.

Discrete Event Simulation and Enhancing Facility Efficiency

Discrete event simulation (DES) tools such as Simio are some of the emerging technologies that play important roles in transforming traditional factory or facility processes. The introduction of DES can help with mapping out previous event schedules to create optimized scheduling templates that can speed up production processes.

DES tools or software can analyze both minor processes that are subsets of a large one, as well as, the entire complex system to produce schedules that optimize these processes. An example of this was the integration of Simio by Diamond-Head Associates, a steel tubing manufacturing company. The challenges the steel tubing manufacturer faced involved meeting production schedules due to a very complex production process with hundreds of production variables.

With the aid of Simio simulation software and the digital transformation it brings, Diamond-Head associates were able to utilize the large data sets produced by the varying production processes. With this simulation model, optimized schedules built for its manufacturing processes were created and this helped with making real-time businesses decisions. The steel tubing manufacturer successfully reduced the time it took to make a decision from an hour and a half to approximately 10 minutes.

This case study highlights how digital transformation can be used to enhance facility efficiency in diverse ways. These ways include optimizing scheduling procedures and drastically reducing the time needed to come up with accurate solutions to complex manufacturing-related scheduling processes.

Enhancing Facility Productivity with the Digital Twin

Another aspect of digital transformation is the use of digital twin technologies to develop digital representations of physical objects and processes. It is important to note that the digital twin does more than a 3D scanner which simply recreates physical objects into digital models. With the digital twin, complex systems can be represented in digital form including the capture of data produced by assets within the system.

The digital twin ecosystem can also be used to conduct simulations that drive machine and facility performance, real-time scheduling, and predictive analytical processes. Thus highlighting how digital transformation provides a basis for receiving business insights that change the leadership of an organization thinks and make decisions.

An example that highlights the application of digital twin technology to enhance productivity or facility efficiency is that of CKE Holdings Inc. CKE Holdings is the parent company of restaurants such as Hardee’s and Carl’s Jr. Earlier this year, the enterprise was interested in providing efficient shop floors or restaurant spaces for its employees to increase productivity levels, train new employees, and deliver better services to its customers. To achieve its aims, the organization turned to the digital twin and augmented reality to aid its business processes.

Once again, it is worth noting that both the digital twin and virtual reality tools are digital transformation tools. And with these tools, CKE Holdings Inc. succeeded in developing optimized restaurants with shop floor plans that played to the strength of its employees. The digital twin was also used to test and implement new products at a much faster rate than the traditional processes previously employed by the enterprise.

The end result was a user-friendly kitchen layout that delivered innovation in how CE Holdings restaurants function. The use of augmented reality also added another dimension to the training of new employees. The use of technology ensured new employees learnt through live practical involvement without any of the consequences attached to failure. This also reduced the hours experienced workers spent getting new employees up to speed within the restaurants. Thus highlighting another aspect in which digital transformation can be applied to drive facility efficiency levels.

The Benefits of Digital Transformation to Manufacturing and Production-Based Facilities

The examples outlined already spell out the benefits of digital transformation and its role in enhancing overall equipment and facility effectiveness levels. But, it is only right to compare and highlight what digital transformation brings to the table against the traditional OEE calculations still used within many shop floors.

  • A Complete Picture – Unlike OEE calculations which rely solely on manufacturing data produced from equipment and tools, digital transformation technologies can capture every aspect of the production process. This includes capturing data from the diverse algorithms, scheduling details, assets, sub-systems, and events that occur within the shop floor. This makes the level of details provided by digital twin environments superior to analyzing and enhancing facility productivity.
  • Improved Customer Strategy – Digital transformation enables the capture of data highlighting customer satisfaction with end products. This information can also be integrated into the manufacturing circle to ensure customers get nothing but the best service. This means with digital transformation the feedback of customers and employees can be used to enhance production facility processes.
  • Improved Employee Retention Strategy – The manufacturing industry is notorious for its high employee turnover rate due to diverse factors that make it unattractive to the new generation of workers. The integration of digital transformation can enhance workplace layout, as well as, bring a more modern and captivating process to manufacturing. These enhancements can reduce the turnover rate and get the younger generation interested in manufacturing.
  • Enabling Innovation – The increased adoption rate of industry 4.0 business concepts and models in manufacturing means businesses must adapt if they intend to retain their competitive edges. Digital transformation offers a pathway to innovating legacy business process and increasing an enterprise’s ability to stay competitive in a changing manufacturing industry.

The Next Steps

The advantages digital transformation brings to enhancing facility efficiency comes with a butterfly effect that affects leadership, innovation, and problem-solving activities. Although the integration process involves technical knowledge of applying digital twin technologies and simulation software, these skills can be acquired with a little effort.

Simio Fundamentals Course offer businesses and other organizations with the opportunity to train staffs about digital transformation and its specific techniques. You can also choose to register employees to participate in the upcoming Simio Sync Digital Transformation 2020 Conference to learn more about digitally transforming your business processes and how to reap the rewards.

Top Trends in Simulation and Digital Twins Technology for 2020

Digital Twins refers to the digital representations of people, processes, and things. It is used to analyze operations and receive insight into complex processes. As 2019 comes to an end, the need to define digital twin technology still exists and hopefully by this time next year, its growth and popularity will make this need obsolete…

In 2018, digital twins were included as a top technology trend by the big names covering the tech industry. According to Orbis research, the digital twin market is expected to grow by 35% within a 5-year time frame and 2020 is right in the middle of this period. But before highlighting the trends to expect in 2020, it is only right to do a recap of the year so far. This is to note if earlier predictions have come to pass before mapping the future.

In terms of popularity, coverage of the digital twins is definitely on the right track as continuous studies by Gartner and other publications show. Today, many professionals across the technical and non-technical divide understand the digital twin concept and how it can be used to drive business processes and concepts. This is why many industries are currently integrating digital twins to bolster business insight and understand data.

The biggest adopters of digital twin technology in the geographical sense remains North America. Enterprises within the US and Canada currently leads the way in terms of adopting digital twin technology. North America accounts for approximately 59% of the digital twin market and economy while Europe and the Asian pacific comes next.

The very nature of the digital twin and simulation, as well as, the solutions they provide makes them attractive business tools for the manufacturing industry and this fact is backed up by data. The manufacturing industry’s affinity to digital twins is powered by Industry 4.0 and the varied ongoing processes that occur within shop floors. The use of smart edge devices, equipment, robots, AI, and automation also fits nicely into the digital twin concept thus making it attractive to manufacturers.

In 2019, manufacturers account for approximately 36% of the digital twin market. Other industries such as the energy and power industry, Aerospace, Automobile and Oil & Gas complete the top five industries who make use of the digital twin to enhance operations. Analyzing this trend highlights the fact that digital twins are important to simplifying complex processes where hundreds or thousands of variables and relationships are needed to successfully accomplish set tasks.

Is the Digital Twin for Only Production-based Industries?

Although the Oil & Gas industry, as well as, the energy and power sector are not tagged as manufacturing industries, a case can be made for it. Therefore, many may assume or wonder if digital twin technology is only useful within production-based industries where discrete or process manufacturing takes place. And the answer is No.

The digital twin is also being used in other industry verticals such as the hospitality industry and in restaurants. One example is the use of Simio by CKE Holdings Inc. to ease workloads in its Carl’s Jr and Hardee’s restaurants. The Digital twin is also being used to support discrete event simulations in hotels, real-estate, and tolling facilities.

