Production Planning Software and Industry 4.0

The latest era of industrial revolution – Industry 4.0 connects and revolutionizes various aspects of the industry including manufacturing processes as well as business processes such as supply chain. The increasing demand of customized product from the customer end is a major driving theme of this transformation in the industry. The traditional processes are highly efficient for batch production and low cost scaling in bulk manufacturing but are relatively time consuming inefficient for manufacturing customized products. Similar is the case for the business processes and models that being used around this manufacturing style. There is need of new production planning style which can simulate the costs, efficiency and resource requirements in real time for any product for mass customization.Industry 4.0 uses Cyber Physical Systems (CPS) and Internet of Things (IoT) to introduce technological and human improvements, which ultimately results in enhanced productivity, product quality with reduced manufacturing time and product price. Hence, the requirement of an advanced production planning and scheduling scheme becomes paramount. In this article,we will discuss how production planning can be implemented in Industry 4.0 and the ways in which it will help manufacturers of any and every product to adapt easily to customer demands and transition smoothly into the upcoming industrial economy.

Industry 4.0 brings along the requirement of new process and production planning where most of the working environment is automatized and the data recorded is processed using fog computing, on-premise clouds or cloud computing servers. Machine to Machine communication is expected to increase more than ever. These changes raise some critical questions and concerns regarding the manufacturing and planning processes:

  • Is it possible to completely automatize production planning using CPS and IoT?
  • Can human knowledge be translated into future products?

The role of Production Planning Software in Industry 4.0 will be to address these concerns effectively and ensure that the decision making processes involved in process selection, resource allocation, operation sequence and scheduling and sufficiently automatized with knowledge importer from previous processes. This should then result in the modeling of the future product including customer based customization demands as well.

Traditional process planning being used in many industries presently is completely based only on the knowledge and experience of the individual or team working on the system. The people working on the systems are technology experts from experience rather than knowledge. The existing demand for change to the new technology solutions can be a big transition for such individuals. This might slow down the progress of these industries, especially SMEs which are slower in the adaptation process. Hence, it is important for each industry to build their own strategy to implement Industry 4.0.

All the manufacturing resources in the industry are now connected to data and information exchange enabling better quality and process control. Scheduling of the product manufacturing and supply chain are being solved by using dynamic scheduling with the help of Structure Dynamics Control (SDC). Data and knowledge is transformed to software that makes a decision based on the technical specification of the order and available material combinations. This type of process planning has been adopted completely in very few industrial processes such as welding.It is still a challenge for many manufacturers to figure out what would be the optimal technique if an industry manufactures various products with different set of technologies. Also, the scaling of this single technology-single product scheme( e.g. welding) might not be easy on multiple types of products. Visualization of the process and predetermining the resource requirements will become more important. Simulation of the complete Production Planning using real time data can be an effective solution to this problem.Let us see how a product planning software can make the manufacturing process ’smarter’.

”Smart products” enable an industry to include information about customization demands of the consumer,collect feedback which can then be used in knowledge databases used in the various phases product design, development and manufacturing process. These include process planning, operation sequencing and scheduling. The collaboration of various product parameters and consumer needs in each stage of product development cycle allows the manufacturer to continuously improve the product quality and optimize the manufacturing costs effectively in real time. This results in an overall better product from both consumer and manufacturer’s point of view. Product Planning Software enable this whole cycle managing various processes starting from material selection, shape, geometry, operation priority, time of operation, machine cost and avail- ability and many more. The Product Planning can also be linked to the ERP( Enterprise Resource Planning Software) in the cloud to include insights and data to other parts of product lifecycle resulting in a better product with every iteration.

A good production planning software that automatizes the various tasks of the product development cycle is a must for mass customization and improved efficiency in Industry 4.0. Thus, it can be easily concluded that a good Planning Production Software will form a critical building block of the industry in Industry 4.0.

The Evolution of the Industrial Ages: Industry 1.0 to 4.0

The modern industry has seen great advances since its earliest iteration at the beginning of the industrial revolution in the 18th century. For centuries, most of the goods including weapons, tools, food, clothing and housing, were manufactured by hand or by using work animals. This changed in the end of the 18th century with the introduction of manufacturing processes. The progress from Industry 1.0 was then rapid uphill climb leading up to to the upcoming industrial era – Industry 4.0. Here we discuss the overview of this evolution.

