Exercises in Supply Chain Optimization and Simulation using anyLogistix

Level 1 Basic

Prof. Dr. Dmitry Ivanov

Berlin School of Economics and Law

Professor of Supply Chain and Operations Management

To be cited as: Ivanov D. (2023). Exercises in Supply Chain Optimization and Simulation using anyLogistix, Berlin School of Economics and Law, 5^th Edition

© Prof. Dr. Dmitry Ivanov, 2023. All rights reserved.

Table of Contents

 TOC \o "1-5" \h \z 1. Introduction ................................................................................................................ 2

2.      Case study 1 ...........................................................................................................  5

2.1     Description of Case Study...................................................................................... 5

2.2     Greenfield Analysis (GFA) for Facility Location Planning: Selecting the Best Warehouse

Location for Polarbear Bicycle .....................................................................................  6

2.3     Network Optimization (NO) for Facility Location Planning: Comparing Polarbear’s Supply

Chain Design Alternatives.............................................................................................. 8

2.4     Simulation: Dynamic analysis of the Polarbear’s supply chain design ..............  14

2.5     Comparison experiment ....................................................................................  21

2.6     Validation using Variation ................................................................................... 23

2.7     Recommendations............................................................................................... 25

3.      Case study 2........................................................................................................... 26

3.1     . Description of Case Study.................................................................................. 26

3.2     Problem Statement ............................................................................................. 28

3.3     Greenfield Analysis (GFA) ...................................................................................  32

3.4     Network Optimization (NO)................................................................................. 34

3.5     Simulation ..........................................................................................................  38

3.6     . Risk analysis: Two-month disruption at one of the DCs.................................... 41

3.7     Comparison experiment ....................................................................................  45

3.8     Validation using Variation ................................................................................... 47

3.9     Recommendations............................................................................................... 49

3.10     References ........................................................................................................ 51

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’ 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). This guide has been developed to support a course in Supply Chain Optimization and Simulation using anyLogistix software using two sample project reports. The following themes will be consid­ered:

•        Facility Location Planning (COG, Trade-off Efficiency vs Responsiveness)

•        Supply Chain Design (Network optimization, CPLM)

•        Inventory Control Policy (simulation, safety stocks, ordering policies)

•        Sourcing Policy (simulation, single vs multiple sourcing)

•        Shipment Policy (LTL/FTL, aggregation rules)

This collection of exercises is designed as an application add-on to the main ALX Handbook which provides technical descriptions of how to build ALX models. The main ALX handbook is available at www.anylogistix.com.

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 course is designed to stimulate and enhance conceptual and analytical decision-making skills in actual operating situations, and will lean heavily on case studies developed throughout the course. Class sessions will be spent mainly discussing the cases and software implementation,

where students will be required to evaluate actual business situations and apply their relevant skills, experience, and judgment to develop viable resolutions to business problems using pro­fessional software for decision-making support. Cases are drawn from both industry and service environments, calling for decisions in different facets of supply chain and operations manage­ment (SCOM). 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.

Upon completing the course, students should be able to do the following:

•        Develop critical thinking skills, be able to identify, generalize, prioritize, isolate, and reduce complexity in complex and ambiguous operational situations,

•        Understand how strategic considerations influence operational decisions,

•        Apply analysis and improvement tools learned in previous courses to actual business situations,

•        Strengthen qualitative and quantitative reasoning skills for operational decision-making.

This course 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 case studies:

•        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 re­main stable and

•        Improving the existing SC - a case study of the beer producer BERLIN BREWERY, which 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), (3) perform a Simulation of different scenarios, (4) validate

the models using Validation, Comparison experiments, and (5) analyze SC behavior under un­certainty using the Risk Analysis experiment.

The author thanks The AnyLogic Company for their invaluable feedback, comments and sce­nario updates to this exercise book. The author also wishes to thank Ms. Meghan Stewart for a thorough proof-reading. The author thanks students Meghan Stewart, Jannes Zuch, Chantal Reimann, Moritz Albrecht, Stephanie Paeschke, Julia Dyck, Lily Creed, Christin Kemper, Ragna Maria Berg in MA Program Global Supply Chain and Operations Management | GSCOM at Berlin School of Economics and Law for case-study samples used in this exercise book. Finally, the author thanks Mr. Nurlan Mammadzada, a former student in the GSCOM master program as well as Mr. Hiran Prathapage, a research associate at HWR Berlin for the updates of all exercises and educational materials.

2.   Case study 1

2.1   Description of Case Study

We consider a company called Polarbear Bicycle. Polarbear Bicycle has been newly founded as an e-commerce start-up selling bicycles. Polarbear’s portfolio includes four different types of bicycles: x-cross, urban, all terrain, and tour bicycles. Polarbear needs to find a good location for a new distribution center (DC). First, they estimate customer demand. 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.

Table 1. Customer demand

 

Polarbear Bicycle has hired a consulting firm to analyze supply and distribution network alter­natives and to develop a best-case scenario for Polarbear Bicycle. They are charged with con­ducting a GFA to determine the possible location of a new DC in Germany, as well as a network optimization to compare several options for network paths. The consulting firm was also asked to run a simulation to validate several KPIs and plan inventory, and to conduct a sensitivity analysis to verify all results as well.

