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MGMT2017 Supply Chain Decision Analytics

Trimester 3A–2023, Singapore

Group Assignment

Format: Business Report

Due date:  (Friday, 19th January 2024, 11:59 PM)

Submission: Online through Turnitin

Marks: %35 marks

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.

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 di↵erent 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 di↵erent 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.

2.2    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-o↵ 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 product-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 are assumed to be $0.66 per beer crate ($26.40 per pallet) and inbound costs are $1 per crate ($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 section, 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 man-made 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 2: Current network of BERLIN BREWERY

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.  3 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).

Figure 3: 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 indepen- dently. 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 ship- ment.  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

2.3 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.

Creating an ALX model

Step 1.  Open scenario BR GFA

Step 2.  Check the tables  Customers, DCs and Factories, Demand, Unit Conversions, and Products.

Performing experiments

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 Site Locations”. Step 3. Go to GFA Experiment and run it for “Number of sites = 2” .

Step 4.   Analyze the results using statistics “Product Flows”and “New Site Locations”. Develop your managerial insights for the results with 7 and 2 DCs in terms of costs and responsiveness.

2.4 Network Optimization (NO)

The NO o↵ers 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 4).

Figure 4: 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. 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.

Figure 5 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.

Figure 5: Cost structure per site

First, we simulate the SC with two DCs in Germany and Spain subject to the following data (Table 3).

Table 3: 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.

Creating an ALX model

Step 1. Open scenario BR SIM

Step  2.   Check data from Table 3 in tables “DCs and Factories”,“Inventory”, “Sourcing”, “Paths”, and“Shipping”. Explain the data in the aforementioned tables. The data in these tables should correspond to Table 3 and Figure 5.

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 by Products”, ”Available Inventory including Backlog”.

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

This analysis simulates a two-month disruption of the DC in Berlin from August 1th to September 30th for two di↵erent network design scenarios, i.e., with two DCs and with a single DC in Berlin.

Creating an ALX model

Step 1. Open scenarios BR SIM Disruption Risk 2 DC and BR SIM Disruption Risk 1 DC. Step 2.  Check data about the disruption in table “Events”.

Performing experiments

Step 1. Go to Simulation Experiment and run it.

Step 2.  Analyze the results using the KPIs “Revenue, Profit, Costs”,“ Service Level by Products”, ”Available Inventory including Backlog”.

2.7 Comparison experiment

In order to simplify comparison of the two simulation scenarios subject to disruption with each other and with the“ideal”scenario (no disruption), we run a Comparison experiment.

Creating a Comparison Experiment

Step 1. Open scenario BR SIM and go to Comparison Experiment.

Step 2. Select scenarios BR SIM, BR SIM Disruption Risk 1 DC and BR SIM Disruption Risk 2 DC.

Step 3. Check KPI dashboard. The KPIs for a comparative analysis of the three scenarios should be “Available Inventory”, “Profit”, “Total Costs”and “Service Level by Products”.