Industry 1.0 The late 18th century introduced mechanical production facilities to the world. Water and steam powered machines were developed to help workers in the mass production of goods. The first weaving loom was introduced in 1784. With the increase in production efficiency and scale, small businesses grew from serving a limited number of customers to large organizations with owners, manager and employees serving a larger number. Industry 1.0 can also be deemed as the beginning of the industry culture which focused equally on quality, efficiency and scale.

Industry 2.0 The beginning of 20th century marked the start of the second industrial revolution – Industry 2.0. The main contributor to this revolution was the development of machines running on electrical energy. Electrical energy was already being used as a primary source of power. Electrical ma- chines were more efficient to operate and maintain, both in terms of cost and effort unlike the water and steam based machines which were comparatively inefficient and resource hungry. The first assembly line was also built during this era, further streamlining the process of mass production. Mass production of goods using assembly line became a standard practice.

This era also saw the evolution of the industry culture introduced in Industry 1.0 into management program to enhance the efficiency of manufacturing facilities. Various production management techniques such as division of labor, just-in-time manufacturing and lean manufacturing principles refined the underlying processes leading to improved quality and output. American mechanical engineer Fredrick Taylor introduced the study of approached to optimize worker, workplace techniques and optimal allocation of resources.

Industry 3.0 The next industrial revolution resulting in Industry 3.0 was brought about and spurred by the advances in the electronics industry in the last few decades of the 20th century. The invention and manufacturing of a variety electronic devices including transistor and integrated circuits auto- mated the machines substantially which resulted in reduced effort ,increased speed, greater accuracy and even complete replacement of the human agent in some cases. Programmable Logic Controller (PLC), which was first built in 1960s was one of the landmark invention that signified automation using electronics. The integration of electronics hardware into the manufacturing systems also created a requirement of software systems to enable these electronic devices, consequentially fueling the software development market as well. Apart from controlling the hardware, the software systems also enabled many management processes such as enterprise resource planning, inventory management, shipping logistics, product flow scheduling and tracking throughout the factory. The entire industry was further automated using electronics and IT. The automation processes and software systems have continuously evolved with the advances in the electronics and IT industry since then. The pressure to further reduce costs forced many manufacturers to move to low-cost countries. The dispersion of geographical location of manufacturing led to the formation of the concept of Supply Chain Management.

Industry 4.0 The boom in the Internet and telecommunication industry in the 1990’s revolutionized the way we connected and exchanged information. It also resulted in paradigm changes in the manufacturing industry and traditional production operations merging the boundaries of the physical and the virtual world. Cyber Physical Systems (CPSs) have further blurred this boundary resulting in numerous rapid technological disruptions in the industry. CPSs allow the machines to communicate more intelligently with each other with almost no physical or geographical barriers.

The Industry 4.0 using Cyber Physical Systems to share, analyze and guide intelligent actions for various processes in the industry to make the machines smarter. These smart machines can continuously monitor,detect and predict faults to suggest preventive measures and remedial action. This allows better preparedness and lower downtime for industries. The same dynamic approach can be translated to other aspects in the industry such as logistics, production scheduling, optimization of throughput times, quality control, capacity utilization and efficiency boosting. CPPs also allow an industry to be completely virtually visualized, monitored and managed from a remote location and thus adding a new dimension to the manufacturing process. It puts machines,people, processes and infrastructure into a single networked loop making the overall management highly efficient.

As the technology-cost curve becomes steeper everyday, more and more rapid technology disruptions will emerge at even lower costs and revolutionize the industrial ecosystem. Industry 4.0 is still at a nascent stage and the industries are still in the transition state of adoption of the new systems.Industries must adopt the new systems as fast as possible to stay relevant and profitable. Industry 4.0 is here and it is here to stay, at least for the next decade.

Simio Partner Finalist in Franz Edelman Award

The prestigious Franz Edelman Award for Achievement in Operations Research and the Management Sciences was presented at the Edelman Gala on April 16th, 2018 in Baltimore, Maryland. The Franz Edelman competition honors distinction in the practice of Operations Research and Analytics, by both individuals and companies, with emphasis on the beneficial impact of their achievements.