The use of interconnected devices and automation within service and hospitality businesses are the driving forces behind the adoption of digital twin within a variety of industries. And the coming year is expected to witness continuous growth as more industries and professionals understand what the digital twin brings to the table.

Top 5 Trends for the Digital Twins and Simulation Technology for 2020

Interrelated Technologies will Boost Adoption Rate – The growth and maturity of interrelated technologies such as 3D printing, metal printing, and mapping will play a part in accelerating the adoption rate of digital twins in 2020. This is because of the need to monitor and consistently improve these technologies and the systems that drive them.

Using 3D printing as an example, many manufacturing outfits are currently making use of 3D printing clusters to speed up their production requirements. 3D printing clusters or farms refers to facilities where hundreds of 3D printers function simultaneously to manufacture physical items. Although these 3D printing clusters have dedicated software for managing the printing process, material delivery, scheduling, and managing the entire supply chain within these facilities are handled manually.

Digital twin solutions can eliminate the manual management and handling process in 3D printing farms to great effect. If properly executed, a digital twin of a large scale 3D printing cluster will provide a data-driven approach to optimize supply, scheduling and the manufacturing process. This will reduce expenditure including the energy expended in 3D printing cluster facilities.

Industry 4.0 will Continue to Drive Adoption – The growth in Industry 4.0 and the devices, as well as, communication channels driving the smart factory is expected to increase the adoption of digital twin solutions. In 2019, Industry 4.0 witnessed the creation of new standards from the OPC Foundation that supports the collection of data from the deepest corners of brownfield facilities. These data were collected from dumb equipment with legacy technologies using smart edge and embedded devices.

The success of this approach, means that digital twin technology can now integrate the data collected from dumb or legacy equipment when developing digital representations. This increases the accuracy levels of the representations thereby enhancing simulation results and scheduling plans. Thus, increasingly accurate digital twin ecosystems and results will create more use cases that will drive the adoption of digital twins in 2020.

IoT and IIoT to Drive Digital Twin Adoption Rate – The move to more interconnected environments across both manufacturing and service-based industries also have roles to play in 2020. As stated earlier, Industry 4.0 will enhance the adoption of digital twin technologies and this also true for the industrial internet of things (IIoT). The widespread adoption of IoT and IIoT devices or equipment have created a race to develop the best management solution to monitor interconnected activities.

This creates an avenue which digital twin service providers are currently taking advantage of and will continue to do so in 2020. The ability of the digital twin to create digital representations of IIoT devices and also integrate the data they produce creates multiple use cases enterprises will explore in the coming years. These use cases include running simulations in complex interconnected facilities to produce accurate results or to access processes that involve the use of IIoT technologies.

Digital Twin for Cybersecurity Challenges – With every passing decade, the cybersecurity challenges enterprises face keeps changing. The millennium brought Trojan horses and other viruses which were effectively stopped with anti-virus software apps and by 2010, attackers pivoted to using phishing attacks and malware. Today, ransomware, spyware, DDoS, and business email compromise attacks have become the new challenges enterprises face. Thus highlighting the ever-changing landscape of cyber threats.

To cater to these threats and attacks, digital twin solutions will be enlisted by enterprises in 2020. In this scenario, the digital twin will be used as a penetration testing tool to simulate the effects of successful data breaches or ransomware to an organizations business processes. Within the digital twin environment, attacks to core equipment can be simulated and the result will be a response pattern that ensures the crippled equipment does not lead to extended downtime.

2020 will also be expected to witness an increase in the cybersecurity threats facing cloud-based digital twin solutions. Thus, more secure communication protocols and standards regulating data use will be developed to protect enterprises making use of digital twin technology. This means developers and service providers will have an increased role to play in securing digital twin environments.

Simulation-based Scheduling – The drive to deliver real-time scheduling is expected to continue in 2020 as enterprises seek more accuracy with managing business process. The need for real-time scheduling is also driven by how enterprises intend to apply simulation and digital twin tools. An example includes the need to make business decisions in real-time, handle unforeseen occurrences such as machine downtime, and reschedule operations.

These challenges fall into the category of issues discrete event simulation (DES) software can handle. Once the required data is accessible, DES and digital twin applications can conduct simulations in real-time and provide accurate solutions to dealing with changing scenarios also in real-time. This will drastically reduce downtime and enhance performance within facilities and warehouses.

Although some DES software offers real-time simulation scheduling, many are still process-based scheduling applications and this is set to change in 2020.

Quantum Computing – If real-time simulation, scheduling, and process management is to be achieved, then digital twin solutions must take advantage of the speed, scalability, and high-performance quantum computing offers. Today, digital twin solutions currently leverage the cloud to provide stable and scalable services to enterprises and only a few integrate the use of high-performing computers to enhance or manage really large workloads.

In 2020, further strides will be made to speed up simulations within digital twin environments using high-performing computers. The success of this initiative will speed up real-time scheduling and complex process management for the foreseeable future.

Planning for 2020…

The benefits of the digital twin have played an important role in ensuring its adoption across diverse industries and the expected trends of 2020 will continue the increased adoption rate that came with 2019. Although digital twin solutions have become more interactive and intuitive to use, enterprises still require the assistance of experienced professionals to get the best out of their digital twin environment and this is where Simio can help.

IT managers, cybersecurity experts, and project managers can take advantage of the Simio Fundamentals Course to learn more about simulation and Digital Twin technology including its application in real-life scenarios.

Simio Showcases the Digital Twin at INFORMS Annual Meeting 2019 – A Recap

The INFORMS Annual Meeting for 2019 has come and gone with multiple keynotes shared, workshop activities, and hundreds of excellent companies sharing their experiences and solutions from brightly colored booths. Once again, Simio was at the thick of things evangelizing the benefits of simulation and Digital Twin technology to the world. As with all annual meetings, the focus was on the strides been made in operational research and analytics. And the meeting provided the chance to explore emerging technologies and its applications across all ‘works of life’.

The term ‘all works of life’ wasn’t used lightly as sessions covering social media analytics and e-learning to applying analytics to human trafficking were explored. As for Simio, our role was somewhere in the middle and as stated earlier, our participation was centered around the digital twin. But before going into details of how the event panned out and Simio’s roles, here is an outline of what the INFORMS Annual Meeting is about for interested individuals.

INFORMS Annual Meeting

INFORMS which stands for the Institute for Operations Research and the Management Sciences is an umbrella organization for professionals plying their trade in operations research and analytics. INFORMS currently boasts of approximately 12,500 members across the globe which highlights its global or international reach.

The organization also sets standards and guidelines to ensure research and analytics within its field are ethically done. To bring its thousands of members together under one roof, the INFORMS Annual Meeting event was created and it holds once a year. The event features keynote sessions, workshops, publication presentations, and an exhibition area for member and enterprises to showcase their wares.

The INFORMS Annual Meeting also coincides with its community service drive to assist non-profit organizations with meeting their obligations. This is done through the INFORMS Pro Bono Analytics section of the organization. If you are wondering why the information about Pro Bono Analytics is included here, then I ask for patience as it will all make sense in the end.

Now to the annual meeting of 2019!

The 2019 INFORMS Annual Meeting was a 3-day event which ran from the 20th of October to the 23rd. This year’s event was definitely a success as more than 5,000 people breezed through the different sessions, exhibition areas, workshops, and lunch areas throughout the 3-day event. The convention center buzzed with activities through these days and we are proud to say Simio capitalized on these activities in different ways. Our participation included a dedicated Simio booth highlighting the use of Simio digital twin technology, a session handled by Jason, and workshop presentations from Renee.

