Polarbear-GBI consolidation exercise 23

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Exercises in Supply Chain Optimization and Simulation using anyLogistix Prof. Dr. Dmitry Ivanov Berlin School of Economics and Law Professor of Supply Chain and Operations Management Modified by Dr. Ed Lindoo, Campbellsville University, 2020. To be cited as: Ivanov D. (2019). Exercises in Supply Chain Optimization and Simulation using anyLogistix, Berlin School of Economics and Law, 2 nd , updated edition © Prof. Dr. Dmitry Ivanov, 2019. All rights reserved.

1. Introduction Supply chain network design and operational planning decisions can have a drastic impact on the profitability and success of a company. Whether to have one warehouse or two, close a factory or rent a new one, or to choose one network path over another are all consequential decisions a supply chain (SC) manager must make. However, these decisions must be the result of more than experience or intuition, and, as a result, research in SC management (SCM) is geared towards providing the data, tools, and models necessary for supporting SC managers’ analytical decisions. One of these decision-supporting tools is anyLogistix, a software which facilitates Greenfield Analysis, Network Optimization, and Simulation. anyLogistix has become more and more popular with the provision of the free PLE version, and because it is an easy-to-use software, includes simulation and optimization, and covers all standard teaching topics (center-of-gravity, efficient vs responsive SC design, SC design through network optimization, inventory control simulation with safety stock computations, sourcing (single vs. multiple) and shipment (LTL vs FTL) policy simulation, and milk-run optimization). The ALX exercise book addresses the application of quantitative analysis methods and software to decision-making in global supply chains and operations. Understanding of optimization and simulation methods in SCM is the core of the course. Technical skills for using simulation and optimization software in praxis can be acquired with the help of anyLogistix software. This case study is designed to stimulate and enhance conceptual and analytical decision-making skills in actual operating situations. The case method requires you to prepare a decision based on careful evaluation of case facts and numbers to the extent possible. As with all business situations, there may be insufficient facts, ambiguous goals, and dynamic environments. This case seeks to convey the following skills: Analytical Skills : Students will possess the analytical and critical thinking skills to evaluate issues faced in business and professional careers. Technical Skills : Students will possess the necessary technological skills to analyze problems, develop solutions, and convey information using optimization and simulation software. Along these lines, throughout the course we will examine two scenarios:

 Building a new SC from scratch -a case study of the Polarbear Bicycle company, which must create and optimize its SC in order to maintain profitability and keep its competitive edge in an increasingly global market where sales prices are driven down while costs re main stable and seeks to analyze the performance of their existing SC and optimize its distribution network, while considering the risks and ripple effect. Using the models available in anyLogistix, we will conduct analyses to (1) determine an optimal location using Greenfield Analysis (GFA) for a new warehouse, given the location of their current customers and those customers relative demands, (2) compare alternative network designs using Network Optimization (NO).

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2. Case study 2.1 Description of Case Study Customer Bicycle Type Demand per day Cologne x-cross 2 Cologne urban 50 Cologne all terrain 15 Cologne tour 10 Bremen x-cross 7 Bremen urban 30 Bremen all terrain 20 Bremen tour 20 Frankfurt am Main x-cross 6 Frankfurt am Main urban 5 Frankfurt am Main all terrain 4 Frankfurt am Main tour 5 Stuttgart x-cross 15 Stuttgart urban 15 Stuttgart all terrain 1 Stuttgart tour 40 Costs Value in USD Factory Nuremberg: fixed (other) costs, per day 15,000 Factory Poland: fixed (other) costs, per day 5,000 DC Germany: fixed (other) costs, per day 15,000 DC Germany: carrying costs (per bicycle) 3.00 DC Czech Republic: fixed (other) costs, per day 5,000 DC Czech Republic: carrying costs 2.00 DC Germany: processing costs (inbound and outbound shipping per pcs) 2.00 DC Czech Republic: processing costs (inbound and outbound shipping per pcs) 1.00 Factory Nuremberg: production costs (per bicycle) 250 Factory Poland: production (per bicycle) 150 All bicycles: product purchasing costs 30 Transportation costs; Paths: from factory -to DCs 0.01 * product(pcs) * distance Transportation costs; Paths: from DCs -to customers 0.01 * product(pcs) * distance Unit revenue 499 Table 1 We consider a company called Polarbear Bicycle. Polarbear Bicycle was founded as an e-commerce start-up selling bicycles, however they were just purchased by the company you work for as an analyst……Global Bikes (GBI). With this new purchase, the board of directors of GBI is asking a number of questions that you as an analyst for GBI need to answer. Polarbear’s portfolio includes four different types of bicycles: x-cross, urban, all terrain, and tour bicycles. You have been assigned the task to find the best location for one or two new distribution centers (DC). First, you estimate customer demand based on Table 1 above. Polarbear distributes their bicycles to four