To reach the finals, companies are required to demonstrate how their use of technology is transforming the approach to some of the world’s most complex problems.

Simio is proud to be the provider of the simulation that facilitated one of this year’s finalists, Europcar, through our partner, ACT Operations Research (ACTOR). Our combined technologies have been used to develop Opticar which provides forecasting, simulation and optimization of the processes relating to vehicle rental for the leading European car rental company.

Simulation for Decision Making

The vehicle rental industry is a huge, complex, constantly changing market, with cultural variations across countries. In order to meet dynamic demand, decisions are continuously made regarding fleet assets, their locations, usage and pricing. The combination of AI, statistical modeling and simulation allows all eventualities to be considered and evaluated in order to establish optimum processes and make informed decisions.

Simulation can be used to model the possibilities with respect to both capacity and revenue, helping managers of car rental companies to reduce their risks in terms of planning for optimal fleet saturation. By making quality decisions, they can constantly maximize business opportunity for the company and ensure consistent financial and service performances.

At Simio, we are constantly solving business problems of this kind through simulation. When complex system schedules and decisions are required, we deliver leading edge solutions across many industries, from manufacturing to transportation and logistics.

Simio is proud to congratulate our partner ACTOR, with Europcar, on their outstanding achievement of becoming a Franz Edelman Award finalist.

Scheduling in the Industry 4.0

Today started badly.

As soon as I hopped into my car, the GPS system was flashing red to show queues of stationary traffic on my regular route to the office. Thankfully, the alternative offered allowed me to arrive on time and keep my scheduled appointments.

In the same way as a GPS combines live traffic data with an accurate map of the city, Simio Software connects real time data sources with a modeled production situation. Just like a GPS, Simio can also impose rules, make decisions, schedule and reschedule.

The major difference is in the scale.

Simio Simulation and Scheduling Software can model entire factories, holding huge quantities of detailed data about each resource, component and material. It leverages big data analysis to run thousands of permutations of scenarios, finding the optimum outcomes for specific circumstances. Lightning fast, it can detect and respond to changes with suggestions that will keep everything flowing in the best possible way.

Thank goodness for Simio, because Industry 4.0 is here.

Smart Factories employ fully integrated and connected equipment and people, each providing real time feedback about their state. Data is constantly collected on each product component, for process monitoring and control. Every aspect of the entire operation is managed through its associated specifications and status data. This large, constant stream of information coming from a known factory configuration can be received, stored, processed and reported upon by the powerful Simio software.

With Industry 4.0, nothing is left to chance. Everything is monitored and optimized, and performance is predicted, measured, improved and adapted on an ongoing basis. Management of so many interconnected components requires a scheduling system that is specifically designed to operate in this dynamic data environment. Simio Production Scheduling Software can be relied upon to provide the integrated solution for enabling technology in the Smart Factories of the future.

We are already seeing a rise in robotics and the increasing digitalization of the manufacturing industry under the effects of Industry 4.0. Soon all components of the factory model will be interconnected, just like my future driverless car that will communicate directly with my GPS to take the best route using current traffic information.

All I will have to do is sit back and enjoy the ride.

How Much Data Do I Need?

I have discussed data issues in several previous articles. People are often confused about how much data they really need. In particular, I frequently hear the refrain “Simulation requires so much data, but I don’t have enough data to feed it.” So let’s examine a situation where you have, say 40% of the data you would like to have in order to make a sound decision and let’s examine the choices.

1) You can possibly defer the decision. In many cases no decision is a decision in itself because the decision will get made by the situation or by others involved. But if you truly do have the opportunity to wait and collect more data before making the decision, then you must measure the cost of waiting against the potential better decision that you might make with better data. But either way, after waiting you still have all of the following options available.

2) Use “seat of the pants” judgment and just decide based on what you know. This approach compounds the lack of data by also ignoring problem complexity and ignoring any analytic approach. (Ironically enough this approach often ignores the data you do have.) You make a totally subjective call, often heavily biased by politics. There is no doubt that some highly experienced people can make judgment calls that are fairly good. But it is also true that many judgment calls turn out to be poor and could have benefited greatly from a more analytical and objective approach.