2.2     Greenfield Analysis (GFA) for Facility Location Planning: Selecting the Best Warehouse Location for Polarbear Bicycle

Now we conduct a GFA for the outbound network of Polarbear 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 new DC in Germany subject to total minimum trans­portation costs.

Performing experiments

Step 1. Go to GFA Experiment and run it for “Number of sites = 1” and the period of one year (select “all periods”). In order to activate the experiment settings, the settings button should be clicked.

Step 2. Analyze the results using statistics “Flows” and “New Sites”:

a)     What are the optimal coordinates of the DC?

b)     What is the maximum distance from the optimal DC location to a customer?

c)     What is the minimum distance from the optimal DC location to a customer?

d)     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” or “From” in table “Prod­uct flows” in the space over the table).

e)     Compare the data in statistics “Flows” and Table “Demand”. Do we satisfy all customer demand from the optimal DC location?

f)      What other costs were not considered in selecting the optimal facility location in the GFA?

Solution:

Step 1. Go to GFA Experiment and run it for “Number of sites = 1” and the period of one year.

Step 2. Analyze the results using statistics “Flows” and “New Sites”:

 

a)     What are the optimal coordinates of the DC?

50.82; 7.47

b)      What is the maximum distance from the optimal DC location to a customer?

266.11 km to Bremen

c)     What is the minimum distance from the optimal DC location to a customer?

43.55 km to Cologne

d)     What are the total costs of the SC?

$16,351,067

e)     Compare the data in statistics “Flows” and table “Demand”. Do we satisfy all customer demand from the optimal DC location?

Yes, total flows equal total demand. (we compare data in Table “Demand” and flows in the statistics “Flows”).

g)     What other costs were not considered in selecting the optimal facility location in the GFA?

Fixed facility costs, inventory holding costs, processing costs.

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 according to the GFA results, Polar­bear 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 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.

						Czech Republic Potential DC
				Nuremberg: Existing Factory			Steimelhagen: Potential DC
		INBOUND						OUTBOUND
	Figure 1. Network optimization alternatives

Potential Factory

7л?

TH?

Frankfurt

7П?

Stuttgart

Cologne

(S^l Bremen

 

 

 

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 Steim- elhagen 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.

Table 2. Cost inputs to optimization model

 

The costs of the rent for the factory in Poland and the DC in Czech Republic are included in “other costs”. For transport, it is always assumed that each truckload fits 80 bicycles, and trucks travel at a speed of 80 km/h.

Performing experiments

Step 1. Go to NO Experiment and run it with the Demand variation type “95-100%”.

Step 2. Analyze the results using statistics “Optimization Results”, “Flow Details”, “Produc­tion Flows”, “Demand”, and “Overall Stats”:

a)     What is the most profitable SC design?

b)     Is demand for all customers satisfied?

c)     What is the total revenue of the most profitable SC?

d)     What is total profit of the most profitable SC?

e)     Compare the data in statistics “Production Flows” and Table “Demand”. Does the production quantity correspond to the total demand?

f)      Compare the optimal SC design as computed in the NO and the initial SC design (factory and DC in Germany) in terms of profit.

g)     What other costs should be considered when redesigning the SC according to NO results?

h)     What other factors, apart from costs, should be considered when re-designing the SC according to the results of the NO?

Solution:

Step 1. Go to NO Experiment and run it.

NOTE! In order to run the NO experiment, you need to change the units in experiment settings 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”:

a)     What is the most profitable SC design?

See statistics “Optimization Results”: Factory Poland, DC in Czech Republic.

 

b)     Is demand for all customers satisfied?

Yes. See statistics “Demand” and columns “Satisfied” and “Percentage”.

c) What is the total revenue of the most profitable SC? $44,623,075.0; see statistics “Overall Stats”.

d)     What is total profit of the most profitable SC? $26,338,905.352; see statistics “Overall Stats”.

e)     Compare the data in statistics “Production Flows” and table “Demand”. Does the production quantity correspond to the total demand?

Yes. See statistics “Production Flows”.

f)      Compare the optimal SC design as computed in the NO and the initial SC design

(factory and DC in Germany) in terms of profit.

The initial SC profit is shown in Iteration 8, and optimal SC profit is shown in Iteration 1. The

profit can be increased from $10,394,880.062 to $26,338,905.352 (see answer “a”) by changing the SC design as follows:

Choosing the highest net profit path would mean closing the factory in Nuremberg and not considering opening the DC in Steimelhagen.

 

This would mean an increase in net profit for Polarbear which is almost three times higher than the approximation of the as-is scenario. This huge increase is primarily the result of the cost savings possible by renting facilities in Poland and Czech Republic, which have much lower fixed operating costs than is possible in Germany (see Table 2).

g)    What other costs should be considered when redesigning the SC according to NO results?

Opening/closure costs.

h)    What other factors, apart from costs, should be considered when redesigning the SC according to NO results?

Workforce qualification and know-how, disruption risks, future market trends, changes in supplier structures, risks of outsourcing.

The optimization results show that the highest profit can be achieved in the SC design with a DC in Czech Republic and a factory in Poland. However, the negotiations with the factory in Poland revealed another constraint: the Polish factory would only consider annual quantities of each bicycle type within the range of 10,000 units (minimum capacity utilization) and 25,000 units (maximum capacity utilization). Polarbear must now conduction another NO to include this additional capacity constraint.

Performing experiments

Step 1. Go to NO Experiment and run it.