Keynote Sessions and Workshops of Note at the INFORMS Annual Meeting

Tens of session covering operations research and analytics were covered throughout the event which makes mentioning and discussing every one of them impossible. So, the focus will be on simulation, digital transformation, cloud computing, and digital twin sessions.

One of the exciting sessions within the above category was the session about the computational infrastructure for operations research, COIN-OR, initiative. The IBM project focuses on providing open-source technologies solely for computational operations research. The end goal is to provide an open-source library of tools which will ensure researches do not have to start from scratch when handling complex research. This creates a foundation that will be built on and maintained by researchers over the years.

The session ‘Robust Optimization and Learning Under Uncertainty’ was also interesting as it discussed the challenges stakeholders face with decision-making and policy creation. Han Yu, a PhD student at the University of California spoke about how important data collection, and an understanding of history should drive real-world decision-making. The session also discussed how modeling and robust optimization techniques can enhance the decision-making process.

Other notable sessions highlighted or raised questions about the role digital twin and simulation could play in enhancing agriculture and the healthcare industry. According to Greg from Syngenta, AI, computer vision, and bioINFORMSatics modelling currently assist Syngenta with making data-driven seed selections and breeding. This raises the question of the role of the digital twin in agriculture which may be explored in other blog posts.

In the ‘Healthcare Modeling and Optimization’ session, Dr. Zlatana Nenova spoke about the role modeling and data analytics play in improving healthcare. Her speech also touched on the use of digital technology to analyze medical care policies for both off and on-site healthcare delivery. In terms of on-site healthcare, there are certainly diverse ways the healthcare industry can benefit from digital twin technology. Although this was not covered in this year’s event, it highlights the possibilities of applying digital twin to the healthcare industry.

Simio’s Events at the INFORMS Annual Meeting

Now, to Simio and our role at the INFORMS conference. In last year’s event, Ms. Renee Thiesing, the VP of Strategic Alliances,  spoke excellently on the role Simio plays in driven discrete event simulation and the digital transformation of brownfield systems as the move to Industry 4.0 continues. She also highlighted the importance of real-time event scheduling and how Simio can help enterprises solve real-time scheduling challenges using Simio.

In this year’s event, Ms. Renee built on her earlier foundation by focusing on the digital twin capabilities of Simio and its application in diverse industries. Her session titled ‘New Innovations: Cloud Computing, Real-Time Scheduling, Industry 4.0 and more’ discussed how Simio leverages cloud computing to deliver high-performing scheduling and simulations.

Through the session, she discussed how Simio leverages the computing power of Microsoft Azure to support complex applications. Simio’s compatibility with Schneider Electric’s Wonderware was also discussed in detail. This includes the leveraging of Wonderware to achieve detailed production scheduling in real-time, as well as, manage real-time risk analysis. The new Simio features such as Simio’s cloud portal and OptQuest were also covered during her workshop. She highlighted OptQuest abilities to optimize scheduling and simulation with the aim of delivering optimal solutions to complex business problems.

Jason Ceresoli also spoke on the benefits of using Simio’s 3D modelling capabilities to solve real—world problems. His presentation covered Simio’s features for system design and operation. Practical examples of how Simio’s rapid 3D modeling, planning and scheduling, and optimization capabilities can be used by enterprises were also discussed by Jason. Finally, his session highlighted the difference between Simio and other simulation tools with a focus on how professional researchers and analysts can use these features. 

Our participation at the INFORMS Annual Meeting will not be complete without recounting our experiences at booth 28 in the exhibition area. The targeted message used in the Simio booth drew its own audience of professionals, entrepreneurs and business representatives interested in the digital twin. This gave us the opportunity to showcase Simio’s features and real-world applications to interested individuals. We can categorically say our booth played a role in the sales leads and opportunities we got from the event.

Simio and the Pro Bono Analytics Event

I remember introducing you to the INFORMS Pro Bono Analytics and now here we are! This year, Pro Bono Analytics partnered with a Seattle-based non-profit organization, FareStart, to assist individuals interested in building careers in food service and culinary arts. At this year’s INFORMS Annual Meeting, Simio alongside other participants made donations to the FareStart initiative.

The event was a success and according to Elise Tinseth, Community Engagement Manager with FareStart, shared, “The INFORMS Pro Bono micro-volunteer opportunity of creating hygiene kits is impactful to eliminating barriers our students who are experience poverty and homelessness have to getting jobs in the food service industry.’ She also thanked everyone who made out time and donated resources to help FareStart meet its goals.

INFORMS Annual Meeting Awards and the Future

And lastly, the INFORMS Annual Meeting Awards. Although Simio did not bag any of the awards, the pomp and pageantry, as well as, the strides made by researchers are worthy of a mention in this post. Hopefully, the prize for teaching operations research and management science may be ours. That being said, the 2019 event was a success and Simio will continue to be a part of the INFORMS Annual meeting for the foreseeable future.

Integrating Simulation and Digital Twin Technology in the Hospitality Industry

The numbers are in and they do look good for the hospitality industry which consists of hotels, restaurants, and other hospitality-related services. According to Forbes, profitability in the hospitality industry is finally on the increase after the slump of previous years. The report further stated that the net profit margins for full-service restaurants grew by approximately 6% which is 3.8% more than the previous year. The National Restaurant Association expects this growth to continue but early wins must also be consolidated if this is to be achieved. And this is where Digital Twin Technology comes into play.

With the expected growth figures also comes challenges and in the hospitality industry, these challenges generally include fending off the competition and enhancing operations to reap increased rewards. In terms of competition in the hospitality industry, the following statistics paint a clearer picture. In 2018, approximately 60,000 new restaurants and lounges were opened in the United States while 50,000 either filed in for chapter 11 or were closed down for other reasons. Although at the end of the year, the industry grew with the addition of 10,000 restaurants, this mass closure still highlights the competitive nature of the industry.

The competitiveness in the hospitality industry is turning many small and large scale stakeholders to turn to emerging technologies to ease operational deficiencies. This is why today, the hospitality industry has become one of the major drivers of innovation in robotics, artificial intelligence, digital visualization, and the internet of things (IoT). The aim is to collect data from every aspect of a hotel or restaurants operational chain and use that data to receive the business insights needed to stay ahead of the competition.

Today, most hotels make use of interconnected devices to simply customer requests and analyze their peculiarities in order to deliver bespoke services. Examples of this include the use of concierge robots by the Hilton group and the design of smart hotels by Marriot and other stakeholders.

And to what benefits?

Integrating digital technology in the hospitality industry has led to a 40% increase in revenue for online travel agencies (OTAs) who streamline and personalize their services for customers. In brick and mortar hospitality facilities like the Marriot hotels, its financial report of 2018, highlighted a 38 percent increase in revenue with emerging technologies playing a starring role in simplifying operations. This led Arne Sorenson, CEO of Marriot, to state that ‘digital transformation is not only speeding up every aspect of our business, but it is also broadening operations’. And this transformation, as well as, the benefits they bring can be broadened much further with the integration of Digital Twin technology.

What is A Digital Twin?

A digital twin refers to virtual representations of physical products, systems, facilities and the processes that occur in them. The technology can be used to create digital replicas of actual physical assets and processes and also integrate potential assets onto the created virtual environment. This means every asset that functions in a shop floor including devices or equipment and all business operation or process can be recreated in a digital environment.

Digital twin environments also create an enabling environment for testing new business policies, operations, and assets to access their performance levels before any physical implementation is undertaken. When put beside the recent adoption of smart technology in the hospitality industry, it is easy to see why digital twin technology is the solution every stakeholder has been waiting for to broaden business operations.