3) Use a spreadsheet or other analytical approach that doesn’t require so much data. On the surface this sounds like a good idea and in fact, there is a set of problems for which spreadsheets are certainly the best (or at least an appropriate) choice. But for the modeling problems we typically come across, spreadsheets have two very significant limitations: they cannot deal with system complexity and they cannot adequately deal with system variability. With this approach you are simply “wishing away” the need for the missing data. You are not only making the decision without that data, but you are pretending that the missing data is not important to your decision. An oversimplified model that doesn’t consider variability or system complexity and ignores the missing data … doesn’t sound like the makings of a good decision.

3) Simulate with the data you have. No model is ever perfect. Your intent is generally to build a model to meet your project objectives to the best of your ability given the time, resources, and data available. We can probably all agree that better and more complete data results in a more accurate, complete, and robust model. But model value is not true false (valuable or worthless) but rather it is a graduated scale of increasing value. Referencing back to that variability problem, it is much better to model with estimates of variability than to just use a constant. Likewise a model based on 40% data won’t provide near the results of one with all of the desired data, but it will still outperform the analytical techniques that are not only missing that same data, but are also missing the system complexity and variability.

And unlike the other approaches, simulation does not ignore the missing data, but can also help you identify the impact and prioritize the opportunities to collect more data. For example some products have features that will help you assess the impact of guesses on your key outputs (KPIs). They also have features that can help assess where you should put your data collection efforts to expand sample or small data sets to most improve your model accuracy. And all simulations provide what-if capability you can use to evaluate best and worst case possibilities.

Perfection is the enemy of success. You can’t stop making decisions while you wait for perfect data. But you can use tools that are resilient enough to provide value with limited data. Especially if those same tools will help you better understand the value of both the existing and the missing data.

Happy modeling!

Dave Sturrock
VP Operations – Simio LLC

Simulation Stakeholder Bill of Rights

The people who request, pay for, consume, or are affected by a simulation project and its results are often referred to as its stakeholders. For any simulation project the stakeholders should have reasonable expectations from the people actually doing the work.

Here I propose some basic stakeholder rights that should be assured.

1. Partnership – The modeler will do more than provide information on request. The modeler will assume some ownership of helping stakeholders determine the right problems and identify and evaluate proposed solutions.
2. Functional Specification – A specification will be created at the beginning of the project to help define clear project objectives, deadlines, data, responsibilities, reporting needs, and other project aspects. This specification will be used as a guide throughout the project, especially when tradeoffs must be considered.
3. Prototype – All but the simplest projects will have a prototype to help stakeholders and the modeler communicate and visualize the project scope, approach, and outcomes. The prototype is often done as part of the functional specification.
4. Level of Detail – The model will be created at an appropriate level of detail to address the stated objectives. Too much or too little detail could lead to an incomplete, misunderstood, or even useless model.
5. Phased Approach – The project will be divided into phases and the interim results should be shared with stakeholders. This allows problems in approach, detail, data, timeliness, or other areas to be discovered and addressed early and reduces the chance of an unfortunate surprise at the end of a project.
6. Timeliness – If a decision-making date has been clearly identified, usable results will be provided by that date. If project completion has been delayed, regardless of reason or fault, the model will be re-scoped so that the existing work can provide value and contribute to effective decision-making.
7. Agility – Modeling is a discovery process and often new directions will evolve over the course of the project. While observing the limitations of level of detail, timeliness, and other aspects of the functional specification, a modeler will attempt to adjust project direction appropriately to meet evolving needs.
8. Validated and Verified – The modeler will certify that the model conforms to the design in the functional specification and that the model appropriately represents the actual operation. If there is inadequate time for accuracy, there is inadequate time for the modeling effort.
9. Animation – Every model deserves at least simple animation to aid in verification and communication with stakeholders.
10. Clear Accurate Results – The project results will be summarized and expressed in a form and terminology useful to stakeholders. Since simulation results are an estimate, proper analysis will be done so that the stakeholders are informed of the accuracy of the results.
11. Documentation – The model will be adequately documented both internally and externally to support both immediate objectives and long term model viability.
12. Integrity – The results and recommendations are based only on facts and analysis and are not influenced by politics, effort, or other inappropriate factors.

