AMRI700 Resource Integration Management: Transportation Problem

You are required to select a case study based on the criteria listed below.

1. Identify the industry other than the one selected for the group project

2. Find an organisation other than the ones provided in Table 1.Examples are presented below to elaborate on the viable options for selecting appropriate case study (assessment two) in regards to each industry listed in Table 1.

Permissible RIT issued. It is the best available option to solve the problem and used suitably.

1. Engineering Economics Principles
2. Quantity Discount Model
3. Linear/Integer Programming Models
4. Goal Programming Model
5. Scheduling Problem
6. Assignment Problem
7. Transportation Problem
8. Industrial Ergonomics Principles

Research Questions

Transportation costs form a significant part of the total operational costs of a manufacturer. Finding an optimum solution to the case company is a food manufacturing company which specializes on loafs, groceries, daily products and oils among others. The company operates four bakeries that supply loaf to the Northern Island of New Zealand. The goal of the current analysis is to demonstrate how linear programming can be used to minimize the transportation costs for bread supplied to the Island. To do this, the analysis shall seek to answer the following questions:

RQ1: Does the company have the baking capacity to meet the annual demand for its breads in New Zealand’s Northern Island?

RQ2: If not, how should the company increase its production capacity to meet the demand?

RQ3: If yes, what amount of bread should be transported from each bakery to each demand center to minimize the transportation costs?

Introduction

In today’s increasingly globalized and competitive business environment, cost management is an important determiner of corporate profitability and sustainability. Cost management involves a variety of approaches and strategies designed to lower the total costs incurred in the production of goods and services (Bhimani, 2018). To achieve this, measures must be taken to reduce the costs incurred at each stage in the supply chain. Some of the common supply chain management approaches to minimizing costs include lean manufacturing, scheduling, and inventory management (Oluwagbemiga, Olugbenga, & Zaccheaus, 2014). This report explores one of the most robust and commonly used approaches to minimizing distribution costs, transportation costs and operational costs in general.

Transportation account for a significant cost of doing business for most contemporary businesses. This is particularly important for companies that sour

Goodman Fielder NZ is a subsidiary of the Singapore and Hong Kong owned food processor and wholesaler Goodman Fielder. The company specializes in pastries including loafs and groceries. In New Zealand, the company operates 13 bakeries which supply the country. Six of these bakeries are located in the Northern Island. The six bakeries are located at different parts of the Island and supply the entire Island (Goodman Fielder NZ, 2018). The demand of bread is typically highest in urban centers; in this case 12 cities in the Island (Ibisworld, 2017). The current analysis sought to explore how the company can optimize its transportation network to reduce costs. This will improve the company’s profitability, sustainability and the company’s competitive advantage in the increasingly competitive market.

Industry Background

The New Zealand baking industry recorded $503 million in sales for the 2017 financial year. In the last five years, the industry has recorded a decline in the market size at an average -1.5%. The industry comprises of more than 1240 business. A majority of these business are small scale bakeries including local hot bread shops and family run bakeries (Deloite, 2018). Such businesses mostly bake and sell their products to end consumers at the same premises. However, there is a glowing trend where customers increasingly prefer pre-packaged bread (pastries). Typically, pre-packaged breads are manufactured by the few large plant bakers who distribute them to supermarkets and other types of shops who then retail them to the end consumer (Australia and New Zealand Banking Group, 2001). Goodman Fielder is a large scale baker.

Goodman Fielder Background

Goodman Fielder is a Singapore and Hong Kong owned food processer and wholesaler. The company specializes in the production of based products including loaves, cakes and other pastries, dairy products, oils and other foodstuffs. Unlike smaller bakeries who mostly sell all of their products themselves, Goodman Fielder is a wholesaler. The company sells its products to retailers who then sell to the ultimate consumers (NZ Ministry of Business, Innovation and Employment, 2013).

The company’s business model has a few advantages and disadvantages. First, due to its large scale capacity, the company enjoys the benefits of economies of scale. However, given its supply chain, the final price of the bread includes the retailer’s premium (Duan, 2010). As such, if all production costs are constant, the company’s loaves would sell at higher price than those produced by small scale producers who do not sell via a middle man. The company can compensate on this by exploiting its capacities to minimize other production costs. This will allow competitive pricing and, hence, enhance profitability and competitive advantage.

As mentioned previously, the company operates six production plants in the Northern Island (Goodman Fielder NZ, 2018). Figure 1 below show the locations of the plants. The Puhoi Valley Cheese and Earnst Adams Palmerston North plants specialize in other products other than bread. Consequently, only the other four production plants are considered as bread bakeries in this analysis.