One of the major features of the digital twin is its ability to virtualize every asset and process that occurs in an environment. In the hospitality industry, these assets may include; the smart devices used in rooms, check-in and check-out points, robots, the equipment used for logistics and supply chain management, inventories, and every process that produces data. This means when correctly deployed, a digital twin can recreate assets and processes from the deepest parts of a hospitality system in a digital ecosystem.

The Digital Twin and Enhancing the Hospitality Industry

The easiest way to understand how digital twin technologies can be leveraged to gain an edge over the stiff competition in the hospitality industry is through case studies and CKE Restaurants Holdings, Inc. provides an example.

CKE Restaurants Holdings, Inc. digital twin Story is one that showcases how harnessing digital twin technology and virtual reality can be used to test and implement new operational policies within the hospitality industry. In its case, CKE recreated hundreds of assets and kitchen configurations using the digital twin with the aim of deciding the best configuration that will increase productivity in its Carl’s Jr and Hardee’s restaurants. With the aid of Simio’s digital twin solutions, restaurant floors and kitchens were digitized which provided the perfect environment for reorganizing shop floor assets to reduce employee traffic and create an enabling environment for customers.

To achieve the level of detail needed to accomplish this task, CKE had mapped out every production aspect that occurs within a restaurant down to the plate cleaning process. With this data, accurate simulations could be executed which yielded highly-accurate results. Thus, integrating new equipment and testing how they function with other variables and assets within the restaurant was made possible. This meant receiving accurate business insight into new policies and the effects of introducing new assets before effecting a physical implementation.

According to Forbes, the integration of Simio’s digital twin helped CKE Restaurants, Holding, Inc. manage hundreds of simulations that consisted of the introduction of diverse assets and processes into the digital model. This allowed the restaurant to predict the effects of introducing approximately ten new equipment to the shop floor, as well as, test the efficiency levels of five layouts for the kitchen. The use of a digital twin also helped analyze new designs that would assist CKE with easing the workload on employees which would lead to higher employee retention in an industry notorious for low retention rates.

The example of CKE Holdings, Inc. still leaves the question of if the digital twin can enhance operations in larger more complex facilities. The short answer to this is, definitely yes!

Digital twin technologies have been made use of in large industrial settings such as Nestlé’s and Boeing facilities to implement new ideas and enhance production. Although these examples highlight the importance of digital twin technology, the focus is on the hospitality industry which leads to the longer answer.

In the large hotels with 300 rooms and above, more operational processes occur that dwarf the example highlighted in the CKE case study. These processes include; logistics and supply chain management, tracking the orchestration of hundreds of customers, power consumption, and correctly assigning workplace assets to meet demand. Other smaller systems within a large hotel’s immediate environment are the valet and parking system, concierge system, and manual workflows.

As stated earlier, digital twin solutions are capable of recreating diverse assets, processes and system in a virtual environment when correctly applied. This actually makes the digital twin a solution custom-built for large hotels where the need to keep track of multiple processes within a system while implementing new ideas is a regular occurrence. With the aid of the digital twin, every data produced in large facilities can be collected and analyzed against the different assets within the system. This gives the system integrator or manager a contextual insight into every aspect of running a hotel facility in real-time.

Furthermore, the digital twin of large hotel facilities can be used to run both discrete and continuous event simulations to better understand the events occurring in different systems. A discrete event simulation can be used to test how the implementation of building of an additional check-out point at the parking lot will ease driving and foot traffic before a physical implementation is considered. Also, a simulation of the power consumption that occurs within the facilities can provide insight into which assets or processes can be periodically shutdown to reduce consumption.

The benefits of Adopting Digital Twin in the Hospitality Industry

 Although the earlier case studies provide an insight into the benefits of the digital twin to the hospitality industry, more information is sometimes needed when making decisions. In this case, the decision to be made is choosing to enhance operations using digital twin solutions.

One of the important benefits of integrating a digital twin is the clarity of purpose it provides to facility managers and hotel owners. The use of a digital twin means decisions no longer has to be made in the dark. An accurate digital twin built with every asset, process, and data coming from a hotel or restaurant is the perfect environment for testing out anything before implementation. The test can be as extensive as analyzing the effects of a new equipment transportation system or how automating a business process will turn out. The test can also be as little as analyzing how changes in shelf heights will increase employee productivity.

Another important challenge hospitality businesses face involves the reduction of operational expenses without having to reduce the quality of services offered. Here again, the insight a digital twin provides can be helpful with reducing waste. An example of this is the use of the digital twin by KONE, an elevator company. KONE makes use of digital twin technology to understand how people move through buildings and the decisions they take when riding an elevator. The knowledge gotten from the use of a digital twin helped the company cut out three to four minutes from the average elevator commute. This, in turn, reduced maintenance cost and increased productivity for building owners.

KONE’s case study highlights the fact that hotel owners can make use of the digital twin and scheduling software to analyze commutes, reception traffic, kitchen and dry cleaning process with the aim of increasing workforce productivity. The model can also be used to enhance customer experience by reducing commute from the reception floor to hotel rooms. As for restaurants, this can be taken further to simplify the drive-through process and increase worker efficiency thereby eliminating waste.

The journey to a smarter hospitality industry also provides the perfect environment for enhancing productivity and providing seamless experiences for customers. Embedded devices and IoT solutions can be used to map out customer attractions and the areas that witness more customer traffic. With this information, simulations run through the digital twin can create optimized schedules for visitation periods. This will ensure that customers do not wait in long queues before being able to access areas of attraction within a facility.

Carving a Niche in the Competitive Hospitality Industry

 Staying afloat in the hospitality industry in order to reap a part of its staggering $550billion revenue requires some effort. These efforts consist of creating an efficient system that takes care of every need of the customer. With advancements in technology, the task of creating that system has become more streamlined and visible to business owners. The digital twin offers visibility and the ability to access real-time information before designing or recreating efficient systems.

CKE Restaurant Holdings, Inc.’s use of Simio’s digital twin solutions provides an excellent case study that highlights how important digital twin is to the transformation of the hospitality industry. With these solutions business owners can better access both small and large scale operational process and enhance these process to the benefit of your customers. You can learn more about the competitive edge the digital twin offers your hospitality and restaurant facilities by speaking to a Simio representative today.

Resources:

https://www.google.com/url?sa=t&source=web&rct=j&url=https://marriott.gcs-web.com/static-files/b82978a6-9d28-4e38-9855-fc4ae2cebe11&ved=2ahUKEwjH8YGxjKPlAhWNTsAKHesCC3EQFjAOegQIBxAB&usg=AOvVaw1wOGkSQxcJ8O7VZBmYm1xF

https://www.nextguestdigital.com/blog/hospitality-digital-tech/

https://www.simio.com/applications/industry-40/Digital-Twin.php

https://www-forbes-com.cdn.ampproject.org/c/s/www.forbes.com/sites/lanabandoim/2019/09/25/how-cke-restaurants-is-using-virtual-reality-to-innovate/amp/

https://www.google.com/url?sa=t&source=web&rct=j&url=https://inbuildingtech.com/uncategorized/digital-twins-proptech/&ved=2ahUKEwi6ieSMjaPlAhWMXsAKHYvxC94QFjACegQIAhAB&usg=AOvVaw2JkDmpLHU5Vs0BI4inPD4n

Analyzing the Paradigm Shift from Production Scheduling to Simulation-Based Scheduling

Through the long centuries of man’s existence, man has always produced materials and products for specific uses. But at the turn of the 17th century, something interesting happened. Man had built industrial equipment for the first time which ushered in the age of industrialization. This age came with larger facilities dedicated to every aspect of the production lifecycle as we know it today. With these large facilities came the need to manage hundreds of workers, the transportation of materials, and the stages of production for a product. And as early as the 1800s, the need for production scheduling methodologies was apparent.

