“Our partnership with PMC opens the door to the US market. PMC‘s many years of experience in simulations and the huge library of data for over 4 decades gives us the opportunity to create the perfect Digital Twin for our customers.” said CEO of Smiling Machines, Mr. George Koutsoudakis.
“Predictive maintenance services are one of the biggest bets in the 4.0 industry, something that Smiling Machines seems to be doing with remarkable results,” said Michigan Professor and CEO of PMC Dr. Onur Ulgen.
Smiling Machine is a Greek next generation technology company that provides Predictive Maintenance and Digital Twin services. It is one of the few companies in the world that manufactures its own AI fault detection sensors.
PMC is a leading provider of quality, engineering, manufacturing, training, operations productivity, metrology, terrestrial scanning, and building information modeling solutions. With proven track record spans four decades and includes more than 7,000 completed projects for 700-plus customers. in diverse industries such as automotive and transport, aerospace, defense, healthcare, high technology, architecture, engineering and construction.
Raid Al-Aomar, Edward J.Williams and Onur M. Ülgen.
Understanding The Role of Simulation Modeling
After understanding the concepts and aspects of the term “simulation modeling,” it is necessary to clarify the role that simulation plays in developing production and business systems. Initially, consider the use of simulation technically and economically and then present the spectrum of simulation modeling applications in manufacturing and service sectors.
“Why and when to simulate?” and “How can we justify a simulation project?” are key questions that often cross the mind of simulation practitioners, engineers, and decision-makers. We turn to simulation because of simulation’s capabilities that are unique and powerful in system representation and performance estimation under real-world conditions. Most real-world processes in production and business systems are complex, stochastic, and highly nonlinear and dynamic. Other modeling types such as graphical, mathematical, and physical models fall short in providing a cost-effective and usable system representation under such conditions.
“Decision support” is another common justification of simulation studies. Obviously, engineers and managers want to make the best decisions possible, especially when encountering critical stages of design, expansion, or improvement projects. Simulation studies may reveal insurmountable problems and save cost, effort, and time. They reduce the cost of wrong capital commitments, reduce investments risk, increase design efficiency, and improve the overall system performance.
Although simulation studies might be costly and time-consuming in some cases, the benefits and savings obtained from such studies often recover the simulation cost and avoid much larger costs. Simulation costs are typically the initial simulation software and computer cost, yearly maintenance and upgrade cost, training cost, engineering time cost, and other costs for traveling, preparing presentations with multimedia tools, and so on. Such costs are often recovered through the long-term savings from increasing productivity and efficiency.
A better answer to the question “why simulate?” can be reached by exploring the wide spectrum of simulation applications to various aspects of business, science, and technology. This spectrum starts by designing queuing systems and extends to designing communication networks, production systems, and business operations. Simulation models of manufacturing systems can be used for many objectives including:
Determining throughput capability of a manufacturing cell, an assembly line, or a production system.
Configuring labor resources in an intensive assembly process.
Determining the size and resources in a complex automated storage and retrieval system (AS/RS).
Determining best ordering policies for an inventory control system.
Validating the outcomes of material requirement planning (MRP).
Determining buffer sizes for work-in-progress (WIP) in an assembly line.
For business operations, simulation models can be also used for a wide range of applications including:
Determining the number of bank tellers that results in reducing customers waiting time by a certain percentage.
Designing distribution and transportation networks to improve the performance of logistic and supply chains.
Analyzing the financial portfolio of a company over time.
Designing the operating policies in a fast food restaurant to reduce customer Time-In-System and increase customer satisfaction.
Evaluating hardware and software requirements for a computer network.
Scheduling the working pattern of the medical staff in an emergency room (ER) to reduce patients’ waiting time.
Testing the feasibility of different product development processes and evaluating their impact on the company’s budget and strategy.
Designing communication systems and data transfer protocols.
Designing traffic control systems.
Table 1.1 below shows a summary of ten examples of simulation applications in both manufacturing and service sectors.
To reach the goals of the simulation study, certain elements of each simulated system often become the focus of the simulation model. Modeling and tracking such elements provide attributes and statistics necessary to design, improve, and optimize the underlying system performance. Table 1.2 shows a summary of ten examples of simulated systems with examples of principal model elements.