Step 2. Analyze the results using statistics “Optimization Results”, “Flow Details”, “Produc­tion Flows”, “Demand”, and “Overall Stats”:

a)     What is the most profitable SC design considering the capacity constraint of the factory in Poland?

b)      What is the total profit of the most profitable SC?

c)     Compare the optimal SC design with the capacity constraint, as computed in the second NO, and the optimal SC design without the capacity constraint, as computed in the first NO experiment, in terms of profit.

d)     Which differences between solutions in incapacitated and capacitated scenarios can be observed? Explain.

Solution:

a)      What is the most profitable SC design considering the capacity constraint of the factory in Poland?

The optimal SC design now is to have a DC in the Czech Republic and two factories in

 

Germany and Poland.

b)      What is total profit of the most profitable SC?

$19,487,446.413

c)     Compare the optimal SC design with the capacity constraint, as computed in the second NO, and the optimal SC design without the capacity constraint, as computed in the first NO experiment, in terms of profit.

The profit is reduced from $ 26,338,905.352 to $19,487,446.413. The optimal SC design now is now to have a DC in the Czech Republic and two factories in Germany and Poland.

d)     What differences between solutions in incapacitated and capacitated scenarios can be observed? Explain.

 

As the “Flow Details” statistics show, the capacity of the factory in Nuremberg will be used to produce the “tour” and “urban” bicycles for which the total demand is higher than the maximum capacity of the factory in Poland. This increases costs.

 

2.4 Simulation: Dynamic analysis of the Polarbear’s supply chain design

In simulation, we extend our analysis by adding the following features:

-       We transit from flows (as in NO) to orders, i.e., the customer demand is no longer con­sidered an aggregated flow during a period, but it is now generated as orders at certain intervals, e.g., 10 bicycles every day.

-        We introduce inventory control to manage ordering processes.

-       We introduce sourcing policies (e.g., single vs. multiple sourcing) to manage replenish­ment processes.

-        We introduce shipment control (LTL/FTL) to manage shipment processes.

First, we simulate the SC with two factories in Poland and Germany and a DC in the Czech Republic subject to customer demand from Table 1 (see GFA exercise) and the following data (Table 3).

Table 3. Parameters for simulation model

 

For the DC and the factory, three inventory policies have to be developed. We assign the DC and factories a “min-max policy” for all products, where the minimum stock is 50, the maxi­mum stock is 100, and the initial stock is 50 bicycles.

To evaluate the simulation results, we consider six KPIs according to the needs of Polarbear:

(1)     Financial KPIs, such as profit, revenue and costs

(2)  ELT service level by product, which is the ratio of products delivered within the ex­pected lead time to the total ordered quantity

(3)     Demand fulfillment (product backlog)

(4)     Available inventory

(5)     Production capacity utilization and

(6)     Lead time.

With all of the parameters described, we now run the simulation for a period of one year.

Performing experiments

Step 1. Go to Simulation Experiment and run it.

Step 2. Analyze the results using the KPI Dashboard “Revenue, Profit, Costs”, “Lead Time”, “ELT Service Level”, “Production Utilization”, “Demand (Product Backlog)”, and “Average Available Inventory”.

a)     What are the profit, revenue, and costs of the SC?

b)     Is demand for all customers satisfied? Explain.

c)     What is the production utilization of factories in Poland and Germany? Explain why the German factory is utilized to 100% and the factory in Poland is utilized only to 75% even if we clearly see insufficient production quantities and the backlog?

d)     What is your judgment on the inventory dynamics in the SC?

e)     What is your judgment on the lead time?

f)      What suggestions for improvement do you have which could increase profit and customer satisfaction?

Solution:

Step 1. Go to Simulation Experiment and run it.

Step 2. Analyze the results using the KPI Dashboard “Revenue, Profit, Costs”, “Lead Time”, “ELT Service Level”, “Production Utilization”, “Demand (Product Backlog)”, and “Average Available Inventory”.

a)     What are the profit, revenue, and costs of the SC?

 

b)     Is demand for all customers satisfied? Explain.

Not at all. We have a very high backlog and a decreasing service level.

c)     What is the production utilization of factories in Poland and Germany? Explain why the German factory is utilized to 100% and the factory in Poland is utilized only to 75% even if we clearly see insufficient production quantities and the backlog?

100% for the factory in Germany and 75% for the factory in Poland result from the

constraints on production quantities and design capacity defined in Tables “Inven-

tory” and “Production”, respectively:

stupp гч<а

 

d)    What is your judgment on the inventory dynamics in the SC?

Fluctuations with a high amplitude are visible in the diagram.

e)     What is your judgment on the lead time?

It is very long and unequally distributed (e.g., high lead time fluctuations).

f)      What suggestions for improvement do you have which could increase profit and customer satisfaction?

Revenue is very low because customer demand is not fulfilled (see backlog and ELT service level diagrams). Inventory control policy parameters (i.e., reorder point and target inventory) might need to be adjusted. Production capacity utilization is very high, which indicates a bottleneck. Capacity must be increased and aligned with demand, order frequency, and inventory control policy parameters.

Polarbear’s SC managers suggest the following changes to the input parameters (Table 4).

 

 

Table 4. Parameters for simulation model

 

Having changed all parameters, we now run new simulation.

Performing experiments

Step 1. Go to Simulation Experiment and run it.