This need led to the development of scientific management processes by legendary figures such as Henry Gantt. In the 1800s, charts and manual data collection techniques were introduced to manage production scheduling challenges. Although these solutions worked perfectly with the industrial equipment and facilities of that age, advancements in production technology made them redundant by early 1900s.

Moving forward to the 80s, production scheduling was being defined as the process of planning to ensure the raw materials and production capacity within a facility are optimally allocated to meet demand. With time, this definition was updated to account for complex tradeoffs between competing priorities and the hundreds of varying relationships that occur on manufacturing shop floors.

To handle these complex trade-offs and production variables, advanced planning and production scheduling systems where developed. These systems or solutions were fondly called APS solutions and they accounted for the materials available for a production cycle, available labor and production capacity. APS systems successfully handled the scheduling of complex production processes by applying a constraint-based approach to scheduling. Thus, these tools created schedules for:

  • Capital-intensive production process where constraints such as equipment and plant capacity where constraints to deal with
  • Production processes where hundreds of components needed to be assembled when building the product.
  • Production processes with changing schedules which were not predicted at the beginning of the process.

The success of production scheduling systems also led to the creation of hundreds of enterprises offering APS solutions and services to ease complex scheduling activities. Other spin-off solutions such as customer relationship management applications and enterprise resource planning solution were also developed due to the success of production scheduling systems.

As with most great technological advancements, the traditional product scheduling solutions began to meet more complex situations than it could handle due to the changing manufacturing landscape. These changes are both technological and conceptual in nature. In terms of technology, the advent of Industrial Internet of Things, smart manufacturing equipment, and automation were changes traditional scheduling software could not deal with. While the conceptual changes include the need to account for all data produced on the shop floor, make predictive analysis, manage disruptions in real-time, and cybersecurity challenges among others. These changes limited the efficiency of production scheduling software in diverse ways which will be further explored.

The Limitations of Production Scheduling Solutions

The limitations of production scheduling tools are all due to the increased complexity of today’s manufacturing and industrial facilities, as well as, the demand for more insight by enterprises. These limitations include:

Flexibility Challenges

The ever-changing processes in modern manufacturing facilities and the introduction of new equipment and process to the shop floor must be integrated into a functional scheduling system. The ability of traditional production scheduling tools to adapt to these changes is limited which means the schedule they produced will be skewed.

Challenges Integrating Real-Time Occurrences

The effects of downtime in manufacturing and industrial facilities have been highlighted in hundreds of reports. Downtime can be caused by a variety of issues but for the topic of production scheduling, a machine going down in a shop floor is the perfect scenario. Production scheduling tools will struggle to predict this event or even take it into account to reschedule events in real-time.

Although production scheduling tools can create schedules that take into consideration defective equipment, they make use of approximated data. This means the schedule they produce are static in nature and would not take into consideration real-time data such as the location of the machine, output at its workstation etc.

Requires Numerous Adjustments

This constraint is a follow up to the challenges production scheduling tools have with integrating real-time occurrences. To prevent trashing the systems integrator must create multiple custom algorithms for different scenarios. This means the product scheduling tool takes these algorithms and try to apply them to a new problem within a facility. To accomplish it multiple adjustments must be made to the initial adjustment which defeats the ability to create reschedule in real-time. According to Oracle, this challenge means the traditional product scheduling tools will struggle with finding good solutions to scheduling problems even when they exist.

With these limitations, a new process to accurately manage production scheduling tasks was needed. This led to the paradigm shift from traditional production scheduling solutions to simulation-based scheduling. Simulation-based scheduling involves the imitation of the operation of a real-world process over time using a digital model. The process involves building a simulation model of the physical process and populating the model with the detailed events and processes that occur in the real-world. The simulation model can then be run to produce an optimized production schedule.

The Impact of Simulation-Based Scheduling

It is important to note that simulation-based scheduling can be handled in two ways. These are through a discrete event simulation and a continuous simulation process. The discrete event simulation models the operation of a manufacturing or industrial facility as a discrete sequence of events that occur with time. In this model, events occur at a particular instant in time and record the change of state in the facility.

On the other hand, continuous simulation models continuously track the events and the changes they produce in the facility. Both the discrete event simulation and continuous simulation model take production scheduling to heights traditional production scheduling tools cannot reach. This paradigm shift has made real-time production scheduling more accurate and flexible enough to deal with the changes that occur in modern facilities.

As stated earlier, the introduction of production scheduling tools led to the development of other complementary technology solutions and this is also the case for simulation-based scheduling. One such concept is simulation-based Digital Twin solutions. The Digital Twin involves the mirroring of physical objects to create a virtual model through simulation-based engineering tools.

The ability to create Digital Twins of every facility and industrial process also takes simulation-based scheduling to new heights. Creating virtual mirrors of real-time systems or facilities and simulate the complex process that occurs in these facilities to create a far more accurate schedule than traditional production scheduling tools.

In the case off dealing with downtime, simulation-based digital twin environments can collect data from real-world sensors and use the data to predict asset –manufacturing equipment—behavior. This allows for the scheduling process to account for defective equipment and quickly reschedule the production process around the defective equipment. Also, simulation-based scheduling tools can manage what-if scenarios better than the alternative. Making it possible for operations teams to simulate possible challenges and create optimized schedules that take these constraints into consideration.

An example of how simulation-based scheduling alongside digital twin technology has been used to develop more efficient schedules. Is in the case of CKE Restaurants. Here, a Digital Twin of the restaurant facilities made it possible to create implementation schedules, supply and delivery schedules in its kitchen facilities. The end result was a more efficient production and service process driven by simulation-based scheduling and Digital Twin solutions.

How Simulation-Based Scheduling Transverses through Diverse Industries

Traditional production scheduling tools were designed and developed primarily for use in manufacturing settings and this still remains its key area of application. Unlike production scheduling, simulation-based scheduling can be integrated into any industrial process to produce accurate schedules.

Once again, its affinity with Digital Twin technology makes this possible. This is because, with digital twin technology, every process and asset in an industrial setting can be modeled and brought into a digital environment. The integration of simulation-based software in this digital environment can then simulate the industrial process and create schedules for them. Simulation-based scheduling can be used in the healthcare industry, pharmaceutical facilities, dockyards, ports, and in every facility where a process can be modeled and mapped.

The rise of Industry 4.0 manufacturing facilities and processes where data is king provides another avenue for simulation-based scheduling to prosper. Smart factories are being run by machines and devices with sensors, embedded systems, and system on modules solutions. This makes it possible to assess data from every asset and process in a facility.

Simulation-based scheduling software can leverage the data collected in an Industry 4.0 – compliant facilities to create real-time schedules. Computing simulations of schedules can also be achieved in real-time with increased accuracy due to the widespread availability of data in facilities that integrate Industry 4.0.

Simulation-Based Scheduling and the Road Ahead

The paradigm shifts from production scheduling solutions to simulation-based scheduling is still very much an on-going journey. This is due to emerging technologies which complement and enhance the use of simulation-based scheduling software. Examples include the rise of cloud-computing and high-performing computers (HPCs). These technologies make it possible to create models of very complex systems such as facilities or processes with thousands of variables while producing accurate scheduled for them.

