1 THE VALUE OF DISCRETE-EVENT SIMULATION IN COMPUTER-AIDED PROCESS OPERATIONS Foundations of Computer Aided Process Operations Conference Ricki G. Ingalls, PhD Texas State University Diamond Head Associates, Inc. January 9, 2017
2 2 Presenter Background Ricki Ingalls Current Position Principal and Co-Founder, Diamond Head Associates Inc. Department Chair and Associate Professor, Texas State University, Computer Information Systems and Quantitative Methods, McCoy College of Business Administration Former Associate Professor in Industrial Engineering and Management at Oklahoma State University and Site Director, Center for Engineering Logistics and Distribution (CELDi) Education Ph.D. Management Science Texas (1999) M.S. Industrial Engineering Texas A&M (1984) B.S. Mathematics East Texas Baptist (1982) Work Experience 32+ Years, including 16 years in industry and 16 years consulting after joining academics
3 3 My Co-Author Dr. Ron Morgan Current Position Chief Technical Advisor at an Energy Services Company Education Ph.D. Agricultural Engineering Texas A&M (1979) M.S. Agricultural Engineering Oklahoma State (1976) B.S. Agricultural Engineering Oklahoma State (1975) Work Experience Tenured Associate Professor at Michigan State before leaving for industry. 37+ Years, with companies such as Ralston Purina, Kraft, Pepsico, and Halliburton
4 4 What we want to do today We want to think about the Process Industry, specifically, a plastics extrusion business, and consider the following: What is the supply chain for that business? How is that supply chain changing in rapidly changing markets? What models are being used today to run the business? What would be different if we used discrete-event simulation to help run the business?
6 6 Overall Characteristics of the business Production facility handles a variety of standard, custom and semi-custom products for their customers. Product mix means that the company is reacting to demand. Products are being delivered from their distribution centers to stores (for standard products) or Shipped directly to stores (for custom and semi-custom products) The company deals with its own raw material suppliers that supply key product to the manufacturing facility.
7 7 Customers (Stores) Customers are becoming more demanding. Customers want to keep low inventory in their stores for the standard products and fast delivery of the custom products. Increased demand of custom products shows a desire for more variety in their product selection. They always want low prices.
8 8 Managing the Distribution Centers (DCs) Company has 5 regional DCs to supply to the stores. DCs were established before custom parts became part of the company s offering to the customer. DCs are located in Indianapolis, Los Angeles, Dallas, Atlanta, and New Jersey. Each DC has the issues of inventory management, repackaging product for the needs of the store, and effectively managing seasonal demand in the business. The DC managers feel like the they have little control their product receipts and how they configure shipments.
9 9 Managing the Distribution Centers (DCs) Managing the shipment of product from the DCs to the stores is becoming increasingly complicated. In the past, the stores were happy to get weekly shipments and would place orders weeks before the shipment occurred. Today, the stores are demanding multiple shipments per week where they order just hours before the truck is scheduled to leave the distribution center. Also, freight costs are increasing at a rate where they are close to their manufacturing costs.
10 10 Manufacturing Located in the Dallas area. Built to do long production runs very efficiently. Increased product variety and custom orders has made manufacturing less efficient. Manufacturing is under pressure to shorten production runs and changeover more often. The company is also shipping product from the manufacturing facility directly to the stores. There are questions about the cost effectiveness of this policy because the company is shipping very small lots at higher prices to get the order to the store quickly.
11 11 Manufacturing Manufacturing deals with a set of issues that are unique to the plastics extrusion process. They include: The waste created by the process. With each run, it takes 30 minutes to get the process to produce quality product and 15 minutes to clean out the product at the end of a run. Manufacturing wants to minimize waste. The process must be controlled. Elastic jet swell, exit pressure and temperature and other viscoelastic properties must be constantly monitored to be sure that they are within expected operating ranges. In addition, manufacturing must adjust the equipment whenever the process is outside of specifications. If the process is out of control, it must be stopped and adjustments must be made.
12 12 Raw Materials and The Suppliers Keep in-house inventories low. Push ownership of the inventory to the suppliers with just-in-time delivery.
13 13 Supply Chain The company want to keep overall costs as low as possible and still deliver to the client. The company monitors and adjusts inventory and transportation that is under its control to keep costs at a minimum.
14 14 Supply Chain Trade-off Issues (Simchi-Levi, et. al, 2008) Lot size vs. inventory trade-off Inventory vs. transportation trade-off Lead time vs. transportation cost trade-off Product variety vs. inventory trade-off Cost vs. customer service trade-off
15 15 Lot Size vs. Inventory Trade-Off Manufacturers want large lot sizes Reduces set up costs Reduces overall costs (volume) Increases inventory Customers, Retailers, and Distributors want short delivery lead times and wide product variety The Trade-Off How to get manufacturing efficiency and still make the customers happy
16 16 Inventory vs. Transportation Trade-Off Transportation wants full trucks going to a single destination Minimizes transportation costs Efficient loading and unloading Delivers large quantities all at once Management wants inventories to be low The Trade-Off Transportation costs go up with smaller delivery lot sizes, but inventories are lower and more predictable
17 17 Lead Time vs. Transportation Cost Trade- Off Businesses want short total lead times Total lead time is made up of time devoted to processing orders, to procuring and manufacturing items and to transporting items between the various stages of the supply chain Reduces inventory holding costs Transportation wants full trucks going to a single destination The Trade-Off The shortest lead times are accomplished when items are not queued, i.e. produced and moved one at a time. Transportation systems are very expensive when they move one item at a time.
18 18 Product Variety vs. Inventory Trade-Off Customers want product variety Customer like a wide variety of choices Inventory is more complex and difficult The Trade-Off This industry may not be able to use delayed differentiation as a strategy to simplify inventory and production Product designs become more expensive
19 19 Cost vs. Customer Service Trade-Off Customer service is absolutely critical Delivering products quickly Delivery high quality products Replacing products when they break But this costs a lot of money The Trade-Off Company has to spend money to improve customer satisfaction. Spend more money when it is cheap, i.e. when it is early in the supply chain, in order to delivery quality and service to customers late in the supply chain
21 21 What is a model? Any mathematical model or heuristic that helps makes decisions in the company. These models can be found in spreadsheets, heuristic algorithms, linear optimization, mixedinteger optimization, non-linear optimization, stochastic optimization, continuous simulation and discrete-event simulation.
22 22 In a perfect world Physics-based stochastic optimization or simulation optimization models that Give optimal controls on our manufacturing process Determine manufacturing schedules Coordinate logistics and delivery to the customer Take in to account random demand, random disruptions in the supply chain and random disruptions in manufacturing Run instantaneously
23 23 Applicability of the models that run the business Models that run the business have to take into account two criteria: What question are you trying to answer? What constitutes a quality answer?
24 24 Modeling techniques based on type of question Type of Question Has a static set of inputs and only one possible solution to those inputs Has a static set of inputs and want to choose the best solution from a large set of possible solutions. Has random inputs (such as demand) and generates a static solution. Has both random inputs and random processes. You want to determine statistics-based outputs. Modeling Approach Spreadsheet Heuristic Algorithm Linear Optimization Mixed-Integer Optimization Non-Linear Optimization Stochastic Optimization Discrete-Event Simulation
25 25 The Fact: Even though stochastic behavior is seen throughout the business, stochastic models are not used in the decision making process.
26 26 Question 2: What constitutes a quality answer? Deals with scale, efficiency, and the assumptions of the model being run. Every model has a time limit On-line models must respond in 2 seconds (Akamai Technologies, 2009) Plant scheduling models in minutes or seconds. Monthly production planning can be an hour or more. Supply chain decisions can run for several hours.
27 27 Question 2: What constitutes a quality answer? A quality answer is generated by a model with proper assumptions A quality answer is understood by the user An example of set-up time in manufacturing. A supply chain model would abstract the set-up time A scheduling model would explicitly model the set-up time
28 28 Models for Customers Demand Forecasting: Most common demand forecasting algorithms look at history and determine a forecast. Algorithms include exponential smoothing and Box- Jenkins. If past demand is an indicator of future demand, this is a legitimate approach. Marketing often uses demand elasticity models to determine demand based on price and promotions. Demand forecasting can be a group effort that settles on a reasonable demand forecast.
29 29 Models for DCs Assumes a demand forecast Truck delivery schedules Simple: weekly deliveries on a given day Complex: route optimization Inventory Policies X weeks of inventory Replenishment models: (R,Q), (S,s), etc. Inventory replenishment triggers orders to manufacturing OR manufacturing produces for efficiency and the DC receives that schedule
30 30 Models for Manufacturing Most difficult to generalize. Scheduling a facility is complex and applicationspecific. Plastics extrusion scheduling needs to: Get the orders out on time Include the setup and teardown times based on sequences of products. This also includes the waste created by the process. Determine the batch size (or run size) of each batch.
31 31 Models for Manufacturing Schedules must be efficient keep the machines working. With 30 minutes setup and 15 minutes teardown, a 3 hour batch would be 80% efficient not including planned and unplanned maintenance, quality issues, etc. Scheduling models tend to be rule-based Rules are often in Master Scheduler s brain Advanced companies use optimization or rulesbased simulation to create the schedule.
32 32 Models for Raw Materials and Suppliers We will assume that the raw material suppliers are using vendor-managed inventory and delivering to the lines in a just-in-time fashion. From a model standpoint, this relieves the company from worrying about the constant volatility that they are forcing on the suppliers. The volatility is real, though, and it costs money, which shows up in the price of the raw material.
33 33 Models for the Supply Chain Supply chain models can be as simple as spreadsheets and as complex as large-scale mixedinteger models. These models are almost always strategic decision models and not tactical decision models. The most detailed model would be a supply chain planning model, which is likely a mixed-integer optimization.
34 34 The Value of Discrete-Event Simulation Today So, here is the key question If there is so much randomness in the supply chain, why don t our models reflect that randomness? If you want random inputs, you should use stochastic optimization for a static (expected value) output or discrete-event simulation for stochastic output. We do not plan or schedule based on random inputs and we do not give random outputs to the next tier in the supply chain.
35 35 Customers Input: Arrival of orders (customers) for the store and their order profile. An initial random forecast would need to be created. Model: A resource-constrained discrete-event simulation model run at the store level to determine how much demand can actually be satisfied. The model could be run with or without stock-outs as well. Output: The discrete-event simulation model would give results on projected sales, which could be aggregated to a forecast over time.
36 36 DCs Input: Random replenishment orders from the stores Model: Constraints include the trailers, trucks, dock doors, labor, material handling equipment and available inventory Assume that the inventory policies in place and effective. Random processes include any human activity including picking and packing and most product movement activity Discrete-event scheduling model would be beneficial Output: Demand for the manufacturing tier
37 37 Manufacturing Inputs: Actual orders and forecasted orders from distribution and/or customers. Model: Simulation evaluates alternative schedules that deals with the issues unique to the extrusion process, including waste, length of production runs, effect of setup and changeover, and random issues such as quality issues and equipment failures. Output: The result is a schedule that evaluates delivery risk Manufacturing simulation is readily available today through several different suppliers
38 38 Supply Chain The design and planning of the supply chain have typically been handled with large-scale mixed integer optimization algorithms. These are the best algorithms for facility location and other supply chain design issues. Large scale mixed-integer formulation does not evaluate the performance of the supply chain under dynamic conditions. This is a second step in the supply chain design process. It is the only way to address key customer delivery metrics such as on-time delivery under the random conditions.
39 39 Discrete-Event Simulation Applications Available Today Simulation has been used in supply chain design since the late 1990 s (Ingalls and Kasales 1999). The leading supply chain optimization vendor also has supply chain simulation capability (Llamasoft). Simulation companies such as Simio, (Simio LLC), ARENA (ArenaSimulation), FlexSim (FlexSim Simulation Software), and AnyLogic (AnyLogic) all advertise supply chain simulation capability. Distribution center and manufacturing simulation has also been performed since the 1960 s when discrete-event simulation was first introduced (Goldman, Nance and Wilson 2010). All of the major discrete-event simulation packages have the ability to simulate distribution centers and manufacturing sites.
40 40 Discrete-Event Simulation Applications Available Today AutoMod (Applied Materials) and Demo3D (Emulate3D Inc.) were built specifically to model material handling systems. Simulation packages have been to evaluate facility designs or changes to an existing facility design. Simio (Pegden) has implemented a simulation-based scheduling package that has the ability to imbed all of the decision rules for a schedule, generate Gantt charts that can be used to fine-tune the schedule Simio can analyze the schedule under dynamic conditions to determine the performance of the schedule based on key performance indicators such as on-time delivery of each order, cost variance, etc.
42 42 Why Don t Our Models Take Into Account Stochastic Behavior? If there is so much randomness in the supply chain, why don t our models reflect that randomness? We do not have standard mechanisms in place to pass stochastic information from one tier of the supply chain to the next. Without this standard for stochastic information for planning and scheduling, if a model uses stochastic inputs, the inputs must be determined outside of the normal planning and scheduling systems.
43 43 The Distribution-Correlation Forecast A new idea: The Distribution-Correlation Forecast. The idea is to schedule on actual orders and plan based on a distribution-correlation forecast. The distribution-correlation forecast includes: Distributions for each time period Correlations between those time periods This distribution-correlation forecast gives much more information for the decision makers in the business than a traditional forecast.
44 44 Distribution-Correlation Forecast Example Demand Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Forecast Month Correlation Max Min Mode Mean Correlation
45 45 What are the advantages? Two modeling techniques can effectively use this data: Stochastic Optimization and Simulation For stochastic optimization, this forecast can be used to generate scenarios for the model. Discrete-event simulation can use this data to generate randomly arriving individual customer orders under a set of assumptions.
46 46 Discrete-Event Simulation in this Environment Two Primary Advantages It can model the business rules for decision making at any level. The statistical output of the simulation can generate the distributioncorrelation data. Input: The set of actual customer orders and a distribution-correlation forecast Output: The shipment (order completion) output is a distribution over time. Simulation is used because random customer orders (generated from the customer order forecast) can be put through the standard business processes and generate a distribution of resource constrained shipment schedules for those orders. The output can be arranged to any time-period needs of the distribution center. Using this philosophy, it is possible to use simulation at each tier of the supply chain.
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