locations throughout Germany: Cologne, Bremen, Frankfurt am Main, and Stuttgart. Table 1 shows customer demand, which is equal to 245 bicycles per day. GBI now needs you to analyze supply and distribution network alternatives and to develop a best-case scenario for Polarbear-GBI Bicycle. You are charged with con- ducting a GFA to determine the possible location of a new DC or DC’s in Germany, as well as a network optimization to compare several options for network paths.

2.2 Greenfield Analysis (GFA) for Facility Location Planning: Selecting the Best Warehouse Location for Polarbear-GBI Bicycle Now we conduct a GFA for the outbound network of Polarbear-GBI Bicycle considering the four customers located in Cologne, Bremen, Frankfurt am Main, and Stuttgart. The aim of this GFA is to determine the optimal location of one (or two) new DC’s in Germany subject to total minimum transportation costs. Note: for the purposes of this analysis we are not considering current GBI customers or DC’s within Europe. Polarbear-GBI makes and sells very unique bicycles that currently are not a good fit within the GBI network, therefore we consider a completely separate distribution network. Creating an ALX model. Step 1. Open Anylogistix. Click on Import Scenario then select the file you downloaded, PB GFA Level 2 with Solutions.xlsx. Change the scenario name to your name Note: You may receive a warning about old data file. You should be able to say OK and just ignore it. Performing experiments. Data from Table 1 has already been entered for Customers, Demand, and Products. Step 2. Go to GFA Experiment and run it for “Number of sites = 1” and the period of two months. Run the experiment by selecting the blue button Step 3. Analyze the results using statistics “Flows” and “New Sites”: Note: Use the Polarbear-GBI case study answer sheet to submit ALL of your answers. DO NOT Submit this document 1. What are the optimal coordinates of the DC? 2. What is the maximum distance from the optimal DC location to a customer? 3. What is the minimum distance from the optimal DC location to a customer? 4. What are the total costs of the SC? (Note: like you did last week, you’ll need to copy all of the numbers in the flow cost estimate column to a spreadsheet and sum your answers). 5. Compare the data in statistics “Flows”and Table“Demand”. Do we satisfy all

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customer demands from the optimal DC location? If Yes, why? If no, why? Step 4. Go to GFA Experiment and run it for “Number of sites = 2”. Step 5. Analyze the results using statistics “Flows” and “New Sites”: 6. What are the total costs of the SC? 7. Compare the results with one and two DCs in terms of costs and responsiveness. 8. What other costs were not considered in selecting the optimal facility location in the GFA?

2.3 Network Optimization (NO) for Facility Location Planning: Comparing Polarbear’s Supply Chain Design Alternatives After selling the bicycles from the newly established DC(s) according to the GFA results, Polarbear-GBI decided to produce their own bicycles. Their production facility has now been established in Nuremberg and 250 bikes are produced each day. Recently, they have received an offer from a Polish production factory to rent a DC in the Czech Republic at a reasonable price. The same company also wants to offer them rental of a factory in Warsaw, Poland, even though they already have one factory in Germany. Polarbear-GBI must now decide which SC design is more profitable:  Option 1: DC in Germany and Factory in Germany  Option 2: DC in Germany and Factory in Poland  Option 3: DC in Czech Republic and Factory in Poland  Option 4: DC in Czech Republic and Factory in Germany In Fig. 1, the different possibilities for the path networks are shown. The dotted lines show possible alternatives and the solid lines the existing structure of Polarbear’s SC. Figure 1. Network optimization alternatives The aim of the NO is to determine which network design is optimal based on Polarbear’s selected KPIs, e.g., profit. Therefore, the factory in Warsaw, Poland, the DC in the Czech Republic, and the DC in Steimelhagen were added as inputs to the model along with the Nuremburg factory. To enable the model’s calculation, the reality of the case must be simplified: all demand is assumed to be deterministic without any uncertain fluctuations. To define the two-stage NO problem (transport between factories and DCs and between DCs and customers) from a mathematical perspective, several parameters must be input as data. These are shown in Table 2.

The costs of the rent for the factory in Poland and the DC in Czech Republic are included in “othercosts”. For transport, it is always assumed that each truckload fits 80 bicycles, and trucks travel at a speed of 80 km/h. Creating an ALX model Step 0. Probably best to close and re-open ALX at this point Step 1 import the file PB NO Level 2 Solution.xlsx. Rename it so that it has your name or initials as the scenario name: Note: Data from Table 2 has been entered for you. Note: You may receive a warning about old data file. You should be able to say OK and just ignore it. Performing experiments Step 1. Go to Network Experiment and run it with the Demand variation type “95-100%”.

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Step 2. Analyze the results using statistics “Optimization Results”, “Flow Details”, “Production Flows”, “Demand”, and “Overall Stats”: b. place a screen shot here clearly showing your new NO results with your name or initials in the scenario name. This needs to be a full screen shot showing the AnyLogistix logo in the top left corner, and date/time stamp bottom right. Like this: 9. What is the most profitable SC design? 10. Is demand for all customers satisfied? Why or Why not? 11. What is the total revenue of the most profitable SC? 12. What is total profit of the most profitable SC? 13. Compare the data in statistics “Production Flows” and Table “Demand”. Does the production quantity correspond to the total demand? Explain. 14. Compare the optimal SC design as computed in the NO and the initial SC design (factory and DC in Germany) in terms of profit. 15. What other costs should be considered when redesigning the SC according to NO results? 16. What other factors, apart from costs, should be considered when re- designing the SC according to the results of the NO? News flash! The company just contracted a deal with a UK and Ireland company that has 4 regional customers. This expands your offering to another 4 customers, but they are pretty far from Germany. Where would you now put your DC’s and what would be the optimal amount to have based on cost flow? To figure this out you can go back to the PB GFA that you imported in step 0 ( PB GFA Level 2

with Solutions.xlsx) or you can use the one you renamed that should already be loaded. To accomplish this you first need to add these 4 customers. Click on the blue customer icon (#1) then with your mouse, move over London and try to put the icon right in the middle of London. Change the name from Customer to London. Do this for Birmingham England, Dublin Ireland and Belfast Ireland. Next, you need to go into the demand table and set for each city. They are only going to do the bicycle “tour” for now, so set all 4 cities to that. Now, run a GFA experiment with 3 DCs. Note: When you go to transfer these numbers to the quiz they many not be

exactly the same. This is because of where you placed your 4 new customers. It would be almost impossible to get them in the exact same spot that I did, and so the numbers might be off slightly. 17. What is the total flow cost with 3 DCs? 18. What is the total flow cost with 2 DCs? 19. What is the total flow cost with 5 DCs? 20. Having also tried 1 site and 4 sites, assuming that it costs $15 million per DC to maintain it yearly, adding flow costs and DC maintenance costs together, what would be the lost cost scenario?

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Polarbear-GBI consolidation exercise 23

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