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.

Why Daily Plans Fail

At 6:00 Monday morning I create a plan for my day starting at 7:00. That doesn’t seem to be such a difficult task. Why is it that by 7:30 my plan already shows signs of being hopeless?

I’ve done the obvious things. First I upgraded from a magnetic Gantt chart based on hand-written information to Advanced Planning and Scheduling (APS) software. That was much easier to use, but frankly the results didn’t dramatically improve. Feeding it with live data from my Manufacturing Execution System (MES) got me a good starting point, with a lot less effort than the paper approach, but my plan still didn’t hold up to the test of time.

I then realized that my software was based on standard lead times and it assumed infinite capacity — it was constantly overestimating my production capability. So I updated to Finite Capacity Scheduling (FCS) software. That helped a lot. But I still had a lot of problems because the FCS tool was based on a “standard” data model for my industry. I guess we do things a bit different than most people in our industry, but the schedule it generates doesn’t recognize those differences.

So I updated to a general purpose simulation product with the flexibility to model my system as it really is AND generate the Gantt charts and other reports I need for scheduling. So now I can account for that problem aisle where my lift trucks get so congested. And I can account for that machine cluster that shares access to a single crane. As a bonus I also got an animation that lets me “play out” the day and visually see what I can expect.

Now I have a much better plan that is realistic and accurate as long as everything goes well. But it is always optimistic. While I can put in preventative maintenance, there is no way to factor in that my Cobalt 120 machine is 30 years old and breaks down almost every day. Or that my supplier for Jenkins 257 material is often way behind their promised delivery. I can pad the schedule to allow extra time, but that just guarantees that I will waste valuable production time when things go well.

In my simulation tool I can run my model with all that variability accounted for (stochastic analysis) and it gives me good long-term capacity analysis. But since there is no way to predict a specific “random” problem, like an equipment failure, I can’t use that knowledge in generating my plan for today — I am limited to a deterministic schedule … or am I?

Actually there is a new technique available called Risk-based Planning and Scheduling (RPS) that first generates a deterministic plan, then applies a stochastic analysis to that plan. It actually tells me how likely it is that I will meet the plan. For example, orders that require the Cobalt 120 machine or Jenkins 257 material may show a high risk of not completing on time. Since I know this before the shift starts, I have more options on how to deal with it – like adjusting labor assignments, rerouting a process, or expediting a material. I can even evaluate the various alternatives to determine which one performs best, and then base my plan on the alternative that generates an acceptable risk at the lowest cost.

Now that’s a plan I can live with!

Happy Modeling!
Dave Sturrock, VP Operations, Simio LLC

General Simulation Project Approach

People often wonder “When is the best time to incorporate simulation into a project?” The answer, without a doubt, is at the earliest possible moment — when an idea for a significant system change or major investment is first being discussed. While it is true that at this early point in a project there are many unknowns and often very little data, simulation can still provide significant value with often a very low level of effort. While the specific issues obviously vary based on the exact systems, at these early stages you are often looking for gross measures of capacity planning and throughput analysis, impact on other facilities, and early identification of potential problem areas.

With modern tools, you can often create high-level simulation models to study such issues in not much more time than it might take to develop a comparable spreadsheet. But instead of using a spreadsheet that is limited to often misleading static analysis and fairly simple relationships, simulation can take full account of the variation and complexity present in most real systems. And as the project concepts mature, the simulation can expand and mature along with it and continually provide value at each step of the project.

For example a project might go through phases with typical questions like these:

1. Early concept validation – How will this new system work? What is the estimated capacity and throughput? What impact will this have on existing facilities? How can I communicate potential issues to stakeholders?

2. High-level system design – What components should be included? What are realistic design objectives? Evaluation of trade-offs of various investments and level of capability provided. High-level bottleneck analysis. Identify “surprises” while they are still easy to deal with.

3. Detailed system design – What specific equipment should be used (e.g., degree and type of automation)? What procedures should be implemented? What reliability can be expected and how will that impact performance and costs?

4. Implementation –Does the system perform as expected and if not, why not and how can it be “fixed”? What is the optimal staffing? When is a “change order” worthwhile?

5. Start-up – What is the impact of learning curves? What are realistic expectations during transition to full capacity? How long will that transition require? What special procedures should be put in place during that transition, what is their cost, and how soon can they be phased out?

6. Operation – How to plan and schedule the intermediate and short-term facility operation? How to effectively deal with the variability present in all systems (e.g., equipment and personnel problems, demand variation, shifting priorities, …)? How well is the system performing on the actual demand as opposed to the originally anticipated or “optimal” demand?

7. System improvement/re-design – As the system reaches stable operation, new ideas, procedures, and technologies will occur. What would be the impact of incorporating changes? Which changes have the best ROI? How do the changes relate to each other?

Until next time … Happy Modeling!