The combination of these technological process will enhance real-time scheduling and rescheduling as we know it. As simulation-based schedule software leverage on the cloud and HPCs, complex simulations can be done in micro-seconds thereby delivering accurate real-time results that enhance productivity in industries. Thus completing the paradigm shift from manual and constraint-based scheduling to a responsive real-time scheduling era.

How to Sell the Idea of Digital Twin to Your Manager

The business world as we know it is changing. Never have there been so many emerging technologies, models, and business concepts competing for the attention of the business community. Today, we have cloud computing services, the Internet of Things, Artificial Intelligence, Robotics, Automation, Blockchains, and the Digital Twin providing timely business insights for enterprises. This is why the internet and even physical business entities have hundreds of salesmen and women trooping in and out of your private space. Selling the ‘next best thing’ in technology like pharmaceuticals marketers do, to CTOs, CIOs, and other decision-makers.

 In this whirlwind of changing activities and millions of ads advertising the best technology solutions is Digital Twin technology. For those who know the benefits of the Digital Twin and its ability to enhance every aspect of an enterprise’s operation, the challenge of convincing management to take a chance with it remains. This leaves one with the question of what are the best techniques to sell the idea of integrating Digital Twin technology to the boss? As with most sales challenges, the traditional answer generally involves listing its value-added propositions and outlining the returns to be made investing in the technology.

Although the traditional answer to selling new ideas to management remains efficient, the increased competition among cutting edge tech services means more selling points are needed. Thus, to effectively answer the question ‘how do I sell the idea of Digital Twin technology to management’, here are some new and timely tips to consider.

5 Tips for Selling the Idea of Integrating Digital Twin Technology to Decision-makers

As a sales representative, business development, or system integrator staff/employee who is part of a team, the successful introduction of new technology solutions depends on your approach. This is because you will serve as the driving force behind ensuring the implementation of Digital Twin technology improves the company’s operational processes to deliver optimal services to customers. The tips for selling the idea of Digital Twin Technology include:

Making Your Case with Data – The task of convincing those who control the money and decide what investments are to be made is not for the faint-hearted. You must come prepared and one of the ways to prepare for every question that may come your way is having the required data in place that answers the most important questions. According to a Mckinsey report, integrating data analytics in the right place or in your sales pitch is one way to convince skeptics on the importance of Digital Twin technology. The data to be included must be relevant to the situation or scenario you intend to create when selling Digital Twin Technology. To simplify your search for adequate information, here are some of the data you will need:

  • To answer the question of the adoption rate and how the competition intends to use Digital Twin technology to enhance business operations, the IDC data on adoption can help. The IDC forecasts that 40% of IoT platform vendors and 70% of manufacturers will be making use of Digital Twin technology by 2022.
  • If the question of how digital twin technology can help increase the revenue of the business, data from the Juniper Research can help you answer the question. According to the research, the use of Digital Twin technology has helped enterprises increase their revenue by 25 to 35%. This is due to the ease in which business insights can be gotten from complex processes and the predictive analytical features of Digital Twin technologies.

Armed with this information, your sale pitch will highlight the importance of staying ahead of the competition by integrating Digital Twin technology to simplify complex processes and difficult business decisions. It can also be applied to drive development and predict future scenarios in a wide variety of industries including manufacturing, architecture, construction, technology, engineering, and healthcare industries.

Make Use of Case Study – With your data in place, the next step to convincing decision-makers in your organization is through the provision of confirmable case studies on how Digital Twin technology can help. This is where a little personal effort comes into play if interested in creating personalized case studies for stakeholders to scrutinize. You can find applicable case studies that highlight how Digital Twin technology has been applied and is still been applied by your competitors here:

  • You can find case studies on the application of Digital Twin technology in the aviation industry, automotive industry, manufacturing, healthcare, mining, and engineering at Simio’s resource center. The case studies here are practical examples that can be integrated into your presentation when making your sales pitch.
  • If you are certain a pitch with case studies may not be enough, then more effort is needed from your end. This effort involves the design of a Digital Twin of a phase of your facilities operations to showcase the benefits of a digital model of physical systems where events can be simulated. Many Digital Twin technology providers offer free trials which can be used to accomplish this task. You can make a request for a Simio Demo to quickly kick start the process of designing a Digital Twin.
  • Provide specific answers to your enterprise or the enterprise’s pain points. Once again, although case studies may be customized to show how Digital Twin technology alleviate business challenges, creating a functional model will do more to pass the message across.

So, the second tip here is making use of case studies to address exactly how Digital Twin technology can be used to eliminate specific challenges an enterprise experiences. The efficient use of case studies is one of the quickest ways to get the ball rolling when trying to sell Digital Twin solutions to the decision-makers in your organization.  

Showcase the ROI – It is a well-known fact that one of the most publicized benefits of Digital Twin technology is the returns it offers enterprises who choose to invest in it. Also, your manager, as well as, stakeholders would definitely expect a breakdown of how much the investment will cost and the returns to expect. It is important to have this in mind because, finances are generally the deciding factor that determines if a positive decision will be made.

According to research by High Tech Software Cluster, the threshold for integrating Digital Twin technology for enterprises costs approximately €50, 000 ($55,000). The study goes on to show that to create digital twin solutions for more complex systems may cost approximately €150, 000 or $165,000. As stated earlier, the returns on this investment can be as much as 35% of the total cost needed to create a Digital Twin. In some cases, returns of approximately 50% have also been reported which highlights the financial leverage Digital Twin technology offers enterprises.

As you probably know, approximations are not enough to sway managers and other decision-makers. This means more exact figures that showcase the total cost of owning a Digital Twin of complex process is needed to successfully sell the idea to management. Calculating the total cost of ownership can be done using an estimate calculator. The estimate calculator is capable of calculating the cost of purchasing the necessary hardware, software packages, energy costs, and other costs associated with owning Digital Twin technologies.

It is also important to highlight any supporting technologies that will be needed for data collection if an accurate Digital Twin is to be developed. These technologies may include embedded systems in manufacturing equipment, IoT devices, cloud computing services to scale simulations, and augmented reality devices. These complementary technologies and services may also add to the bottom line of designing a functional Digital Twin environment of complex systems. Thus, using the estimate calculator to highlight the ROI of creating a Digital Twin is one of the major steps that must be taken to convince management about the need for a Digital Twin.

Ask the Right Questions – During strategy sessions, some push backs are expected and this will definitely be the case when selling the idea of a Digital Twin to enhance business operations. This push back should be expected even after using data to answer questions, creating case studies or applicable scenarios, and defining the return on the investment made. When the expected push back occurs, the best way to understand how management thinks and the challenges they foresee is by asking questions. Asking the right questions provides you with the foundation needed to provide the answers needed to convert non-believers into believers.

The questions to ask are varied and should be determined by the level of skepticism shown by particular decision-makers. Some of the questions you must ask to assess the mindset of your superiors include:

  • Do you need more information to make a decision and what information do you need? The purpose of asking this question is to have an idea about what your audience or manager is thinking. Remember that IT managers are notoriously skeptical about new technology therefore, having an understanding of the prejudices management has against Digital Twin technology is important.
  • I know you love the way things are going, but would you be interested in a 6-month trial? If the feedback you get from the manager and decision-makers is negative due to their satisfaction with how things are within the company, this question might help break the ice. Satisfaction with the present condition of things can dampen the enthusiasm for Digital Twin technology. But pushing for a trial could be the turning point that turns ‘no’ into a ‘yes’.
  • If it helps you surpass your personal targets will you try it? It may come as a surprise to you that managers think more about self-preservation than the success of a business. This is one reason why your manager may be resistant to change. Thus, making your questions a bit personal and putting the manager’s self-interest first may be the strategy that gets him/her to experiment with Digital Twin technology.