Figure 1: Goodman Fielder Production Plants

The four bakeries supply the entire Island with the company’s bread with demand being concentrated at the major urban centers. For simplicity, demand is considered to originate only in the 12 major Northern Island cities. Figure 2 below shows the locations of these major demand locations. Typically, the company transports the bulk loaves of bread to multiple retailers in the demand centers. In this analysis, the demand is treated as originating from all retailers in a certain city and its environs is treated as originating from a single point in the respective city (Kant, 2014). Typically, the company uses its specialized trucks to deliver the products; and outsource transportation whenever demand exceeds capacity.

Figure 2: Main Bread Demand Locations in New Zealand

Linear Model

Linear modeling is used to find a solution to Goodman Fielder’s transportation problem. The goal is to optimize the transportation network and resources to minimize costs while meeting all of the demand. The company does not provide elaborate data on it operations that is required to conduct the analysis. Consequently, estimations were used to generate the data required.

Data

First demand forecasts are an important input into the transportation planning. First, it is assumed that retailers order the company’s products in truckloads. There is a strict requirement that orders should never be a fraction of a truckload. Estimates of the demand per demand center are shown in table1 below.

Table 1: Fielder Bread Demand

The ability to meet the demand is directly influenced by the capacities of the bakeries. In this case, there are two limiting factors. First, the amount of bread that each bakery can produce is limited to a given value. Next, each bakery has a transportation department with a given capacity to transport the products. This transportation constraint is catered for by limiting the amount of distance that each department can transport the loaves of bread annually. The annual production capacity and the distance capacity estimates used in this analysis are presented in table 4 below.

Table 2: Bread Production Capacity

Next, estimates of the costs and distances involved in transporting the bread from the bakeries to the various demand centers are required to solve the transportation problem. Table five below summarizes the estimated costs for transporting a truckload of loaves for one kilometer and the total distance between the bakeries and the demand centers. Notably, some routes (path from bakery to demand center) are very long such that transportation along these routes is not cost effective under any scenario. Therefore, table five below includes estimates for the routes whose costs are tolerable.

Table 3: Route Information

Objective Function

The objective of the analysis is to minimize the total bread distribution costs incurred by Goodman Fielder given the situation described by the data in the previous section. Here, the transportation costs are a function of the distance which the goods are transported and the number of truck loads transported along each route. The objective function is given by:

TC=TL1*C1+TL2*C2…….

Where TC=Total distribution costs

TLi=the number of truck loads transported along route i

Ci= transportation cost per truck load alongside route i

The goal of the analysis is to find the values of TLi for all of the routes that will ensure that all demand is met at the lowest distribution costs possible (minimize TC) (Ghazali, Majid, & Shazwani, 2012).From the data section, the minimization problem is subject to the following constraints.

Constraints

a) Demand

Failure to deliver all of the loaves demanded is undesirable. This translates into loss of potential earnings, customer trust and market share (Patel, 2017). Therefore, the breads supplied to each demand center must be equal to the demanded amount.

b) Production Capacity

A bakery cannot produce more bread than it has the capacity to do. Therefore, all bread supplied from a given bakery must be equal or less than its production capacity.

c) Transportation Department Capacity

It is assumed that each bakery has a transportation department with a fixed capability. Each transportation department can only transport loaves produced at the bakery where it is based. Therefore, the total distance traveled in distributing bread from a given bakery is constrained by the bakery’s transportation department capacity.

d) Order Size

The transportation costs have a fixed component as well as a variable component which is directly related to distance. Consequently, the transport cost per unit is lowest when trucks carry the maximum capacity. Consequently, it is assumed that no retailers order portions of truck loads; or that any such orders are combined with others to make a truckload. Therefore, each truck that leaves a given bakery carries a full truckload. In the model, this constraint is achieved by a constraint requiring that all TL values should be integers.

TL=Int

e) Nonnegative Truck Loads

Finally, a constraint is put in place to ensure that the solver algorithm does not set negative numbers of truckloads of bread to be delivered. A negative TL is impractical in this case since it would imply that bread is moving from the retailer to the wholesaler. Consequently, each TL value is constrained to positive values using the following constraint:

TL-Absolute (TL) =0

Results

Capacity and Demand

The first research question sought to establish whether the four bakeries have the capacity to meet the current demand. In total, the demand centers require 2630 truckloads of bread per month. The four bakeries have a capacity of producing 2900 truckloads of bread. Consequently, the current production capacity is sufficient to meet the total demand.