Note that every set of rights comes with responsibilities. The associated stakeholder responsibilities are discussed as part of the Simulationist Bill of Rights.

Give these expectations careful consideration to improve the effectiveness and success of your next project.

Dave Sturrock
VP Products – Simio LLC

Why Simulation is Important in a Tough Economy

Everyone wants to cut costs. No one wants to spend unnecessarily. When budgets are tight, software and software projects are an easy place to cut. Staff positions like Industrial Engineers are sometimes easier to cut or redeploy than production jobs. I suggest that following this reasoning to eliminate simulation projects is often short-sighted and may end up costing much more than it saves. Here are a few reasons why it may make sense to increase your simulation work now.

1) Minimize your spending. Cash is tight. You cannot afford to waste a single dollar. But how do you really know what is a good investment? Simulate to ensure that you really need what you are purchasing. A frequent result of simulations intended to justify purchases is to find that the purchases are NOT justified and in fact the objectives can be met using existing equipment better. A simulation may save hundreds of times its cost with immediate payback.

2) Optimize use of what you have. Could you use a reduction in cost? Would it be useful to improve customer satisfaction? I assume that your answer would always be yes, but even more so in difficult times. But how can you get better, particularly with minimal investment? Simulation is a proven way to find bottlenecks and identify often low-cost opportunities to improve your operation.

3) Control change. In a down economy you are often using your facilities in new and creative ways – perhaps running lean or producing products in new ways or in new places. But how do you know these new and creative endeavors will actually work? How do you know they will not cost you even more than you save? Simulation helps you discover hidden interactions that can cause big problems. Different is not always better. Simulate first to avoid costly mistakes.

4) Retain/improve your talent pool. Some people who might otherwise be laid off may have the skills to be part of a simulation SWAT team. By letting them participate in simulation projects, they will likely achieve enough cost reduction and productivity improvements that they more than pay for themselves. As an added bonus, the team will learn much about your systems, the people, and communication – knowledge which will improve their value and contributions long after the project is complete.

5) Reduce risk. You are often forced to make changes. How do you know they are the right changes? Will a little more, a little less, or a different approach yield better results? How do you measure? A strength of simulation is its ability to objectively assess various approaches and configurations. Substitute objective criteria for a “best guess”, and, in turn, reduce the risk associated with those changes. In a down economy it is more important than ever that you don’t make mistakes.

In summary, rather than thinking of the cost of simulation, you should think of what the investment in simulation today will save you today, tomorrow and every day following. Simulation is not a cost, it is an investment that may return one of the best ROIs available in a tough economy.

Dave Sturrock
VP Products – Simio LLC

Can Simulations Model Chaos?

Can chaotic systems be predicted? I guess we first need to agree on exactly what a chaotic system is.

BusinessDictionary.com defines it as a
“Complex system that shows sensitivity to initial conditions, such as an economy, a stockmarket, or weather. In such systems any uncertainty (no matter how small) in the beginning will produce rapidly escalating and compounding errors in the prediction of the system’s future behavior.”

It is hard to imagine a complex system that does not show sensitivity to initial conditions. If the follow-on statement is true, then there is little point to ever trying to model or predict the behavior of such a system because it is not predictable. But it is not hard to find counter-examples, even to the examples they provided. Meteorologists do a reasonable job predicting the weather; it depends on your standards of accuracy. Certainly they can predict fairly accurately the likelihood of a 90 degree day in January in Canada or anticipating the path of a tropical storm for the next 12 hours.

A less technical but perhaps more useful definition comes from membrane.com:
“A chaotic system is one in which a tiny change can have a huge effect.”
That leads us toward a more practical definition for our purposes.

For the types of systems we normally model, I would propose yet another definition.
A chaotic system is one in which it is likely that seemingly trivial changes in the initial conditions would cause significant changes in the predicted results, over the time frame being considered.

This definition, while not technically rigorous, acknowledges that most of us rarely have the opportunity or the need to deal in absolutes. We live in a world where the majority of decisions are made subjectively (“Joe has 20 years experience and he says…”) or with gross simplification (“Of course I can model that in a spreadsheet…”). In this world, being able to base a decision on a simulation model with better accuracy and objectivity can help realize tremendous savings, even if it is still only an approximation and only useful within specified parameters.