These questions should be asked without sounding too pushy to your manager and other decision-makers. This is because a pushy attitude could be interpreted as a desperate attempt to make some money for yourself on the side.

Associate the Integration of Digital Twin with Achieving Business Goals – Finally, the ace in your back pocket should be tying the integration of Digital Twin technology to the ideology of the enterprise. This includes highlighting how Digital Twin can be used to realize the business’s mission statement or meet certain key performance index (KPIs). With the answers to the questions asked above, accomplishing this task should be a bit easier than the first steps of selling the idea to management.

Your ability to showcase the benefits of Digital Twin technology and how it meets your company’s culture or KPIs is determined by your knowledge about the transformative powers of the technology. To accomplish this, a lot of research is needed to know more about how this emerging technology can be applied to your business case. Once again, you can turn to the information highlighted in these tips to refine your pitch to resell the idea of integrating Digital Twin technology to your manager. For more information, you can also choose to attend conferences centered around the adoption of Digital Twin technology in your industry.

The Benefits of Digital Twin Technologies is Worth the Extra Effort

Digital Twin technologies create value in diverse ways that can ease the effort expended doing your job. Some of the more important benefits include:

  • Descriptive Values – The ability to visualize the status of an asset in real-time via its Digital Twin is valuable when those assets or facilities are either remote, complex, or dangerous. In plants and other facilities, a Digital Twin makes information easily accessible for interpretation and to make business decisions.
  • Diagnostic and Standardization Value – In facilities where hundreds of variables are involved with production, Digital Twin technology can be used to stabilize these variables, pinpoint the root cause of problems, and leverage analytics to standardize complex systems.
  • Predictive Value – Industry-leading enterprises like General Electric have used Digital Twin technologies to improve efficiency and the output of plants. This was accomplished using a Digital Twin to propose solutions that can lead to customer satisfaction and profitability.

You can learn more about getting started with Digital Twin technology in your facilities, plants, and business by speaking to a Simio engineer today.

Industry 4.0 Revolution: Understanding the Digital Twin and Its Benefits

The world is moving toward an era of more efficient business operations driving by automation. This was one of the key messages of Mitsubishi Hitachi Power Systems CEO, Paul Browning, at the just concluded 2019 CERAWeek held in Houston. Paul Browning, who was the keynote speaker on the ‘digital transformation agenda’, spoke about Mitsubishi’s use of artificial intelligence (AI), machine learning, and digital Twin technologies to create the world’s first autonomous power plant.  He ended his speech by saying ‘Mitsubishi is building the world’s first autonomous power plant capable of self-healing.’

The use of digital transformation technology to eliminate downtime and reduce unplanned shutdowns are just a few of what can be accomplished with Digital Twin technologies. In fact, the ability to virtualize workspaces and complex systems have important roles to play in achieving the smart factory and Industry 4.0 revolution the most industries dream off. This is because no other emerging technology has the potential to bridge the gap between the physical world driven by machines and the virtual world like the Digital Twin. This is why the Digital Twin market is forecasted to be worth approximately $26billion by 2025.

While the numbers highlight the growing acceptance of Digital Twin solutions, many businesses are a bit skeptical about its implementation and benefits. This is why practical case studies are needed to highlight the application of the Digital Twins and how others have benefited from it.

The Most Important Benefits of Digital Twin Technology

Industry 4.0 business model relies on data to automate business processes. The Digital Twin, in turn, creates the perfect environment for collecting data from every aspect of the manufacturing process for analytics and simulation. When data is accurately collected and a Digital Twin is designed, system integrators, data analysts, and other stakeholders can use it to drive business policies and improve decision-making processes. The benefits Industry 4.0 and manufacturers stand to gain from Digital Twins include:

Enhanced Plant Performance – Having the capacity to access and quantify every information produced from a manufacturing process and the shop floor is key to automation. Digital Twin technologies allow manufacturers collect data from the sensors and embedded systems integrated onto a shop floor. The Digital Twin also takes things a step further by replicating physical manufacturing processes and creating a digital environment where these processes can be assessed.

With the necessary data from equipment, machines, material handling, and production cycles in place, manufacturers can develop policies and run simulations to determine how efficient they are. Once determined, the manufacturing policies and regulations can then be applied on the shop floor. This gives large enterprises a cheaper way to access the effects of decisions on productivity levels.

A DHL study on the importance of Digital Twin in enhancing plant performance highlights the use of Digital Twin by Iveco solutions to optimize welding capabilities. The Iveco manufacturing line struggled with constant breakdown of its welding components which delayed production. The cause of these breakdowns were pin-pointed to a lamellar pack which wore out constantly. To enhance performance and reduce downtime, Iveco designed a Digital Twin model of its manufacturing line/

The Digital Twin model helped Iveco understand the different welding concepts and requirements, as well as, their effect on the lamellar pack. Using simulation and machine learning, Iveco developed an optimal welding process that could forecast the probability of component failures in other to reduce them.

Driven Predictive Maintenance –  One of the benefits of designing a Digital Twin of manufacturing shop floors or plants is the opportunity to integrate predictive maintenance into business models. Predictive maintenance involves the prediction of a component or machine failure and the taken of preemptive action to forestall failure. Digital Twin technology has created an environment that drives predictive maintenance across various systems.

Once again, The Mitsubishi Hitachi Power System plant serves as an example where Digital Twin technology can drive the predictive maintenance policy in Industry 4.0. The Digital Twin model created by Mitsubishi gives power plants the ability to monitor sensors and other parameters that determine the plant’s performance levels. On application, the Digital Twin, alongside AI, and machine learning provided insight on the best time to schedule maintenance activities without disrupting production.

The benefits Mitsubishi reaped from its use of Digital Twins include a more efficient way to discover fault components and a maintenance culture that reduced downtime. The autonomous plant was also able to run self-diagnostics and repair stuck valves that affected power generation. Smart facilities can take advantage of the Digital Twin to drive a predictive maintenance culture which will eliminate resource waste and downtime caused by faulty equipment.

Advanced Control of Complex Systems or Processes – Digital Twin ecosystems provide an avenue to control complex systems and processes in ways other traditional technologies can’t. This is because, AI, machine learning, and simulations can be applied to the digital environment thereby allowing enterprises to see farther. Digital Twin takes control process which involves comparing system performances with set standards, discover deviations, and design corrective actions to greater heights. This makes it a great resource for research in Industry 4.0.

An example of how Digital Twins makes advanced control of complex systems possible can be seen from how the U.S. Department of Energy National Energy Technology Laboratory (NETL) deployed Digital Twin solutions. The Digital Twin of the (NETL) plant was used to carry out research on the use of carbon dioxide to power plants as a replacement to the hazardous coal-powered plants currently in use. The Digital Twin also mapped out the plant’s sensor network in other to optimize its use.

The Digital Twin created by the research team served as a virtual testbed for analyzing operational relationships and their effects on power generation. The benefits of Digital Twins, in this case, included a cheaper more effective way to analyze control process phenomena and reduce downtime. Increasing plant reliability and optimizing the use of resources were also singled out as benefits. 

Easing Training and Onboarding Process – The future of Industry 4.0 is being driven by emerging technology solutions such as the industrial internet of things (IIoT), IoT, automated vehicles and equipment. This means to effectively take advantage of the benefits of Industry 4.0 older and new employees must be thought to function in a smart facility. Digital Twins of plant systems and processes provide a virtual environment for employees to learn about operational processes.