The optimal Transportation Solution

Table five below summarizes the optimum solution obtained for the transportation problem. Specifically, the table shows the number of truck loads that should be transported via each route as well as the total costs incurred for using the route and the total distance covered on the route.

Table 4: Optimum Solution

Table six below summarizes the demanded volumes of bread and the volumes that will be supplied under the optimal solution in truckloads. From the table, each of the demand centers will receive the exact amount of read that is demanded. This shows that the demand constraint is met and not demand will be left unsatisfied.

Table 5: Demand Constraint

Finally, table 7 below summarizes the data on the capacity (production and transportation) for each of the bakeries. From the table, it is observed that the optimal solution uses lesser resources than the capacities of the production plants. This implies that no bakery will be overstretched or require any expansion.

Table 6: Capacity Constraint

Limitations

The current analysis has one major limitation. This is the fact that fictitious data is used to test the linear model. The case company does not provide detailed data regarding its demand estimates, capacity and transportation costs. Therefore, the current solution is as accurate as the data estimates are. While the solution provides a suitable illustration of the application of linear modeling to transportation problems, it is not an accurate representation of the situation for Goodman Fielder.

Recommendations

From the foregoing, linear programming can be applied to minimize transportation costs while meeting demand and capacity requirements. In light of this, Goodman Fielder (and companies in the industry in general) should adopt linear programming approach to optimize their transportation. The costs cuts realized should be passed down to consumers through competitive prices (Sulanjaku, 2015). This will ultimately enhance a company’s competitive advantage (Wagner, 2008).

Bibliography

Auckland Regional Coincil. (2010). Industry snapshot for the Auckland region: Food and Beverage Sector. Auckland : Auckland Regional Coincil.

Australia and New Zealand Banking Group. (2001). INDUSTRY BRIEF: ANZ Food and Beverage Industry. Sydney: Australia and New Zealand Banking Group.

Bhimani, A. (2018). Cost management in the digital age. LSE Research Online.

Deloite. (2018). New Zealand’s food story: The Pukekohe Hub. Wellington: Deloite.

Duan, Y. (2010). Buyer–vendor inventory coordination with quantity discount incentive for fixed lifetime product. International Journal of Production Economics, 351-357.

Ghazali, Z., Majid, A. A., & Shazwani, M. (2012). Optimal Solution of Transportation Problem Using Linear Programming: A Case of a Malaysian Trading Company. Journal of Applied Sciences , 2430-2435.

Goodman Fielder NZ. (2012). The 2012 Goodman Fielder Sustainability Report. Wellington: Goodman Fielder NZ.

Goodman Fielder NZ. (2018). Locations. Retrieved from Goodman Fielder NZ: https://goodmanfielder.com/careers/locations/

Ibisworld. (2017). Bakery Product Manaufucturing-New Zealand Market Research. IBISWorld.

Jackson, J. (2015). QUANTITY DISCOUNTS, CAPACITY DECISIONS, AND CHANNEL CHOICES . New York: New York State University.

Kant, G. (2014). Coca-Cola Enterprises Optimizes Vehicle Routes for Ef?cient Product Delivery. Interfaces.

Kostoglou, V. (2018). Quantity Discount Model Cas Studies and Solutions. Retrieved from https://aetos.it.teithe.gr/~vkostogl/files/Educational%20material/Linear%20Programming_case%20studies+solutions.pdf

NZ Ministry of Business, Innovation and Employment. (2013). An Investor's Guide to New Zealand Food & Beverage Industry 2013. Wellington: Ministry of Business, Innovation and Employment.

Oluwagbemiga, O. E., Olugbenga, O. M., & Zaccheaus, S. A. (2014). Cost Management Problem and Firm's performance of Manufucturing Companies. International Journal of Economics and Finance.

Patel, R. G. (2017). Optimal Solution of a Transportation Problem. Global Journal of Pure and Applied Mathematics.

Sulanjaku, M. (2015). STRATEGIC COST MANAGEMENT ACCOUNTING INSTRUMENTS AND THEIR USAGE IN ALBANIAN COMPANIES. European Journal of , Economics and Accountancy.

USDA Foreign Agricultural Service. (2018). New Zealand Food Processing Ingredients Report 2018. Wellington: USDA Foreign Agricultural Service.

Wagner, S. (2008). Cost management practices for supply chain management: an exploratory analysis. International Journal Services and Operations Management.