Step 2. Analyze the results using the KPI Dashboard “Revenue, Profit, Costs”, “Lead Time”, “ELT Service Level”, “Production Utilization”, “Demand (Product Backlog)”, and “Average Available Inventory”.

a)     What are the profit, revenue, and costs of the SC? Did we improve?

b)     Is demand for all customers satisfied? Explain.

c)     What is the production utilization of factories in Poland and Germany? Explain.

d)     What is your judgment on the inventory dynamics in the SC?

e)     Explain how MIN (reorder point) and MAX (target inventory) values have been computed.

f)      What is your judgment on the lead time?

g)     Explain why the changes made improved SC performance.

h)     How can you validate the simulation modelling results using the previous network optimization experiments?

Solution:

Step 1. Go to Simulation Experiment and run it.

REVENUE, TOTAL COST, TRANSPORTATION С... О Ў # Statistics                                  Value                                   Unit filter                                      filter                                       filter 1        Inventory Carrying C...          267,017.995                           U5D 2        Profit                                    16,130,064.777                     USD 3        Inbound Processing...           89,180                                  USD 4        Other Cost                            9,125,000                              USD

LEAD TIME

AVERAGE DAILY AVAILABLE INVENTORY IN P... О D

PRODUCTION UTILIZATION

О 0

ELT SERVICE LEVEL BY PRODUCTS

I 50                  100       150       200 Chart items visible: 1 of 1 DEMAND (PRODUCTS BACKLOG) °--------------- 1---------- 1----------- 1---------- r­ I 50                  100       150       200

 

Step 2. Analyze the results using the KPI Dashboard “Revenue, Profit, Costs”, “Lead Time”, “ELT Service Level”, “Production Utilization”, “Demand (Products Backlog)”, and “Average Available Inventory”.

a)     What are the profit, revenue, and costs of the SC? Did we improve?

 

Before:

Yes, we significantly improved revenues and profits.

b)     Is demand for all customers satisfied? Explain.

Yes. The service level is 100% and there is no backlog.

c)     What is the production utilization of factories in Poland and Germany? Explain. 78%. There is no longer production backlog in the SC.

d)     What is your judgment on the inventory dynamics in the SC?

Stable inventory dynamics.

e)     Explain how MIN (reorder point) and MAX (target inventory) values were com­puted.

s=demand per day * transportation time; S=2*s

f)      What is your judgment on the lead time?

It became constant at a value of two days because there was no backlog or product shortage.

g)     Explain why the changes made improved SC performance.

The adjustment of inventory policy parameters along with increasing production ca­pacity and aligning it with demand, ordering frequency, and inventory policy im­proved the performance of the SC.

h)     How can you validate the simulation modelling results using the network optimiza­tion (NO) experiments?

Profit in the simulation experiment is close to the profit obtained in the network optimization for the same SC design and experimental dataset.

2.5 Comparison experiment

In order to simplify a comparison of two considered simulation scenarios, we run a comparison experiment.

Step 2. Analyze the results using the KPI “Demand (backlog)”, “Profit”, and “ELT service level”.

a)     What are the profit, ELT service level, and order backlog for the two scenarios? Did we improve in the second scenario?

b)     How can you explain the relationship between the Simulation and Comparison ex­periments?

Solution:

Creating a Comparison Experiment

Step 1. Open scenario PB SIM Level 1 and go to Comparison Experiment.

Step 2. Select scenarios PB SIM Level 1_Improved and PB SIM Level 1.

Step 3. Check KPI dashboard. The KPIs for a comparative analysis of the two scenarios should be “Demand (backlog)”, “Profit”, and “ELT Service Level”.

Performing experiments

Step 1. Run Comparison experiment.

Step 2. Analyze the results using the KPIs “Demand (backlog)”, “Profit”, and “ELT service level”.

a)     What is the profit, ELT service level, and order backlog in the two scenarios? Did we improve in the second scenario?

 

Yes, we improved all KPIs.

b)     How can you explain the relationship between the Simulation and Comparison ex­periments?

2.6     Validation using Variation

Rather than running the same simulation multiple times with different parameter values or com­binations, the variation experiment allows multiple variations of the same simulation to be run simultaneously. A variation experiment highlights how KPIs change depending on different parameter values. This kind of sensitivity analysis can also be used to verify the validity of the results of the simulation model.

A variation analysis must be performed to check Polarbear’s simulation model and the results. The daily demand for “urban” bicycles in Cologne can be varied with a minimum demand of 50 and a maximum demand of 170 in steps of 40. The variation should be performed for a period of one year.

Creating a Variation Experiment

Step 1. Open scenario PB SIM Level 1_Improved and go to Variation Experiment.

Step 2. Check the parameter we will vary (i.e., DemandData: bicycle “urban”, Cologne; quantity).

Step 3. Check KPI dashboard. The KPIs for a variation analysis should be “Demand (back­log)”, “Profit”, and “ELT Service Level”.

Performing experiments

Step 1. Run Variation experiment.

Step 2. Analyze the results using the KPIs “Demand (backlog)”, “Profit”, and “ELT Service Level”.

a)     What is the profit, ELT service level, and order backlog for different demands?

b)     Why and how do the KPIs change as demand changes?

c)     Is the simulation model sensitive?

Solution:

Creating a Variation Experiment

Step 1. Open scenario PB SIM Level 1_Improved and go to Variation Experiment.

Step 2. Check the parameter we will vary (i.e., DemandData: bicycle “urban”, Cologne; quantity).

