Use The Polarbear-GBI Answer Sheet To Submit Your Answers. The Two Excel File Provide Are Called For As You Go Through The Exercise.

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, 2nd, 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).

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 conducting 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 New Scenario, click OK. Next 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.

Select custom periods and make sure the default dates 11/1/17 – 12/31/17 are set.

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.

 

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: to compute the sum of costs or flows in GFA Results, just slightly drag the heading of the column “Period” in table “Product flows” in the space over the table.

5. Compare the data in statistics “Flows”and Table“Demand”. Do we satisfy all 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 Po

larbear’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. Now create an new scenario as you did in Step 1 above and 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 NO Experiment and run it with the Demand variation type “95-100%”.

NOTE! In order to run the NO experiment, make sure the units in experiment settings is set from m3 to pcs to align it with product data.

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.

 

 

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?

 

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