Like any other engineering tool, simulation has limitations. Such limitations should be realized by practitioners and should not discourage analysts and decision-makers from using simulation. Knowing the limitations of simulation should emphasize using it wisely and should motivate the user to develop creative methods and establish the correct assumptions in order to benefit from the powerful simulation capabilities. Still, however, certain precautions should be considered to avoid the potential pitfalls of simulation studies. We should pay attention to the following issues when considering simulation:
The simulation analyst as well as the decision-maker should be able to answer the question “when not to simulate?” Simulation studies may not be used for solving problems of relative simplicity. Such problems can be solved using engineering analysis, common sense, or mathematical models.
The cost and time of simulation should be considered and planned well. Many simulation studies are underestimated in terms of time and cost. Some decision-makers think of simulation as model building although it consumes less time and cost when compared to data collection and output analysis.
The skill and knowledge of the simulation analyst need to be addressed. Essential skills for simulation practitioners include systems thinking, fluency in programming and simulation software, knowledge in statistics, strong communication and analytical skills, project management (PM) skills, ability to work in teams, and creativity in design and problem-solving.
Expectations from the simulation study should be realistic and not exaggerated. A lot of professionals think of simulation as a “crystal ball” through which they can predict and optimize system behavior. It should be clear that simulation models by themselves are not system optimizers. They are flexible experimental platforms that facilitate planning, what-if analysis, statistical analyses, experimental design, and optimization.
The time frame of the simulation project needs to be realistic and properly set. Insufficient time and resources at various project stages, improper work breakdown structure, and lack of project control are issues that result in project delays and low-quality deliverables. Typical PM skills are essential to execute the simulation project in an efficient manner.
The results obtained from simulation models are as good as the model data inputs, assumptions, and logical design. The commonly used phrase of “garbage-in-garbage-out (GIGO)” is very much applicable to simulation studies. Hence, special attention should be paid to data inputs selection, filtering, and simulation assumptions.
The analyst should pay attention to the level of detail incorporated into the model. Some study objectives can be reached with macro-level modeling while some others require micro-level modeling. The analyst should decide on the proper level of model detail and avoid details that are irrelevant to simulation objectives.
Model verification and validation is not a trivial task. As will be discussed later, model verification aims at making sure that the model behaves according to intended model logic. Model validation, on the other hand, focuses on making sure that the model behaves as the actual system. Both practices determine the degree of model reliability and usefulness.
The results of simulation can be easily misinterpreted. Hence, the analyst should concentrate the effort on collecting reliable results from the model through proper settings of run controls and by using the proper statistical analyses. Typical mistakes in interpreting simulation results include relying on short run time, including biases caused by model initial conditions in the results, using the results of only one simulation replication, and relying on the mean of the response while ignoring variability inherent in response.
The analyst should pay attention to communicating simulation inputs and outputs clearly and correctly to all parties of the simulation study. Also, the results of the simulation model should be communicated to get feedback from parties on relevancy and accuracy of the results.
The analyst should avoid using wrong measures of performance when building and analyzing the model results. Such measures should represent the kind of information required for the analyst and the decision-maker to draw conclusions and inferences on model behavior.
The analyst should also avoid the misuse of model animation. In fact, animation is an important simulation capability that provides engineers and decision-makers with a valuable tool of system visualization. Such capability is also useful for model debugging, verification, and validation. However, some may misuse model animation by relying solely on observing the model for short-term, which may not necessarily reflect its long-term behavior.
Finally, the analyst should select the appropriate simulation software tool that is capable of modeling the underlying system and providing the required simulation results. Criteria for selecting the proper simulation software tool typically include price, modeling capabilities, learning curve, animation, produced reports, input modeling, output analysis, and add-in modules. Simulation packages vary in their capabilities and inclusiveness of different modeling systems and techniques such MHS, human modeling, statistical tools, animation.
A manufacturing plant with machines, people, transport devices, conveyor belts, and storage place.
A bank or other personal-service operation, with different kinds of customers, servers, and facilities like teller windows, automated teller machines (ATMs), loan desks, and safety deposit boxes.
An IT organization with software products, developers (e.g., coders, testers, reviewers, etc), file servers, automated testing tools, software migrations and releases.