Step 3. Check KPI dashboard. The KPIs for a comparative analysis of the two scenarios should be “Demand (backlog)”, “Profit”, and “ELT Service Level”.

Performing experiments

Step 1. Run Variation experiment.

Step 2. Analyze the results using the KPIs “Demand (backlog)”, “Profit”, and “ELT Service Level”.

a) What is the profit, ELT service level, and order backlog for different demands?

 

b)     Why and how do the KPIs change as demand changes?

Our SC is not well prepared for an increase in demand in Cologne. Due to limited production capacity and selected reorder points and target inventories, the demand increase in Cologne would result in backlog, and significant decreases in service level and profit.

c)     Is the simulation model sensitive?

Yes, it is. Changes in demand have direct impact on service level, profit, and back­log.

2.7    Recommendations

Develop recommendations for Polarbear Bicycle’s management. Consider GFA, NO, SIM, Comparison and Variation results. What SC design would you recommend? Consider the im­pact of inventory control and production capacities. Are there any other factors that should be taken besides the results of the experiments performed?

3. Case study 2

3.1   . Description of Case Study

BERLIN BREWERY is a Berlin beer brand well-known for its small, traditional, handmade beer. Currently, they have one brewery (factory) and one DC in Berlin. Although they have been in business a short time, they already have 50 customers in eight countries. Nevertheless, they face several problems which need to be solved. On the one hand, the German beer market, which is the primary market with the highest sales for BERLIN BREWERY, can be character­ized as a mature market with strong prices and pressure from competition. A sales crisis has been going on for years and competition is intensifying as a result of consolidation processes implemented by international brewing companies. To expand their distribution network and reduce costs by inventory management improvements, a simulation and optimization analysis must be performed to evaluate their current SC performance and develop suggestions for its improvement.

To provide context, an overview of the German and international beer market will be explained, and the relevant technical details of the BERLIN BREWERY and its SC presented. Next the current problem will be described, followed by developments of the GFA, NO, and Simulation alongside the respective results. Finally, recommendations for management need is given based on the computational results.

Beer is an alcoholic and carbonated beverage which is made by fermenting water, malt, and hops. The production of beer can be traced back to the farmers who lived in the 4th millennium BC during the Babylonian and pharaonic times. In Europe, the addition of hop to water only became generally valid in the 14th century. In the early Middle Ages, beer was brewed primar­ily in monasteries. Particularly in northern Germany, the top-fermented brewing industry flour­ished. After the Thirty Years’ War, Bavaria gradually came to the fore as a beer country. To save the purity and tradition of beer, the Bavarian dukes Albrecht IV and William IV regulated the raw materials with strict purity laws. According to these laws, only barley, hops and water could be used to make beer. The purity requirement of 1516 is the oldest, still valid food legis­lation in the world (o.V.2017).

The German brewing tradition enjoys a prestigious reputation worldwide. According to the German Brewers’ Federation (o.V.2017), there were around 5,000 beer brands in Germany at

the beginning of 2015 and over 6,000 in 2017. The trend is rising, although consumption is decreasing.

The market for beer products and the German brewing industry are characterized by strong prices and competition. As mentioned, a sales crisis has been going on for years and the com­petition is intensifying because of consolidation processes implemented by international brew­ing companies.

The SC of a brewery is highly complex. First, essential ingredients (hops, malt, yeast, and wa­ter) and extra ingredients have to be shipped to the brewery. Shipping hops is particularly com­plex as the product is very sensitive: moisture, heat, and oxygen reduce its brewing value over time. As a result, hops must be stored between -2° and +3° Celsius. After the brewing process, the young beer is stored in tanks at a temperature of 1° to 2° Celsius for a length of time between three weeks and three months. In this time, the beer ferments and gets its characteristic colour. In the final steps, the beer is filtered, bottled, and labelled (Plank, 2013). Figure 1 summarizes the logistics process of the craft beer industry.

• Distribution of raw materials

• Research & development • Shipment to warehouse • Mainly trough beverage • Storaging raw materials • Often organised by                           wholesaler • Brewing                                    external company • Packaging

Figure 1: Logistics process of the craft beer industry

 

Warehousing, picking, and loading are carried usually out by company employees, but this depends on size and internal factors for each company. Some companies set up their own ser­vice companies for in-house logistics, but most breweries engage external companies for sorting empties, cleaning empties, and the cleaning of the plant. Compared to other industries, the dis­tribution of beer is diversified as many different channels have to be covered. These include the classic food retail trade, beverage disposal markets, food discounters, petrol stations, and gas­tronomy. However, the importance of different distribution channels has changed in recent years. Food retailers (especially discounters) have gained more importance in the brewing in­dustry, while the importance of gastronomy has declined. In general, all channels receive the beer either directly from the breweries (it’s possible that a third-party-logistics provider takes care of transportation) or through a beverage wholesaler.