Can we accurately predict true chaotic systems? By strict definition clearly not. And even by my definition, there will be some systems that are just too chaotic to allow any predictions to be useful.

But can we provide useful predictions of most common systems, even those with some chaotic aspects? Absolutely yes. Every model is an approximation of a real or intended system. Part of our job as modelers is to ensure that the model is close enough to provide useful insight. A touch of chaos just makes that more interesting. 🙂

Dave Sturrock
VP Products – Simio LLC

Professional Development

The annual Winter Simulation Conference (WSC) starts two weeks from today. Initially as a practitioner and then later as a vendor I have attended over 20 of these conferences in addition to dozens of other similar events. WSC is just one of many events that you could choose to attend. But why should you attend any of them?

All such events are not identical, but here are a few attributes of WSC that are often found in other events as well:

Basic tutorials – If you are new to simulation, this is a good place to learn the basics from experienced people.

Advanced tutorials – If you already have some experience, these sessions can extend your skills into new areas.

Practitioner papers – There is no better way to find out how simulation can be applied to your applications than to explore a case study in your industry and talk to someone who may have already faced the problems you might face.

Research – Catch up on state-of-the-art research through presentations by faculty and graduate students on what they have recently accomplished.

Networking – The chance to meet with your peers and make contacts is invaluable.

Software exhibits and tutorials – If you have not yet selected a product or you want to explore new options, it is extremely convenient to have many major vendors in one place, many of whom also provide scheduled product tutorials.

Supplemental sessions – Some half and full day sessions are offered before and after the conference to enhance your skill set in a particular area.

Proceedings – A quick way to preview a session, or explore a session that you could not attend. This serves as valuable reference material that you may find yourself reaching for throughout the year.

I think every professional involved in simulation should attend WSC or an equivalent conference at least once early in your career, and then periodically every 2-3 years, perhaps rotating between other similar conferences. If you want to be successful you have to keep your skills and knowledge up to date. And in today’s economy, a strong personal network can be valuable when you least expect it.

I hope to see you at WSC in Miami!

Dave Sturrock
VP Products – Simio LLC

Read My Project Report!

I read a lot, both for business and pleasure. But it seems I never have enough time. So when I sit down with a magazine, for example, most articles probably get less than a couple seconds of attention. Unless an article immediately captures my attention, I quickly move on to the next one. I know that I occasionally miss out on good content, but it is a way to cope with the volume of information that I need to process each day. Consider the implications when you are writing a project report for others to read…

We are all busy. When we are presented with information to read or review, we often don’t have time to wade through the details to see if the content merits our time.

Tell me the most important thing first! Give me the summary! How many times have you asked (or wished) for that?

At one point, it was common to give presentations by starting with an introduction, building the content, and ending with the conclusion – “the big finish”. While this is appropriate for some audiences, many people don’t want to take the time to follow such a presentation. Instead, they want to be presented with a quick overview and a concise summary first. They will then decide to read on if the overview has captured their interest and they need more information.

Think about your own experiences. When you have a document to read and you are not sure it is worth your time, what do you do? If you are like most people you will probably consider most, if not all of the following:
• Does the title look interesting?
• Do you know/respect the author?
• Scan the major headings or callouts for content of interest.
• Scan any pictures/diagrams for content of interest.
• Evaluate the summary or abstract.
While the order and details might differ slightly, at each stage of the above process if you are not convinced of the value of continuing, you will put the document aside. Only after the document has passed this gauntlet of tests, will you spend the time to seriously read the content.

What can we learn from this?

Content is not enough. The best content in the world is of little value unless it is read.

When you are preparing a project report, try to get inside the head of your target audience. If you expect that they will also have a process something like the above, spend adequate time on those parts. Take an extra minute to create an interesting title. Add major headings and callouts to help focus the reader’s attention. Add some figures to help convey and support your message. Have a good abstract and/or summary that is easy to find to help your audience quickly get the point of your report.

Write each report so everyone, including your busy stakeholders, will take the time to read it. Keeping these simple suggestions in mind will help you succeed at getting your message across.

Dave Sturrock
VP Products – Simio LLC