In a case study conducted in an automotive facility, employees were taught the repair and assembling process in a virtualized environment and through manuals. At the end of the training employees preferred the option of learning through virtualized environments and retained more information compared to learning from physical manuals. This means the hands-on learning approach driven by Digital Twin technology creates a better environment for learning complex process safely.

Take Advantage of The Benefits of Digital Twins

The combination of Digital Twin technology and cloud computing has made the design, emulation, scheduling, analytics, and simulation services it offers even more affordable to end-users. Small and medium scale businesses can now access Digital Twin solutions to solve complex problems. This means Digital Twin as a Service is slowly but surely becoming an option for enterprises to explore. You can learn more about the Digital Twin opportunities for your business by contacting the experienced engineers at Simio.

Resources:

https://www.ice.org.uk/knowledge-and-resources/case-studies/digital-twins-for-building-flexibility-into-power

https://www.logistics.dhl/content/dam/dhl/global/core/documents/pdf/glo-core-digital-twins-in-logistics.pdf

https://www.bloomberg.com/news/articles/2018-04-09/forget-cars-mitsubishi-hitachi-sees-autonomous-power-plants

Digital Twin Technology: 5 Challenges Businesses Face By Overlooking It

A Disruptive technology is a product, concept or service that has the ability to redefine the traditional way of doing things.  Today, the digital twin concept is being hailed as a disruptive technology with the capacity to change how we design, solve complex problems and collaborate. In fact, a Gartner Report predicted that by 2021 approximately 50% of industrial companies will integrate the use of digital twin technologies to increase workforce performance and manufacturing efficiency. So, what is this disruptive technological concept?

The Digital Twin refers to a real-time replica of a physical entity. This entity could be a living thing, an inanimate physical object, as well as, assets, processes and systems that function in the physical world or environment. Although this concept is actually three decades old, the convergence of emerging technologies such as the internet of things (IoT), artificial intelligence (AI), machine learning has taken it to new heights. Digital twins juxtapose these emerging technologies to create digital models of physical entities with the ability to simulate real-time changes that occur to the physical model.

An example of how this concept work involve the development of the digital twin of an aircraft. With the digital twin, finite element analysis (FEA) can be applied to determine the fatigue limit of the aircraft’s structure. The results of this simulation can then be used to design or choose more suitable materials or design for a more durable aircraft. Outside manufacturing, digital twins can be employed in diverse industries including healthcare to simulate how the human body reacts to external forces. The benefits of integrating digital twins include increased design efficiency, enhancing predictive analysis, and collaboration.  This is why the market for it is expected to hit approximately $15billion by 2023. The benefits of digital twins are huge but the challenges business will face not embracing it is even bigger.

This article will discuss:

  • The challenges businesses face not integrating the digital twin in business operations.
  • The effects of not embracing the digital twin.
  • The disruptive capabilities of the digital twin.

The Five Challenges Businesses Face Not Embracing Digital Twins

With approximately 50% of industrial companies integrating the use of digital twins, the 50% who don’t will definitely be losing their competitive edge. This is because the digital twin will redefine real-time simulation applications in ways the average 3D modelling software or Building Information Modelling platform can’t aspire to. The challenges to expect include:

Keeping Legacy Solutions, Designs or Data – As the generation of baby boomers continue to retire daily, the probability of losing the knowledge that built legacy equipment and systems could be lost. This includes the Mylar copies of traditional manufacturing equipment or the designs of legacy military aircraft. Regardless of technological advancements, the loss of legacy data destroys the foundations newer prototypes were built on.

With the aid of the digital twin concept, businesses across every industry, can create an accurate digital model of legacy equipment or solutions. The digital model can then be stored for posterity sake or analyzed with the aim of developing upgraded prototypes. Models can also be used as materials for training the younger generation of workers through virtual reality environments.

 Enhancing Lean Manufacturing Processes – Toyota’s integration of lean manufacturing to speed up production while efficiently using resources has become folklore in the automotive industry. The integration of lean manufacturing models – which were disruptive at that time – helped Toyota dominate the industry for decades. This is the leverage the digital twin concept offers. The ability to optimize entire product value chains is something that can be achieved in real-time through the digital twin.

A study at the Bayreuth University, Germany focused on analyzing the impact of digital twins in collecting real-time data and optimizing production systems. The study compared the efficiency of digital twins and the commonly used value stream mapping solutions. In the end, the results showed that digital twins exceeded traditional solutions in data acquisition, automated derivation of optimization measures, and the capturing of motion data. These data which are crucial to optimizing production could also be utilized in a digital twin environment to optimize diverse processes. Thus, shunning digital twins will leave firms in the lurch while competitors who leverage this concept can optimize production variables in real-time.

Limitations in the Integration and Use of Data – The Industry 4.0 revolution currently going on relies heavily on the collection and use of data to receive important business insight and automate processes. The tools or applications currently used today are enterprise relationship management software, and industrial cloud solutions. Although these solutions do excellently well in collecting data from smart or industrial internet of things (IIoT) devices, they still struggle with collecting data from legacy or dumb equipment. This limits the penetration of Industry 4.0 in the deepest layers of manufacturing shop floors which is what the OPC Foundation intends to solve.

Digital twin concepts can help smart factories integrate dumb equipment from the deepest levels of a shop floor into models of the manufacturing plant. This makes it possible to capture the hundreds of non-measured information in the shop floor into a digital environment thereby truly meeting Industry 4.0 and OPC UA standards. If successfully done, the digital twin with the captured data can be used to predict the facilities transient response to external disturbances, equipment failure, and system malfunctions.

Manufacturers who overlook digital twin concepts will be stuck with using data from only smart equipment and IoT devices to track real-time changes on the shop floor. The limitations associated with not capturing non-measured data will lead to approximations when automating operations in a smart factory. This could lead to downtime, an inefficient workforce, and in extreme situations accidents to workers.

Limiting the Effectiveness of Predictive Analysis – Another important challenge shunning the integration of digital twins into industrial operations is the difficulties that come with making blind or half-informed changes. Making blind changes when making important decisions such as designing a new material handling system or reducing the number of processes needed to develop a product will have terrible consequences. These consequences will include wastage of resources, a subpar end product or confusion on the shop floor.

According to Gartner, downtime in the manufacturing industry could lead to huge losses. In the automotive industry alone, downtime is responsible for a loss of $22,000 per minute. Although the numbers may be less in other industries, the effects are still considerable. Digital twins can help eliminate these challenges or losses by helping businesses simulate the real-time effect of making certain changes. For example, a change of production schedule while going through a transition period would have left the aviation manufacturer Lockheed Martin unable to meet its delivery timelines. With the aid of the Simio simulation software, the manufacturer was able to make informed decisions that optimized the production process.

Next Steps

The match to industry 4.0 and a more connected factory is one that must be planned for if manufacturers intend to remain competitive for the long run. One way to achieve this is by integrating a digital twin for simulating and receiving the insights needed to automate industrial processes. If properly executed, you will be turning the disruptive nature of the digital twin to your benefit.

Resources:

https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-digital-twins/

https://news.thomasnet.com/companystory/downtime-costs-auto-industry-22k-minute-survey-481017

https://www.isw.uni-stuttgart.de/en/institute/highlights/digital-twin/

https://blogs.opentext.com/addressing-the-data-challenges-in-the-digital-twin/

https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-digital-twins/