3.2    Problem Statement

Currently, the BERLIN BREWERY’s Brewhouse consists of 5 tanks which have a total capac­ity of 20hl. Assuming that demand for its beer is rising, the BERLIN BREWERY has the option to expand its Brewhouse with more tanks. The whole process from the original brewing to the finished product requires between four to six weeks depending on how long each type of beer must be stored. As the location of production in Berlin is small, all beer produced is stored in the only DC in Berlin. An external service provider takes care of all logistical requirements. Currently, most of the beer BERLIN BREWERY sells is sold in Berlin, though the craft beer is also sold to wholesalers all over Germany. Since 2018, customer locations include Switzer­land, Austria, Sweden, Norway, France, Italy, and Spain. BERLIN BREWERY collaborates with three suppliers in Germany who deliver empty beer bottles in crates, as well as the hops and malt. Empty bottles in crates are delivered from a location close to Nuremberg, the hops come from Koblenz, and malt is delivered by a supplier from a location close to Dresden. BER­LIN BREWERY’s current sales’ figures and further financial figures, which are important for the analysis, are summarized in the next step. One-off acquisition costs for the brewing equip­ment is $300,000. Moreover, maintenance costs (including energy and electricity) for the loca­tion of the factory are $80,000 per month or $2,630 per day.

Assuming that one beer crate consists of 20 bottles, the whole cost for one crate is $10. Note that in the anyLogistix model, we consider a product “Beer” which is equivalent to one beer crate. The costs of the product “Beer” can be broken down as follows in Table 1.

Table 1: Bill of materials (BOM), based on information of BERLIN BREWERY

 

Table 1 depicts the BOM (Bill-of-Materials) for the product “Beer” which consists of 1 piece of Hops, 1 piece of Malt and 1 piece of Crate.

Carrying costs, which include warehousing costs, handling inside inventory, and inventory costs, are estimated to be $0.005 per beer crate ($0.2 per pallet) per day. The transportation from the BERLIN BREWERY factory to the DCs are calculated as volume-distance based transport and costs are $0.00175 per km/beer crate ($0.07 per pallet). The transport from the three suppliers to the BERLIN BREWERY factory is paid by suppliers. Inbound and outbound costs are shipment processing costs. Outbound costs at factory are assumed to be $0.66 per beer pallet and inbound costs are $40 per pallet. Hops and malt are delivered in a one-kilogram packaging unit: one pallet of malt or hops equals 40 packaging units.

BERLIN BREWERY wishes to expand its sales and work as efficiently as possible to increase profit. To reach these goals, several problems must be overcome: as mentioned in the first chap­ter, beer consumption in Germany, BERLIN BREWERY’s main market, is decreasing and the market as a whole is highly competitive. The German beer market is a mature, nearly saturated market. Two potential solutions exist: expansion into other countries or increased sales to ex­isting customers. These options instigate further challenges as BERLIN BREWERY has only one DC in Berlin. Long routes and long delivery times to individual customers are the main problems. Because of the long routes, BERLIN BREWERY can respond only relatively inflex­ible to spontaneous requests and a high number of unnecessary routes might be taken. There is also the risk of manmade or natural disruptions which can influence service quality (e.g. a storm destroys DC).

In sum, the goal of BERLIN BREWERY is to expand their distribution network, serve their customers as efficiently and satisfyingly as possible, raise their sales numbers, and increase profit. This is possible by optimizing their SC: an optimal number of DCs as well as good locations for these DCs must be found to save as much logistics costs as possible. Loss of quality and delivery problems should be avoided.

Fig. 2 depicts the current situation. Production is in the centre of Berlin and the raw ingredients are shipped by truck directly from Dresden, Nuremberg, and Koblenz. To store as little as pos­sible, raw materials are delivered on demand and used directly (JIT- just-in-time) for the pro­duction. The beer is delivered to 50 customers all over Europe.

Figure 4: Current network of BERLIN BREWERY

 

To provide a better understanding of the circumstances, a few assumptions are made:

•        All prices and costs are shown and calculated in $.

•        All processes are considered in terms of (beer) crates or pallet specifications, rather than bottles. This is because BERLIN BREWERY sells their beer only in whole crates, and these terms help to simplify the model. In one beer crate there are always 20 bottles of beer, which have 0.33 liters of content per bottle (6.6 liters per crate).

•        1 pallet = 40 beer crates = 800 beer bottles

•        The recycling deposit on bottles is not considered.

•        Transportation costs from the factory to all DCs are the same.

•        Transportation-/ handling costs from the DCs to the customers are adapted to the price

level of the actual country.

•        One year consists of two periods:

o Summer period: 01.05.2021 - 31.10.2021

Demand coefficient: 1.2 (meaning 1.2 times higher demand as in winter due to higher demand during warm months)

o Winter period: 01.11.2021 - 30.04.2022 Demand coefficient: 1.

•        Orders are received every seven days (static demand).

•        Transportation speed is 80km/h, the capacity of a truck is 1,320 crates, which equals 33 pallets which are single stocked.

•        Price per crate for customer: $60.00.

We assume that BERLIN BREWERY will sell 260,405 beer crates within the coming summer period (151,035 crates) and winter period (109,370 crates). Fig. 5 below shows the customers, according to the summer and winter periods. Substantial sales are made in Germany, especially in Berlin (32,807 beer crates per year) and the least sales are achieved in Basel, Switzerland (871 beer crates per year).

  winter demand per year [in beer crates]

  summer demand peryear [in beer crates]

Figure 5: Distribution of sales by country and period, own illustration

 

The main customers are beverage retailers, which purvey to smaller retailers or restaurants. Therefore, it is considered that only one wholesaler is supplied per city. This wholesaler makes resales independently. As a result, no further storage costs are incurred as no further DC is required. Transportation to the wholesalers and handling is currently being handled entirely by a logistics service provider as BERLIN BREWERY does not yet have the necessary capacity and occupancy rate for profitable shipment. This service provider picks up the goods in the brewery, stores them in their own warehouse, and launches directly to the distributor as needed. The current financial performance is presented in Table 2.