A distribution network of plants, warehouses, and transportation links.
An emergency facility in a hospital, including personnel, rooms, equipment, supplies, and patient transport.
A field service operation for appliances or office equipment, with potential customers scattered across a geographic area, service technicians with different qualifications, trucks with different parts and tools, and a central depot and dispatch center.
A computer network with servers, clients, disk drives, tape drives, printer, networking capabilities, and operators.
Freeway system or road segments, interchanges, controls, and traffic.
A central insurance claims office where a lot of paperwork is received, reviewed, copied, filed, and mailed by people and machines.
A chemical products plant with storage tanks, pipelines, reactor vessels, and railway tanker cars in which to ship the finished product.
A fast-food restaurant with workers of different types, customers, equipment, and supplies.
A supermarket with inventory control, checkout, and customer service.
A theme park with rides, stores, restaurants, workers, guests, and parking lots.
Pedestrian flow in malls, museums, buildings, stadiums, airports, plants, etc.
Military planes, rockets, etc. that can be operational at any one time under different scenarios, maintenance, material handling, and supply chain operations.
Eight Mistakes to Avoid When Hiring a Technology Consultant
Technology is constantly evolving and it affects almost every aspect of today’s businesses. In order for your company to stay on the cutting edge, you must keep up. That means having good IT consultants who can keep your business at the cutting edge of current technology. When it comes to the mission-critical task of hiring a good consultant, you can’t afford to make a mistake. Here are the top eight mistakes to avoid:
Failing to Identify Your Needs
There are numerous reasons for hiring a Technology Consultant from upgrading your current enterprise system (or modules), maintaining or modernizing your legacy systems, updating software and hardware, or just having a qualified resource to help train your staff or work on short term projects. No matter the need, finding the right Tech for the job first requires identifying exactly what you need him/her for.
Too Little Manpower
If the work you need is extensive and you’ve hired an independent contractor, there may not be enough manpower to handle the job. That could either create a situation in which the work goes on far too long with no end in sight or simply does not get done at all. Neither situation is acceptable. It’s important to hire a company that has enough manpower to handle the size of the job.
A consulting contractor can handle the job if it is within their means. Some small companies can also handle some very large jobs as well. Just be certain the size of the company suites the size of the job.
Area of Expertise
It’s important that the person hiring the consultant understands what the company needs well enough to conduct the interview with pointed questions. Does this consultant have the expertise to do your specific job? Technology consultants cover many different areas of expertise. Their resume may not pinpoint your job. Therefore, interview questions must be specific in cases like these. Do you, as the interviewer, have the specific knowledge required to garner whether or not this consultant can do what he or she needs to be able to do for your company? Are you asking the right questions? Is someone else within your company in a better position to be interviewing for this contract?
Get it in writing
Be certain you know exactly what you’re getting for your money. Be certain the entire scope of work and job is outlined within the contract. What will the consultant be providing? Some consultants set up hardware, software, or complicated systems and then leave without training anyone to use it. Be certain that having someone on your team trained is part of the service and that it is specified in writing as part of the contract with the IT consulting firm. This is a critical part of your contract.
Since the IT consultant will have access to some of your most sensitive data, be certain this contract also includes a confidentiality agreement as well as a right to all Intellectual Property created during services rendered.
Make Sure the IT Consultant is Not Trying to Sell You a Product
Your Technology Consultant should be focused on what is best for you and your company – not on selling you a particular product because he/she gets a kickback from that company. Ask them outright if they receive a commission for selling you a particular software. Be certain that your written agreement with the IT consultant specifies that they are not engaged to sell you a particular product, that they do not represent any one software or hardware company. Only in that way can you be certain that the tech has your best interest in mind.
Failing to Speak the Same Language
In addition to making sure someone within your company is trained to continue using the software or hardware or system that the Technology Consultant has put into place, be certain that someone within your company, someone who has been involved in the interview process (see #3) understands exactly what the consultant is doing. They don’t have to understand how the tech is doing what he’s doing, only why he is doing it. If you’re in a role that requires oversight of specific software, hardware or systems, you should understand this information. Anyone within your organization that will have direct involvement working with a particular software or system should be involved in hiring this consultant to be certain communication is open and fluid.
Becoming Dependent on Your Technology Consultant
If you or someone trusted in your organization does not train in what the consultant is putting in place within your system, the consultant can build in complexities that will require you to call on them frequently at their regular hourly rate (which could be astronomical and eternal). Additionally, they could custom-build something into the system. Be certain that your contract gives you full ownership of any Intellectual Property conceived during the project.
An unsavory Technology Consultant can hold your systems hostage as was exhibited with the Colonial Pipeline recently. Ransomware is prevalent. Generally, it’s not as blatant as a consultant who has been inside your business – but it can be. Why take the chance? There have been numerous instances of highjacking associated business bank accounts and draining the funds. It can take months or even years to recover these funds – if ever. And often the perpetrators never get caught. Don’t let this happen to you. That’s why it’s so important to make sure you are working with a reputable company and consultants.
Know your consultant. Looking at this person’s resume is not enough. Your Technology Consultant will have access to some of your most sensitive data. Be certain that he has had this responsibility before. Know that he has completed past projects on time and with exceptional results. You can only be certain of this by having his employer verify this information. You can also so do a separate background check for internal purposes as well.
Hiring a Technology Consultant is a big responsibility. Don’t let it intimidate you. The consultant or consulting company is there to help you. By selecting the right consulting company and avoiding these pitfalls, you can make this a win-win for both parties. Remember, working with the right consultant could make the difference between success or failure for your company.
The client is a leading global supplier of automotive dampers and performance shock absorbers in commercial and defense segments. PMC was able to offer its expertise in Ergonomic Analysis, MODAPTS and Dynamic Simulation modeling to identify the challenges faced by the customer to improve the current throughput in their efforts for capacity expansion. Our utilization of discrete event simulation techniques and Ergonomic analysis techniques allowed the company to identify utilization of all the stations and operators working in the production line, identify the potential ergonomically perilous activities carried out by the operator and provide solutions to the high-risk ergo concerns. The combination of Simulation Analysis along with Non-Value-added (MODAPTS) analysis and Ergonomic analysis has resulted in improvement in the line capacity and throughput.
The current line has 11 stations running 2 shifts per day. Each shift being 8 hours there are 11 operators allocated to the stations. There are 2 stations which are replenished by the line leaders in addition to the 11 operators. Out of the 11 stations, 2 station are outside the line in the current state, which will eventually be shifted to the line in the future state. There are 22 FGI parts for 2 different customers (Customer A and Customer B) making a share of 66.7% and 33.3% respectively. A third of the FGI parts produced for Customer A go through the offline machines currently. Essentially there are 2 different process flows with respect to the type of FGI being produced.
The current process flows and procedures at the plant have been proving to be inefficient to improve productivity, and meet customer demands due to ramping up of production and inclusion of new products on the line. With the unreliable equipment on the line the company had a challenge to improve their throughput with wide product variety and high changeover times. It was necessary for the client to focus on the current operational improvements to reduce the overall cycle time of the products.
a) Data Collection and Validation (PFEP): Data collection in simulation modelling is a vital process that highlights the required data sets and their desired properties such as accuracy, sample period and format to allow the simulation model to achieve the project objectives. A PFEP (Plan for Every Part) was developed with the data collected from various sources and validated on the line.
b) Ergo Analysis (JACK): All the operator movements associated with operations, handling etc., during production for each station have been captured and studied. The 3D models of the stations were imported to JACK to study the movements of the operators at and between the stations and the postures were simulated. The Ergo analysis provided qualitative and quantitative rating to the movements of the operator. One of the rating systems that provide the magnitude of risk associated with a task is RULA (Rapid Upper Limb Assessment). From the analysis, the medium to high risk items were identified and suggestions/improvements were made on these operator movements. The suggestions were re-simulated in Jack and results were provided. This unique technique of PMC has ensured that the customer identified the tasks that will need immediate changes and eventually it would save on cycle time due to minimization or elimination of high-risk tasks.
c) MODAPTS: PMC’s expertise in MODAPTS is utilized in this case to identify the Non-value added and waste movements of the operators in the process for each station. All the operator movements were video recorded and analyzed utilizing a custom built MODAPTS sheet created by PMC. The MODAPTS sheet gives an account of all the Value Added, Non-Value Added and Waste activities associated with the process. Based on the operations suggestions were made on tasks, equipment utilized in the operations by the operator and standard operating procedures to minimize the Non-Value Added and Waste times associated with the cycle. At this point MODAPTS and JACK analysis improvements go hand in hand, since any changes made to ergonomics would reflect in MODAPTS due to change in the operation.
d) Simulation Model (PlantSim): The layout, the PFEP and the Production Schedule are fed to PlantSim, to get the base simulation model. The current process flows of material and the operators are incorporated in the model. The current capacity and throughput are obtained after running the simulation for 40 days with provided schedule. The changes made through Ergo analysis and NVA study are incorporated in the simulation study to get the future state simulation model and an analysis of the simulation results were made.
The improvements made through the MODAPTS NVA analysis and the JACK ergonomic analysis resulted in efficient utilization of the operators at the stations. By simulating the changes made and running scenarios the following throughput improvements have been achieved.
1. Base model + MODAPTS NVA Analysis + Jack Ergo Analysis – 4.33% improvement in throughput
2. Scenario 1 + Addition of resource – 25.56% improvement in overall throughput
3. Scenario 2 + Change in SOPs – 41.77% improvement in overall throughput
The unique approach to problem solving through simulation not only achieves the output the client wishes for but also identifies the hazardous and perilous processes along the way and aims to keep the work environment streamlined and safer while being efficient at the same time.
The client, a major automotive company, desired a new vehicle distribution system for its North American dealership network. The goal was to create a system that would be responsive to customer choices while reducing distribution costs. After comprehensively evaluating the supply chain, with an emphasis on customer satisfaction metrics, PMC developed and recommended a Distribution Center (DC) plan which optimally balanced customer needs and transportation costs. This plan demonstrated the possibility of reducing transportation costs by 25% while simultaneously improving customer service.
• Inefficient vehicle distribution system
• High inventory at point of sales location
• Low customer responsiveness
• High transportation costs
• Long vehicle delivery times
• Inadequate service levels
Vehicles manufactured abroad were shipped to multiple ports within the United States to satisfy North American demand. Dealerships received
inventory directly from the ports nearest to their respective metro area. Most transportation from portside distribution centers to dealerships was performed via road transportation (i.e. trucks).
The primary objective of the project was to improve customer satisfaction with a cost-effective distribution plan. Features of the former plan targeted for improvement included:
• High Transportation costs between ports and metro markets
• Long vehicle delivery times
• Waning customer satisfaction metrics relating to vehicle choice and availability
The client was considering the introduction of more distribution centers, closer to dealerships, as a potential strategy for improvement. PMC was tasked with both developing tools to generate and evaluate various distribution center placement alternatives, and proposing an improved distribution plan. Both the quantity and location of distribution centers were to be analyzed.
PMC’s first step was to thoroughly document the existing distribution network. To do this, a multi-step plan was initiated: First, process maps describing the customer and vehicle flow were created. Then, key contributors to customer service level and transportation costs were identified, using created dynamic and stochastic input variables. Such variables included dealer inventory control policies, truck load factors, customer demand and demand seasonality, as well as transportation delays. PMC consultants developed both a Mixed Integer Program (MIP) optimization and discrete event simulation model to represent the details of the distribution network. Results of the MIP, obtained with AMPL Plus, combined with ProModel what-if analysis techniques were used to determine the optimal number of DCs to include and the ideal locations to place them.
PMC’s MIP was developed to generate distribution center alternatives that minimized transportation-related costs per year. The alternatives were then evaluated using the simulation model, which explicitly considered the probability and dynamic elements in the system, and hence, estimated the overall effect of the given options more realistically. The client was updated on the distribution network options available to them, the expected benefits of each, and the new design recommended by PMC.
The solution outcomes demonstrated that a decentralized DC concept would achieve the designated performance criteria. Significant cost reduction opportunities relating to DC inventories and transportation modes were revealed. It was shown that, under certain circumstances, the recommended distribution network could yield over $20 million savings per year in transportation-related costs. In addition to cost savings, the distribution plan improves customer service levels by increasing the likelihood of first-choice vehicles being available and reducing the instances of lost customers.