Table 2: Current cost structure

 

3.3     Greenfield Analysis (GFA)

Now we conduct a GFA. The aim of this GFA is to determine optimal DC locations in the SC subject to minimum total transportation costs.

Performing experiments

Step 1. Go to GFA Experiment and run it for “Number of sites = 7”.

Step 2. Analyze the results using statistics “Flows” and “New Sites”:

a)     What are the optimal coordinates of the new DCs?

b)     What is the maximum distance from an optimal DC location to a customer?

c)     What are the total costs of the SC?

d)     Compare the data in statistics “Flows” and table “Demand”. Do we satisfy all customer demand from the optimal DC locations?

e)     Which costs, relevant for choosing an optimal DC location, were not considered in this GFA analysis?

Solution:

Step 1. Go to GFA Experiment and run it for “Number of sites = 7”.

Step 2. Analyze the results using statistics “Product Flows” and “New Sites”:

 

a)     What are the optimal coordinates of the new DCs?

 

b)     What is the maximum distance from an optimal DC location to a customer?

533.82 km to Bergen in Norway.

c)     What are the total costs of the SC?

$39,473,586

Note: To compute the sum of costs and flows, slightly drag the heading of the column “Product” in table “Product Flows” in the space over the table.

d)    Compare the data in statistics “Flows” and table “Demand”. Do we satisfy all cus­tomer demand from the optimal DC locations?

Yes, total flows equal total demand.

e)    Which costs, relevant for choosing an optimal DC location, were not considered in this GFA?

Fixed facility costs, inventory holding costs, processing costs.

3.4 Network Optimization (NO)

The NO offers the possibility of optimizing an existing SC according to maximum possible profit. In this case, the solutions of the GFA will be taken into account to optimize the SC. Having checked the suggested GFA sites for DCs, the SC manager of BERLIN BREWERY analysed those locations further regarding additional factors such as availability of warehouses to rent, construction costs for building new warehouses, fixed costs, infrastructure, future de­mand forecasts, etc. As a result, some of the GFA suggested locations are moved (Figure 6).

Figure 6: Alternative DCs locations

 

The NO goal is to find the SC design with the highest possible profit. To define the NO problem from a mathematical perspective, several parameters must be input as data. Each of the DCs has an inventory capacity of min. 5,800 beer crates and max. 11,600 beer crates as well as a one-week inventory range. The brewery can stock 10,000 crates at maximum and should carry an inventory of at least 5,000 crates. Customers and their demands remain the same as in the GFA. To avoid confusion, the DCs are now marked as red icons. Green icons are added to symbolize the suppliers of beer ingredients. These suppliers are located in Nuremberg, Koblenz, and Dresden.

Table 3 contains the costs of the sites. These numbers have been adjusted to the income ratios of each country. Two of them, Berlin and Bochum, are in Germany, and the overall prices in Berlin are cheaper than in western Germany.

Table 3: Cost structure per site

 

Production costs at the brewery are $1.46. Revenue is $60.00.

Performing experiments

Step 1. Go to NO Experiment and run it.

Step 2. Analyze the results using statistics “Optimization Results”, “Flow Details”, “Produc­tion Flows”, “Demand”, and “Overall Stats”:

a)     What is the most profitable SC design?

b)     Is demand for all customers satisfied?

c)     What is total profit of the most profitable SC?

d)     Compare the optimal SC design as computed in the NO and the initial SC design (factory and DC in Germany) in terms of profit.

e)     What other costs should be considered when redesigning the SC according to NO results?

f)      What other factors, apart from costs, should be considered when redesigning the SC according to the results of the NO?

Solution:

Step 1. Go to NO Experiment and run it.

Step 2. Analyze the results using statistics “Optimization Results”, “Flow Details”, “Produc­tion Flows”, “Demand”, and “Overall Stats”:

a)     What is the most profitable SC design?

See statistics “Optimization Results”: a single DC in Berlin is the most profitable option.

b) Is demand for all customers satisfied?

 

Yes, see statistics “Demand” and columns “Satisfied” and “Percentage”.

DEMAND                                                                                                                                                                                                                                                                                                                                                          ^x

 

c)     What is total profit of the most profitable SC?

$12,277,202.35; see statistics “Overall Stats”.

d)    Compare the optimal SC design as computed in the NO and the initial SC design (fac­tory and DC in Germany) in terms of profit.

Following the NO analysis and with all given costs, the results show that having one DC in Berlin is the most profitable SC design (i.e., the optimal SC design is the same as we currently use).

e)     What other costs should be considered when redesigning the SC according to NO re­sults?

Opening/closure costs.

f)      What other factors, apart from costs, should be considered when redesigning the SC according to NO results?

Workforce qualification and know-how, facility disruption risks, future market trends, changes in supplier structures; risks of outsourcing.

Given the threat of DC disruptions and the expansion plans with new customers, BERLIN BREWERY decides to redesign the SC by adding a second DC in Spain, which means that the fifth-best alternative is chosen out of 10 best SC designs displayed in “Optimization results”. The difference in profit between the best and the fifth-best SC design is very small but the SC design with 2 DCs offers more flexibility and resilience.

3.5 Simulation

Simulation can be used in many ways. It promotes understanding of how the SC will react in the event of disruptions, such as outages and/or external influences. To run different simula­tions, the outcome of the NO has been used. In the case of BERLIN BREWERY, a two-month disruption has been simulated to see what happens if only one DC is kept or if a second is rented in Spain. Differences in, for example, the service level and profit could be decisive. For the simulation, assumptions must be made to make the model more realistic. The sourcing policies are defined as follow: for the DCs, the program should choose the closest dynamic source. In this case, there is only one factory and the customers should choose the fastest dynamic source to receive orders as soon as possible.

In simulation, we extend our analysis by adding the following features:

-        We transit from flows (as in NO) to orders. i.e., the customer demand is no more con­sidered as an aggregated flow during a period but it is now generated as orders in certain intervals, e.g., 10 crates per day.

-        We introduce inventory control to manage ordering processes.

-        We introduce sourcing policy (e.g., single vs. multiple sourcing) to manage replenish­ment processes

-        We introduce shipment control (LTL/FTL) to manage shipment processes.

First, we simulate the SC with two DCs in Germany and Spain subject to the following data (Table 4).

Table 4. Parameters for simulation model

 

To evaluate the simulation results, we consider six KPIs according to the needs of BERLIN BREWERY:

(1)    Financial KPIs, such as profit, revenue and costs

(2)  Service level by products, which is calculated as (number of outgoing orders / number of placed orders), where an outgoing order is an order that is not dropped

(3)     Available inventory including backlog at DCs.

With all of the parameters described, we now run the simulations for a period of one year from May 1, 2021 until April 30, 2022.

Performing experiments

Step 1. Go to Simulation Experiment and run it.

Step 2. Analyze the results using the KPI Dashboard “Revenue, Profit, Costs”, “Service Level”, “Average Inventory including Backlog”.

a)    What are the profit, revenue, and costs of the SC? Does this result match with the NO results? Explain.

b)      Is demand for all customers satisfied? Explain.

c)    What is your judgment on the inventory dynamics in the SC? Explain the change in inventory dynamics in the second part of the simulation period.

Solution:

Step 1. Go to Simulation Experiment and run it.

Step 2. KPI Dashboard “Revenue, Profit, Costs”, “ELT Service Level”, “Average Inventory

 

including Backlog”.

a)     What are the profit, revenue and costs of the SC? Does this result match with the NO results? Explain.

PROFIT, TOTAL COST, REVENUE

 

The results of simulation and NO are very close to each other. This confirms the correctness of the simulation model. However, since some parameters have been added in the simulation, e.g., the aggregate flows have been replaced by orders, and aggregate inventory by inventory control policy, the NO and simulation results do have some differences. These differences are caused by differences in methodology: NO is a static method while simulation is a dynamic method and simulates the sce­nario from the beginning of a period until the end of a period. Sometimes an order is already placed and the delivery inroute, but the customer has not yet paid for their order. As a result, less revenue is accrued in the simulation. The simulation calcu­lates the routes exactly and counts them on a daily basis. Despite this, the differences between the two methods are very small. This means that the results are valid and further simulations can be run.

b)     Is demand for all customers satisfied? Explain.

The service level is nearly 100%; however, in the summer period we can see a lower

service level. This indicates that our SC is not capable to totally cope with an in­creased demand in summer. However, the service level in general is very good.

 

c)     What is your judgment on the inventory dynamics in the SC? Explain the change in inventory dynamics in the second part of the simulation period.

 

The inventory behavior is stable: there is no backlog. The second part of the simu­lation period corresponds to a lower demand resulting in less frequent replenish­ments from DCs to factories. The DCs should probably adjust their MIN-MAX pa­rameters in inventory policy in winter according to the lower demand.

Note: the inventory diagram in our example displays inventory at both DCs. You can adjust presentation to a single DC or the total SC inventory (2 DCs + factory)

in the settings:

 

3.6. Risk analysis: Two-month disruption at one of the DCs

This analysis simulates a two-month disruption of the DC in Berlin from August1^st to Septem­ber 30^th for two different network design scenarios, i.e., with two DCs and with a single DC in Berlin.

Performing experiments

Step 1. Go to Simulation Experiment and run it.

Step 2. Analyze the results using the KPI Dashboard “Revenue, Profit, Costs”, “Service Level”, “Average Inventory including Backlog”.

a)     What are the profit, revenue, and costs of the SC for the two different network design scenarios?

b)      Is demand for all customers satisfied? Explain.

c)     What is your judgment on the inventory dynamics in the SC? Explain the change in inventory dynamics in the disruption period.

Solution:

Step 1. Go to Simulation Experiment and run it.

Scenario with 2 DCs:

Scenario with 1 DC:

Step 2. Analyze the results using the KPI Dashboard.

 

a)    What are the profit, revenue and costs of the SC for the two different network design scenarios?

Scenario with 2 DCs:

 

Scenario with 1 DC:

 

We can see that the SC with two DCs is more profitable. In the event of a DC dis­ruption in Berlin, the second DC in Spain can be used as a backup to serve the cus­tomers. As such, the SC with 2 DCs is more resilient.

b)     Is demand for all customers satisfied? Explain.

In the scenario with two DCs, there is a slight decrease in service level. In the sce­nario with a single DC, service level decreases drastically during the disruption. Scenario with two DCs: