Abstract

Material flow along the supply chain affects overall logistics performance. To optimize

the material flow network with minimized logistics cost without influencing the effective

raw materials and components distribution is an important strategic task for

companies.

In this thesis, consolidation is considered as a solution to address the material flow

optimization, leveraging the capacity of long haul transport to reduce the logistics

cost. Instead of several expensive LCL shipments, a coordinated FCL is much more

cost competitive, concerning the logistics flow from variety of international suppliers to

multiple consignees.

A model focusing on material flow optimization via consolidation is presented in this

study, which suggests appropriate consolidation patterns for shipment. The author

also discusses the application of this model and further analyzes the benefits based

on industry case study.

This thesis consists of five parts. In part I, the author expounds the background and

motivation of this study. In part II, the author gives an introduction of shipping concept

and reviews some relevant literature. Models with assumption and parameters are

presented in part III, as well as solution method. An industry case study and its result

discussion make up as part IV, followed by part V of conclusion.

Scope

Objective

Approach

Key words: material flow optimization, consolidation, LCL, FCL, Break Even Point

(BEP)

1. INTRODUCTION.........................................................................................4

1.1 Background.................................................................................................4

1.1.1 Benefits of logistics outsourcing..................................................................5

1.1.2 Risks of logistics outsourcing......................................................................7

1.1.3 Material flow optimization............................................................................8

1.2 Research question and research purpose................................................10

1.2.1 Problem description..................................................................................10

1.3 Organization of thesis...............................................................................12

2. THEORETICAL FRAMEWORK................................................................13

2.1 Shipping concept.......................................................................................13

2.1.1 Less than container load...........................................................................13

2.1.2 Buyer consolidation...................................................................................13

2.1.3 Multiple consignees...................................................................................13

2.2 Break-even point.......................................................................................13

2.3 Logistic material flow networks.................................................................14

2.4 Literature Review......................................................................................15

3. MODEL DESIGN AND SOLUTION METHODOLOGY..............................18

3.1 Mathematical model..................................................................................18

3.1.1 Assumption...............................................................................................22

3.1.2 Parameters................................................................................................23

3.1.3 Cost functions...........................................................................................25

3.2 Solution Methodology................................................................................31

4. INDUSTRY CASE STUDY........................................................................33

4.1 Company profile........................................................................................33

4.2 Data collection and analysis......................................................................35

4.3 Data input and computation......................................................................35

4.4 Results analysis........................................................................................35

5. IMPLICATION FOR BUSINESS PRACTICE.............................................36

6. CONCLUSION..........................................................................................37

7. LIMITATION AND FUTURE WORK..........................................................38

1. INTRODUCTION

1.1 Background

Logistics is an integral part of any organization and is vital to every economy and

every business entity. In Wikipedia, Logistics is defined as the management of the

flow of goods between the point of origin and the point of destination in order to meet

the requirements of customers or corporations. It involves the integration of

information, transportation, inventory, warehousing, material handling, and packaging,

and often security. Logistics is a channel of the supply chain which adds the value of

time and place utility.

For companies, 10 per cent to 35 per cent of gross sales are logistics cost, depending

on business, geography and weight/value ratio (Nansi, 2007). Dehler (2001, pp. 233-

244) states that logistics performance directly influences the overall firm performance.

As indicated in Figure 1.1, lower logistics costs have a positive direct, and therefore

also total, effect on financial performance. However, increased levels of logistics

services have a significantly stronger total effect since they affect both the

adaptiveness and the market performance of the firm, which in turn both considerably

influence the financial performance.

Figure 1.1 Performance effects of logistics

Source: Dehler, 2001, pp. 233 – 244.

In Singapore, logistics industry is a key enabler of the economy. Despite the global

economic slowdown in 2009, Singapore’s logistics sector has held steady, attracting

some S$481 million in business spending, making up about 9 per cent of Singapore’s

GDP (Source: Government of Singapore).

1.1.1 Benefits of logistics outsourcing

Outsourcing has become a megatrend in many industries, most particularly in logistics

and supply chain management (Feeney et al. 2005). The worldwide trend in

globalization brings fierce competition, which has led many manufacturers,

distributors and retailers to outsource their logistics functions to third party logistics

(3PL) companies, so as to focus on their core competencies and shed tasks

perceived as noncore.

According to the Council of Supply Chain Management Professionals, 3PL is defined

as "a firm [that] provides multiple logistics services for use by customers. Preferably,

these services are integrated, or bundled together, by the provider. Among the

services 3PLs provide are transportation, warehousing, cross-docking, inventory

management, packaging, and freight forwarding." As shown in figure 1.2, a modern

3PL suitable for providing services today exist in abundance, from managing the

receiving, put-away, inventory counting and picking processes, to reacting to the ever

increasing demands of the customers and the subsequently developing markets.

Warehouse Management Order Management Activity Billing

ReceivingOrder Entry (call center,

customer service)Customer Service

Put away Web Storefront Agreements (Contracts)

Replenishment Product Catalogs Rate Change Automation

Kitting Voice Data Access Flexible Accounting

Cycle Counting Online, Web-Based Order Accessorial Charges

Picking/Order ManagementStatus for Clients and

CustomersBilling Reports

Packing 　 　

Reporting 　 　

Inbound Scheduling,

Reporting and Door Control　 　

Transportation

Management

Customer Relationship

ManagementAccounting

Manifest/LabelingCustomer Contact Information

ManagementGeneral Ledger

Rating/Routing Purchasing History Accounts Receivable

Carrier Scheduling Sales Management Accounts Payable

Carrier Settlement Event Management Bank/Cash Management

Dispatch and Equipment

Control

Recall and Hold Processes

and Notification　

Human Resources

ManagementProductivity Management Business Intelligence

Payroll Labor Tracking Inventory Forecasting

Time Management Productivity Measuring ABC Analysis

Regulatory 　 Dead Inventory Alert

Benefit Management 　 Financial Reporting

　 　 Performance Tracking

Figure 1.2: 3PL Functions

Source: Kelvin, 2003, p. 3

Besides concentration on the core competence, another benefit of outsourcing,

usually the most obviously observed, is reduction of the firm’s logistics costs and

application of high technologies. Lower production costs can be achieved through

economies of scale and scope on the ground of larger volumes of similar or equal

logistics services a 3PL produces, and the higher utilization ratio of the assets

employed. Furthermore, many organizations generally use their internal reserves

while providers of outsourcing implement the new technologies; they simply have no

other choice in order to stay on the market (Parashkevova, Vadyba/Management

2007). In this way the use of outsourcing allows the organizations to apply new and

high technologies. Many other advantages like use of the best logistics methods and

experience and increase of competitiveness also facilitate the close partnership to

3PLs. The studies of Cap Gemini Ernst & Young show that the use of 3PL providers

leads to the following changes for the companies:

1. Logistics cost reduction by 8.2%;

2. Fixed logistics asset reduction to 15.6%;

3. The average order cycle length is reduced from 10.7 to 8.4 days;

4. Overall inventories are reduced by 5.3%.

1.1.2 Risks of logistics outsourcing

As discussed in chapter 1.1.1, logistics is as important to an organization as its core

principles for the attainment of maximization of profits and overall growth and

development of the organization. In the past decades, logistics outsourcing has been

employed for many enterprises, especially foreign-funded enterprises and used by

multinational companies. However, the logistics outsourcing has brought many

benefits to the enterprise meanwhile it brings lots of risks. Several articles (Freight

Forwarder, 2010, Tsai et al, 2008) have studied the potential risks of logistics

outsourcing and the conclusions were similar in that, although they are inherently

different according to the individual firms’ perception, the risks mainly lie in the

following areas:

1. Outsourced control deficiencies;

2. Increased reliance on outsourcing risk;

3. Internal staff resistance;

4. Lower user satisfaction;

5. Conflicts in corporate interests.

The risk perception increases as the number of functions outsourced increases (Tsai,

C. et al, 2008). In 2010, Gonzalez revealed a survey among manufacturing and retail

companies, looking into the reasons to non-outsourcing logistics. Of the 102 survey

respondents, 23 per cent were currently not working with a 3PL, stating reasons as

shown in figure 1.3. Data is informative that the reasons companies are not

outsourcing their logistics operations to 3PLs boil down into two high-level categories:

1. Companies believe that they can do a better job than 3PLs in terms of cost,

quality, and service;

2. Companies have not explored the outsourcing option.

Figure 1.3: Reasons Why Companies Are Not Outsourcing to 3PLs

Source: Gonzalez, A., 2010

This is precisely the reason there is a need to implement effective logistics

management in each company so as to get back into the games. Rather than entirely

dependent on third-party logistics service providers and take hidden potential risks,

companies choose to train their own in the strategic and technical logistics parts,

defending the potential risks via coordination and cooperation with 3PLs.

1.1.3 Material flow optimization

Material flow, in its most literal sense, is a systems approach to understanding what

happens to the materials we use from the time a material is extracted, through its

processing and manufacturing, to its ultimate disposition (USGS, 1998). Figure 1.4

provides an overview of the types of materials.

Figure 1.4: Material classification

Source: Adapted from Bringezu and Schultz (1988)

Material flow affects the economy, society, and the environment. As pointed out by the

Organization for Economic Co-operation and Development (OECD) in 2004, the

manner of using and managing resources, from an economic perspective, affects (i)

short-term costs and long-term economic sustainability; (ii) the supply of strategically

important materials; and (iii) the productivity of economic activities and industrial

sectors.

In a supply chain, the material flow is simplified as the network of raw materials,

components work-in-process products, or finished goods distributed through channels

among suppliers, manufacturing centers, warehouses, distribution centers and retail

outlets. However, to most companies, this “simple” material flow is significant enough

to be the physical basis of survival and along which, efficiency is the underlying

“philosophy”.

Material Flow Optimization (MFO) is a quantitative procedure for re- designing the

flow of materials through the supply chain, on the basis of both material and economic

information. It identifies the material flow potential optimizations and maximizes the

transportation throughput for economic activities and asks whether it is sustainable in

terms of overall economical, social, and environmental performance.

Though, optimization of material flow from suppliers to customers makes difference

from a 3PL company that manages the logistics function for its multiple clients to a

company that manages its own logistics function. For 3PL companies, MFO is to

maximize the utilization of its distribution facilities to support the material flows in

supply chains of multiple customers, and deliver superior performance for each client.

A 3PL firm is to balance the need to provide customized solutions to its clients with the

economic benefits of maximizing consolidation in terms of freight and warehouse

capacity, using a single network. For the latter, MFO is about designing a material

network to optimally support its own supply chain at reducing costs while

simultaneously achieving long-term sustainability in logistics operation and

management. Especially for a multinational company who has outsourced its logistics

function to more than dozens of 3PLs, they tend to manage the worldwide material

network itself to avoid problems like peer defense among 3PLs. This could be another

reason for international companies to lead an MFO. As to the customers, when they

are better served with a superior logistics network, their needs are well satisfied, with

not only basic delivery efficiency but also easy access to visibility of the material

flows. In conclusion, no matter who is planning a MFO, it yields economic benefits to

all three parties (supplier, logistics service provider and customer) involved in the

supply chain.

1.2 Research question and research purpose

While work productivity is in main focus of management since more than hundred

years, the resource and of productions lines and value chains in the majority of cases

are still suboptimal. The implementation of MFO offers an enterprise a high potential

for realizing new economic competitive advantages.

1.2.1 Problem description

Consider the international supply chain of an industry company, there are overseas

suppliers, distribution centers, warehouses and manufacturing plants, and along

which chain the materials are transported world widely as per delivery requirement. In

this overseas network, nodes like origin ports and destination ports must be also

taken into the material flow design. The one-way linkage of material flow can be

described as pre-carriage from suppliers to origin port via road transport, main

carriage from origin port to destination port via sea transport, and on-forwarding

carriage from destination port to consignees via road transport again (figure 1.5).

When this company outsources its logistics functions to Logistics Service Providers

(LSPs), LSPs are required to customize the distribution network and transport

strategy, determining the number and location of warehouses and manufacturing

plants, allocation of customer demand points to warehouses, and allocation of

material flows to warehouses, in order to serve customer in some geographical parts

and logistics functions. The traditional setting for each 3PL is to customize the

network for each client. The shipment of materials will go directly from suppliers to

each set of manufacturers, regardless the quantity and volume of shipment or the

distance to consignees, as showed in figure 1.6a. Thus, the material flow network

suffers,

Suppliers capture no economies of scale in long haul sea transport and have to

pay for very expensive less–than-container-load (LCL) transportation;

The 3PLs “waste” the rest of container capacity, not able to full utilize their

facilities;

Difficult access to visibility from the supplier to consignee warehouse;

Higher risk of damage and loses of goods via LCL transport.

Figure 1.5 Material flow networks

By setting up consolidation and deconsolidation hubs at pair origin-destination port,

and coordinating materials from a set of suppliers to this hub nearby the origin port,

shipments are to be consolidated before going to long haul sea transport; the

consolidated shipments are then broke-bulk at a deconsolidation hub nearby

destination port and sent separately to each destination of manufacturing sites (figure

1.6b). Such an optimized material flow network design delivers a win-win-win situation

to all parties involved in the supply chain,

Suppliers win because it is more economic and easier arranged for them to deliver

materials to a nearer location and during the long-distance main carriage part,

expense and risk are to be shared with each other.

3PL wins because with the integrated network, it can better control the delivery

process from the suppliers to consignees. In case of any discrepancy in deliveries,

components can be returned to the suppliers in short term.

Manufacturers win because they are able to manage their inventory more

efficiently with advance supplement information and to better plan their business.

The author considers the material flow network design problem as to set up

appropriate consolidation/ deconsolidation hubs and implement transport schedule

along the pair links among suppliers, ports and consignee warehouses, so as to

optimize a trade-off between the service level to customers and total logistics cost

while moving materials during time and over space. This paper is therefore to present

a deterministic model of material flow network optimization where transportation and

inventory problems are solved in an integrated way,

1. Determining the shipment size on each link;

2. Defining the flow allocation along a supply chain: from shipper to port, port to port

and port to consignees, respectively, which indicates the openness of hubs

nearby ports;

3. Choosing right type of transport service for main carriage, when a shipment is

ready at the origin port.

Figure 1.6a b

Designing such an optimized material flow network, which links multiple shippers to

multiple consignees, requires coordination of many interlinked aspects of the

distribution system. The reliability and effectiveness of the supply chain must be

maintained at a satisfied level, which is to make sure that the right frequency of

shipment departures from each suppliers; the right quantity of materials are shipped;

the right pair of origin-destination port is chosen to bridge the suppliers and their

consignees; the right number and location of hub are set up; and the right type of sea-

freight service is used for main carriage.

1.3 Organization of thesis

This thesis consists of five parts. In part I, the author expounds the background and

motivation of this study. In part II, the author gives an introduction of shipping concept

and reviews some literature on this topic. Mathematical model with assumptions and

parameters are presented in part III, as well as corresponding solution methodology.

An industry case study and its result discussion make up as part IV, followed by part

V of conclusion and future study suggestion.

2. THEORETICAL FRAMEWORK

2.1 Shipping concept

2.1.1 Less than container load

2.1.2 Buyer consolidation

2.1.3 Multiple consignees

2.2 Break-even point

In economics and business, specifically cost accounting, the break-even point (BEP)

is the point at which the total cost or expenses and sales revenue are equal (see

figure 2.1): there is no net loss or gain, and one has "broken even" (Wikipedia, 2012).

Figure 2.1

In the linear Cost-Volume-Profit model, the break-even point (in terms of Unit Sales

(X)) can be directly computed in terms of Total Revenue (TR) and Total Costs (TC) as:

TR=TC

P*X=TFC+V*X

P*X-V*X=TFC

(P-V)*X=TFC

X=

Where:

TFC is Total Fixed Costs;

P is Unit Sale Price; and

V is Unit Variable Cost.

A simple example of BEP calculation is presented as following.

Sales price per unit = $250

Variable cost per unit = $150

Total fixed expenses = $35,000

Calculation:

X = $35,000 / ($250-$150)

X = 350 Units

Therefore, the break even point is 350 units, in other words, after selling 350 unit

products only does the business start making net profit.

The main advantage of BEP analysis is that it explains the relationship between cost,

production, volume and returns, and it indicates the lowest amount of business activity

necessary to prevent losses (see Accounting for management, 2012).

In this paper, the author brings the BEP concept into the decision of transport service

type for main carriage. Shipping cargos via sea transport is either via LCL or FCL,

while the former is usually for lower volume and only the cargo size reaches a certain

point will it be economic to use FCL service (see figure 2.2). Given the unit price for

shipping each unit of cargo via LCL and the fixed FCL shipping cost of per container,

the BEP is easy to be compute as,

X= Fixed cost of per FCL shipment/ Variable unit cost of LCL shipment

Transport service is usually contracted with LSP and distribution rates are defined

under different service types, which ranges from LCL/LCL, LCL/FCL 20’, LCL/FCL 40’

to FCL/FCL 20’, FCL/FCL 40’. Figure 2.2 is an example that for international multi-

mode transport, rates are specified into pre, main and on-forwarding carriage level,

including other charges like BAF and custom clearance fee.P

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HANGZHOU HONG KONG LCL/LCL 260.00 CNY 165.00 CNY - USD 2.00 USD 198.00 HKD 205.00 HKDHANGZHOU HONG KONG LCL/FCL 20' 260.00 CNY 165.00 CNY 87.39 USD 11.66 USD 2,025.00 HKD 1,100.00 HKDHANGZHOU HONG KONG LCL/FCL 40' 260.00 CNY 165.00 CNY 113.00 USD 34.32 USD 2,875.00 HKD 1,225.00 HKDHANGZHOU HONG KONG FCL/FCL 20' 2,490.00 CNY 1,300.00 CNY 87.39 USD 9.94 USD 2,345.00 HKD 1,100.00 HKDHANGZHOU HONG KONG FCL/FCL 40' 3,400.00 CNY 2,200.00 CNY 113.00 USD 30.88 USD 3,085.00 HKD 1,225.00 HKD

Figure 2.3

Therefore, BEP is calculated as following,

BEP 20’ =

BEP 40’=

In other words, a more cost friendly shipping method will be FCL 20’ rather than LCL if

the cargo is more than 9.31 units. Similarly, when cargo is over 12.54 units, it should

be shipped via FCL 40’.

2.3 Logistic material flow networks

Logistic material flow can be defined as the study of planning, implementation and

control of the movement and positioning of people and/ or goods and the associated

supporting activities in order to optimize a trade-off between the service level to

customers and the total cost. A modern view of logistics is characterized by the global

consideration of material flows, which are apparent through movement and storage,

plus information and value flows.

This paper refers to Daskin (1985), Golden and Baker (1985), Hall (1985) and Sheffi

(1985) for an overview of logistic problems with indication of integrated research. The

author also briefly summarizes the most important characteristics of logistic material

flow networks concerned with the modeling of the systems (refer to Fleischmann,

1993 and Slats et al. 1995).

There are five basic types of networks,

The single link case – is the simplest network, composed of two nodes, origin and

destination only. It models several practical situations and represents the building

block for the analysis of more complex networks when the dimension of the

network does not make it possible a global optimization. In these cases, the

network can be decomposed into sub networks, then each sub network is

optimized independently and finally the network is improved by means of local

search techniques.

The sequence of links case – is composed of one origin, one destination and one

or several intermediate nodes, all of which each product must be shipped though.

Materials are shipped via different transport mode on different links between

depots, as serial systems in the framework of production system (Muckstadt and

Roundy, 1993). A typical example is that an overseas shipment is shipped first

from producer to port by truck, and then goes via sea freight to destination port,

afterwards to consignee by truck again.

The one origin-multiple destinations case – represents the typical distribution

system in which a set of materials are supplied at the origin point and demanded

by several destinations. Bertazzi and Speranza (2000) suggested two shipping

strategies for this case: direct shipping, which means there are only one origin

and one destination in each shipment and involved no routing problem; or

peddling, in which each journey can touch more than one destination and

therefore the optimization issue is brought in.

The multiple origins-one destination case – this network is symmetric to the one

origin-multiple destinations case, while it represents the typical material

management system (destination requires diverse materials from multiple origins).

Similar solutions from above can be adapted.

The multiple origins-multiple destinations case – is the most complex material

network, which is typically used by 3PL trucking companies that collect goods

from a set of depots then distribute them to several depots.

In this thesis, the author focuses on a multi-cases material flow network. This network

is composed of the multiple origins-one destination case, the single link case and the

one origin-multiple destinations case.

2.4 Literature Review

Since the mid-1990s, analysis and optimization of material flows through regional

economies, up to level of economic regions such as the European Union and down to

individual firm level have been addressed under the topic of material flow

optimization. Although the first example of integration between inventory and

transportation costs was published in Harris (1913) (see Erlenkotter, 1990, for

comments and curiosities), integrated logistics systems have been intensively studied

only recently. A survey of the results obtained on dynamic routing and inventory

problems, based on the dichotomy frequency domain/ time domain, can be found in

baita et al. (1996), where stochastic models are also discussed. In this paper, the

author reviews some deterministic integrated transportation-inventory models in

material flow networks, with the aim to understand the different approaches adopted

in continuous time models.

One critical aspect in mathematical programming models to design such a network is

the coordination of the inventory replenishments from the suppliers to the hubs, and

from the warehouses to the manufacturers. The author brings in two trades-off: the

inventory holding costs incurred at the warehouses, to trade-off with the network

shipping cost; and cost-saving in long haul FCL shipment, to trade-off the possibility of

increase in short-distance LCL by road. In view of all the related issues, we shall refer

to previous literature which covers the four aspects as follows:

a) EOQ(Economic Order Quantity)-based material flow network design

b) Material flow network design with hubs

c) Network design with integration of inventory and transportation

d) Network design with concave shipping costs

e) Coordination of production and shipping lot sizes

f) Mathematical programming material flow network optimization models

a) EOQ-based material flow network design

EOQ is the order quantity that minimizes total inventory holding costs and ordering

costs. It is one of the oldest classical production scheduling models (Wikipedia, 2012).

A large number of network design models have been based on the EOQ model, in

which the main common assumptions are:

Single product: only one product is considered;

Steady state and equilibrium: product is offered at origins and absorbed at

destinations at given constant rates, such that the sum over the origins of the

production rates is equal to the sum over the destinations of the consumption

rates;

Single and continuous shipping frequency: exactly one shipping frequency is

selected on each link to ship all the materials;

In Blumenfeld et al. (1985) an EOQ-based model is applied to the single link case, the

one origin-multiple destinations case, the multiple origins-one destination case, the

multiple origins-multiple destinations case and to more complex networks as well. In

1989, Erlenkoter formulated a single link case model and defined the optimal time

between two consecutive shipments by,

Where,

h=the inventory cost in the time unit

q=production and consumption rate of the product at the origin and at the destination

v=the unit volume

c=transportation cast per journey

r=the transportation capacity of each vehicle

For the multiple origins-one destination case and its symmetric case of one origin-

several destinations case are studied based on EOQ-model. In Burns et al. (1985)

and in Daganzo (1996), the guidelines are given, although they did not proposed

exact methods for these networks. Some approximations are made on the data, for

instance, the methodology is based on the destination density instead of their exact

locations. The author solves the peddling routing problem on the basis of the concept

of “delivery region” (a delivery region is the set of destinations that one vehicle has to

visit during one journey and its size is given by the number of destinations that belong

to it): First determine the size of the delivery regions; then determine the destinations

which belong to each region; finally, send to each region a full load vehicle on the

minimum distance route. Hall (1985) shows that if the shipping frequency differs

among the nodes, then the total cost can be reduced; based on the EOQ model, Hall

further proves that the optimal shipping frequency is a discontinuous function of the

production rate, and that the optimal transportation mode depends on the production

rate.

The more complex case with peddling is analyzed in Burns et al. (1985) and Daganzo

(1996). The multiple origins-multiple destinations network with the assumption of

independent shipments to the consolidation node and from the consolidation node is

solved by simply optimizing separately each link.

b) Material flow network design with hubs

Hubs are transshipment facilities that allow the construction of a network where large

numbers of direct connections between nodes (including suppliers, warehouses and

customer locations) can be replaced with fewer, indirect connections. In solving hub

location problems, two distinct questions need to be resolved: finding the best location

for the hubs, and identifying the best route for flow of materials from the origin nodes

to the destination nodes via the hubs.

One of the earliest works in hub location is by O’Kelly (1986) who demonstrated that

the one hub location problem is equivalent to the Weber least cost location model; he

also discusses the two hub location in a plane. For the location of two interacting

hubs, the flows between the hubs are an endogenous function of their relative

location, and a gravity model is linked into the objective to allow for complete

interdependence between the interaction and hub location.

c) Network design with integration of inventory and transportation

There are two indicative streams of the interaction of location and inventory while

designing distribution systems. The first stream of research addresses issues related

to allocating inventory across multiple locations in a distribution system. Eppen (1979)

shows the benefit of centralizing (or pooling) inventory in a multi-location newsvendor

problem. Later in 1981, Eppen et al. examine a system consisting of a central

distribution center that holds no inventory but must allocate inventory to several

retailers using a multi-period newsvendor framework. Schwarz (1981) presents the

benefit of pooling inventory in a multi-location EOQ framework. Since hub investments

increase as the number of hub increases in a multi-location EOQ model, Meller (1995)

provides the increase in demand that is required to offset these increased costs. The

second stream of research examines the issues relating to determining the number

and location of hubs in order to minimize the costs related to transportation and

operating the hubs. The most basic form of this problem is known as the warehouse

location problem, the location allocation problem, or the generalized Weber problem.

Efroymson and Ray (1966) consider the problem of determining the number and

location of hubs in order to minimize the hub fixed costs and the transportation costs

associated with serving a set of discretely located customers. Soland (1974)

considers a similar problem, but also includes concave production/distribution costs.

Sherali and Adams include location-specific production costs in 1984. A more

thorough review of work on this problem can be found in Brandeau and Chiu (1989),

where they discuss the difficulty of these NP-hard (non-deterministic polynomial-time

hard) problems.

The author then reviews some work on related strategic production/distribution

models, in which the demands at a set of specified locations are assumed fixed and

known with certainty. Most of the formulations focus on a mixed integer programming

(MIP) representation, which generally include integer variables for locating plants

and/or hubs in given zones and allocating customers to hubs, and continuous

variables for determining flows of materials through the distribution system. Examples

of such models are described by Geoffrion and Graves (1974), Robinson (1989), Gao

and Robinson (1992), and Arntzen et al. (1995).

A notable attempt to include demand uncertainty and safety stocks is the work by

Cole (1995). He presents a formulation that includes a Normal distribution of demand,

and focuses on the safety stocks required for maintaining a specified level of

customer service, along with decisions on DC location and customer allocation. Cole's

model is represented as a capacitated fixed-charge multi-commodity network flow

model with side constraints. The side constraints are the nonlinear inventory service

level constraints resulting from the assumption of normal-distributed demands. He

suggests two solution procedures, and examines three example problems. The

largest problem has four products, nine customers, three potential plant locations, and

six potential warehouse locations. The model has about 2300 constraints and 2900

variables (of which about 900 are integers). He illustrates that as the customer service

level increases, the effort required to solve the model increases sharply. Unfortunately

his solution procedure is impractical for most problems of realistic size.

Masters (1993) has illustrated a very effective technique for determining safety stocks

for various products in a multi-echelon distribution system. He uses a model for

inventory based on Palin's Theorem (see Feeney and Sherbrooke, 1966), but his

analysis does not consider location decisions for the hubs. We have adopted a very

similar approach to modeling inventories at the hubs, but linked this to a facility

location formulation.

d) Mathematical programming material flow network optimization models

The multiple origins-multiple destinations case is considered in Klincewicz (1990) for

the case of multiple products. The problem is to decide for each origin-destination pair

the quantity of each product to ship directly and the quantity to ship through a

consolidation node.

One of the focuses in this paper is on managing the suppliers for more than one

consignee, each of who places purchase orders frequently, as per their customers’

demand. Such a logistics arrangement is similar to a supply hub (figure 2.1), which

was brought up by Barnes et al. in 2003.

In the multiple assignment hub location model (Campbell, 1994), each interacting pair

is allowed to utilize the hub that will result in the lowest travel costs for a particular

origin to destination path, independent of how this flow helps to produce a large

bundle of interaction.

One of the earliest works in hub location is by O’Kelly (1986) who demonstrated that

the one hub location problem is equivalent to the Weber least cost location model.

In many conventional logistics problems, we expect to see the shortest path emerge

as an ideal candidate for shipments between origins and destinations. In material flow

optimization systems, however, determination of the optimal routing for any particular

origin-destination pair is a complex question, which is sensitive to the allowable

connections between nodes.

3. MODEL DESIGN AND SOLUTION METHODOLOGY

In particular, the strategic design of the material flow network is of crucial importance.

It deeply impacts the supply chain planning and eventually the performance of the

company. Our aim is to design the optimized material flow network at a strategic

decision level.

3.1 Mathematical model

In this thesis, the author is focusing on a four-layer, multi-shipper multi-consignee

network. The network elements are namely, suppliers , origin ports , destination ports

and consignee warehouses , (in this network, a port is with a hub for consolidation

or deconsolidation function, in other word, a consolidation hub is to be built nearby the

chosen origin port , and a deconsolidation hub is to be built nearby the chosen

destination hub). The suppliers will supply to the warehouses , which will in turn

replenish the manufacturing plants on a regular basis to support Just-in-time (JIT) or

Make-to-order (MTO) production or assembly process, via ports and . Each

warehouse is dedicated to serve its own manufacturing plant only. The model

integrates three decisions: flow allocation, shipment sizes and type of shipping service

on each link.

Given the daily demand of final product and the bill of material (BOM) relating the

supplier components to the final product, the manufacturer must place the right

amounts of components from each supplier, taking into account the shipping

frequency from the origin port, the shipping time from suppliers to their assigned port,

from port to port and from the deconsolidation hub to the warehouses. In this model,

only inventory holding at the warehouse is considered and the hubs function as cross-

docks which do not hold inventory.

The author associates each pair of origin port and destination port with a set of

shipping options and each pair can have a different amount of shipping options.

Each shipping option is defined with,

Shipping frequency, (days between shipment)

Shipping capacity of main carriage from consolidation hub/ origin port to

deconsolidation hub/ destination port , (number of shipping units, where

each shipping unit is 1 cube meter)

Annual shipping cost and shipping time of pre-carriage from supplier to

consolidation hub/ origin port , ($) and (days)

Annual shipping cost of main carriage from consolidation hub/ origin port to

deconsolidation hub/ destination port , ($)

Annual shipping cost and shipping time of on forwarding from deconsolidation hub/

destination port to consignee warehouse , ($) and (days)

For each shipping option at each pair of port and , if supplier is assigned to

port and main carriage is directed to port , the shipping lot size for supplier and

receiving shipment size for consignee are defined as and respectively (in

terms of shipping capacity required).

An illustrative example of the application of shipping option is showed in figure 3.1, in

which we have two suppliers ( =1 and =2) assigned to one origin port (with

consolidation service), and consolidated shipment is directed to one destination port

(with deconsolidation service), and to two warehouses ( =1 and =2). The two

warehouses share same suppliers, in

other words, either of the suppliers is serving two warehouses, and either of the

warehouses is receiving from both suppliers. Port has two available shipping

options to port , and the analysis selected shipping option 1 (m = 1),

The consolidation hub/ origin port will ship to the warehouses every 7 days (

= 7 days) and suppliers ( =1 and =2) will also ship to consolidation hub/ origin

port every 7 days;

The shipping capacity from hub to hub ( ) is sum of capacity from to (

and for warehouse 1 and 2 respectively); also, it equals to sum of

shipping lot size from supplier ( and for supplier 1 and 2);

The annual cost for shipping of pre-carriage from supplier to consolidation hub/

origin port ( ) is and , for supplier 1 and 2 respectively;

The shipping time of pre-carriage from supplier to consolidation hub/ origin port

( ) is and , for supplier 1 and 2 respectively;

The annual cost of main carriage for shipping from consolidation hub/ origin port j

to deconsolidation hub/ destination port k,

The annual cost for shipping of on forwarding from deconsolidation hub/

destination port k to consignee warehouse ( ) is and , for

warehouse 1 and 2 respectively;

The shipping time of on forwarding from deconsolidation hub/ destination port k to

consignee warehouse ( ) is and , for warehouse 1 and 2

respectively.

Total annual shipping cost for the material flow network is sum of ,

and

Total shipping lead time for shipment from supplier to consignee warehouse

is sum of , and

Figure 3.1

The model costs consist of the transport costs and transshipment/warehousing costs

along the material flows. In order to assess costs which reflect reality and the relation

to the cost-originators at best, mostly process cost models are applied. The network

cost (Figure 3.2) includes,

Annual transport cost of pre-carriage from supplier to consolidation hub/ origin

port j with shipping option m, , including handling charges at suppler and

any other variable costs;

Annual transport cost of main carriage for shipping from consolidation hub/ origin

port j to deconsolidation hub/ destination port k with shipping option m, ,

including handling charges at hub and any other variable costs;

Annual transport cost of on forwarding from deconsolidation hub/ destination port k

to consignee warehouse with shipping option m, ($), including handling

surcharge at hub k and any other variable costs;

Fixed annual cost of hub with shipping option m, ($);

Fixed annual cost of hub k with shipping option m, ($);

Annual inventory holding cost at warehouse , attributable to components from

supplier , who using hub and k with shipping option m, ($);

Annual custom clearance fee for shipping from hub j to hub k with shipping option

m, ($).

Figure 3.2: Cost Components for the Consolidated Network Design

3.1.1 Assumption

Economic order quantity (EOQ) inventory – the demand rate at warehouse is

assumed to be constant over the year and each new order is delivered in full when

inventory reaches zero. The inventory review system is assumed to be continuous

and shipments of items, of a given size, are shipped instantaneously, in other

words, the transportation time is negligible compared to the holding time at the

consignee warehouses.

Perfect coordination – shipments of items are only sent to a partner when the

latter’s inventory is empty, and flows are balanced, i.e. the total shipment amount

equals the total demand.

Each supplier supplies only one type of material or component (i.e. supplier

supplies only component ). If the supplier supplies more than one type of

component, dummy suppliers for each component type are created.

Each supplier can supply to more than one warehouse. This assumption is in line

with the concepts of multiple consignee consolidation and concave shipping cost.

A supplier can consolidate the total quantity of components required by more than

one consignee and ship via its assigned main carriage flow, to take advantage of

concave shipping cost. If each supplier only supplies to one consignee

warehouse, benefits of such consolidation will not be fully realized.

Each consignee warehouse is demanding components from more than one

supplier. This assumption is in line with the concept of buyer consolidation.

Consolidated materials from several suppliers are sorted according to each

consignee orders after main carriage, only materials ordered by consignee are

shipped to warehouse . If each consignee only demands from one suppler,

benefits of such consolidation will not be fully realized.

Each supplier is assigned to exactly one consolidation hub and shipment from this

hub is linked to exactly one destination port, with no direct supplier or

consolidation hub to warehouse link is allowed. If the shipment at origin port

needs to be shipped to more than one destination port, dummy origin ports for

each destination port are created. This assumption will reduce the shipping cost

involved, by combining all potential consolidation among single LCL shipments,

since it is more economical to ship a larger quantity to a single hub, then to

distribute smaller quantities to multiple consignees.

A consolidation or deconsolidation hub is to be set up close enough to each

chosen origin or destination port, and that transport effort between port and hub

can be neglected.

Each hub acts as a cross-docking facility and holds no inventory. Without loss of

generality, no inventory holding cost at hubs is considered in this model. We use

the shipping frequency for a selected shipping option to coordinate the shipping

lot sizes from the suppliers and the lot sizes required by the warehouses. This

assumption is in line with perfect coordination concept.

Each hub can serve more than one warehouse. This assumption allows the hub

to ship sorted shipment directly to multiple consignee warehouses.

No shipment capacity limitation for main carriage. This assumption simplifies the

shipment size decision and allows the full consideration of influence of shipping

option on shipping lot size decision.

At most one shipping option is chosen for each pair of port and . With a single

shipping option, all the components from suppliers assigned to the hub j are to be

consolidated for shipping. Also, we can coordinate the shipping lot sizes from the

suppliers and the lot sizes required by the consignees.

For a selected shipping option for hub and , if supplier is assigned to hub

, then supplier must also ship to hub using the same shipping option .

These assumptions are reasonable as this paper focuses on strategic decision level.

EOQ policy has been shown to be quite robust and valid at the strategic decision level

(Nahmias, 2009). Assuming perfect coordination in general is an approximation as

specific proportionality between the demand rate and supply rate is required for

perfect coordination to be achievable. Perfect coordination simplifies the flow balance

issue, which is not relevant at the strategic level. Similarly, other assumptions are for

purpose of generalization and simplification, and will not influence the effectiveness of

model at strategic level.

3.1.2 Parameters

The input parameters include,

, , = indices for suppliers, consolidation hubs/ origin ports, destination ports/

deconsolidation hubs respectively

= index for manufacturing plants as well as their dedicated warehouses

= index for the available shipping options for each pair of hub and

= daily production or assembly rate of final product at manufacturing plants

= demand of component to produce or assemble one unit of final product at

warehouse , which refers to BOM of the final product (number of units)

= shipping capacity required per unit of component (number of shipping units,

where each shipping unit is 1 m3)

= weight per unit of component (number of shipping units, where each

shipping unit is 1 KG)

= inventory holding cost of component at warehouse ($ per unit of

component per year)

= handling cost per unit of component at hub j ($)

= handling cost per unit of component at hub ($)

= custom clearance fee per shipment at port ($)

= shipping time of pre-carriage from supplier to consolidation hub/ origin

port with shipping option , including shipment lead time (days)

= shipping time of main carriage from consolidation hub/ origin port to

deconsolidation hub/ destination port , including shipment lead time (days)

= shipping time of on forwarding from deconsolidation hub/ destination port

to consignee warehouse with shipping option , including shipment lead time

(days)

= shipping frequency for consolidation hub/ origin port to deconsolidation

hub/ destination port , with shipping option (days between shipment)

= number of shipments per year from consolidation hub/ origin port to

deconsolidation hub/ destination port under shipping option

= shipping lot size for supplier of pre-carriage with shipping option

(number of shipping units, where each shipping unit is 1 cube meter)

= shipping lot size receiving for consignee from on forwarding carriage with

shipping option (number of shipping units, where each shipping unit is 1 cube

meter)

= shipping capacity of main carriage from consolidation hub/ origin port to

deconsolidation hub/ destination port (number of shipping units, where each

shipping unit is 1 cube meter)

= annual fixed investment of hub with shipping option ($)

= annual fixed investment of hub with shipping option ($)

= annual shipping cost of pre-carriage from supplier to consolidation hub/

origin port with shipping option ($)

= annual shipping cost of main carriage from consolidation hub/ origin port

to deconsolidation hub/ destination port with shipping option ($), including

port origin and destination surcharge and BAF.

= annual shipping cost of on forwarding carriage from deconsolidation hub/

destination port to consignee warehouse ($)

= annual inventory holding cost at warehouse , attributable to components

from supplier , who uses pair link of consolidation hub/ origin port and

destination port/ deconsolidation hub with shipping option ($)

= annual custom clearance fee at destination port for shipping from hub

to hub with shipping option ($)

3.1.3 Cost functions

Before formulate the completed model, we can pre-calculate parameters , ,

, as following.

a) For a selected shipping option for hub to hub , if supplier is assigned to

hub , = (shipping cost from to ) * (number of shipments per year), thus,

= [( * ) + ] *

Where,

= Shipment lot size from supplier , measured in units of shipping

capacity. Given the average daily demand of component at warehouse (

), shipping frequency in days ( ), and shipping capacity required per

unit of component ( ),

= Applicable shipping rate from to , for shipping option m and

shipment capacity , usually equal to LCL rate.

= Fixed logistics cost incurred from to for shipping option m

= Number of shipment per year for hub and , with selected shipping

option m, = Int (365 / )

b) Similarly,

Where,

= Shipping size of main carriage from consolidation hub/ origin port to

deconsolidation hub/ destination port in units of shipping capacity.

= Applicable shipping rate from consolidation hub/ origin port to

deconsolidation hub/ destination port , for shipping option m and shipment

capacity

= Fixed logistics cost incurred from consolidation hub/ origin port to

deconsolidation hub/ destination port for shipping option m

= Number of shipment per year for - pair origin-destination port, with

selected shipping option m, = Int (365 / )

After consolidation, shipment size at hub is consolidated and incurred

from hub to hub , depending on selected shipping service type and container

size. varies from LCL, FCL 20’ to FCL 40’. BEP is decisive to the

determination of change from LCL to FCL, as discussed in Chapter 2. Following is

an example that illustrates the relationship between shipment features and

shipping service decision. If a consolidated shipment of weight and volume is

ready at hub for long haul sea transport to hub , given the rate card we can

easily calculate the BEP for 20’ and 40’ for this shipping lane using the method

mentioned in Chapter 2, as shown figure 3.3.

BEP20' 11.26 BEP40' 17.45Max Volume20' 28 Max Volume40' 56Max Weight20' 24 Max Weight40' 30.48

Max Weight/Volume 20' 0.86 Weight/Volume 40' 0.54

BEP Calculation

Figure 3.3

Therefore, when < 30.48 && < 56 is true, figure 3.4 is applicable for this

shipping service decision: i.e. if = 7 and = 12, according to figure 3.4, the most

economic way for this shipment from consolidation hub/ origin port to

deconsolidation hub/ destination port is to go via FCL as 1x20’ container. Total

cost saving in this shipment will be EUR34 as shown in figure 3.5.

w

Max W 40'3 3 3 3

Max W 20'2 2 2 3

BEP 40'2 2 2 3

BEP 20'1 2 2 3

0 BEP 20' BEP 40' Max V 20' Max V 40' v

1 LCL Shipping Service = Max(v,w)2 FCL Shipping Service = 1x20'3 FCL Shipping Service = 1x40'

Figure 3.4

Cost Saving in Euro

Saving 34

Unconsolidated Cost 1,432

Consolidated Cost 1,398

Figure 3.5

c)

Where,

= Shipping lot size from consolidation hub/ origin port to deconsolidation

hub/ destination port in units of shipping capacity. Given the average daily

demand of component at warehouse ( ), shipping frequency in days (

), and shipping capacity required per unit of component ( ),

= Applicable shipping rate from deconsolidation hub/ destination port

to warehouse , for shipping option m and shipment capacity , usually

equal to LCL rate.

= Fixed logistics cost incurred from deconsolidation hub/ destination port

to for shipping option m

= Number of shipment per year or - pair origin-destination port, with

selected shipping option m, = Int (365 / )

d) For a selected shipping option m for hub j to hub k, if supplier is assigned to hub j

and k, which in turn is to be stored in warehouse , can be pre-calculated.

According to the EOQ model, the inventory at warehouse is reviewed periodically,

with a frequency equal to the selected shipping frequency .

Where, is the expected inventory of component at warehouse , which is

the total amount of cycle stock and safety stock. Cycle stock is held based on the

re-order point, and defines the inventory that must be held for production, sale or

consumption during the time between re-order and delivery. Safety stock is held to

account for variability, either upstream in supplier lead time, or downstream in

customer demand. Given by,

,

Where the first item is cycle stock and the second item is safety stock,

= normal distribution service factor based on desired service level

= standard deviation of demand of component at warehouse

= the maximum lead time for component ( ) being

shipped from supplier to warehouse , =

An example computation of effective lead time for safety stock is given in figure

3.3. Supplier 1 and 2 are both assigned to from hub j to k, and corresponding

material 1 and 2 are shipped to warehouse afterwards. Shipping time for each

linkage is given, = 2 days, = 4 days, = 15 days, = 2 days,

with selected shipping frequency = 7 days. Thus, lead time for supplier 1 is 19

days and lead time for supplier 2 is 21 days. Effective lead time for safety stock

calculation is = 21 days.

1A sent 1B sentSupplier 1

2A sent 2B sentSupplier 2

1A&2A received 1B&2B receivedHub j

1A&2A received 1B&2B receivedHub k

1A&2A received 1B&2B receivedWarehouse k

Lead Time of Supplier 1Lead Time of Supplier 2

= 7 days = 2 days = 4 days

= 15 days = 2 days

jkmt

jkmTP1jkmTP2

jkTM

jklmTO

Figure 3.6

The decision variables in the model are,

= binary variable to denote if supplier is assigned to consolidation hub/ origin

port , using shipping option

= binary variable to denote if shipment at consolidation hub/ origin port is

shipped to deconsolidation hub/ destination port , using shipping option

= binary variable to denote if shipments at deconsolidation hub/ destination port

are distributed to warehouse , using shipping option

The model minimizes the network cost given by objective function as,

Min

+

+

+

Subject to,

,

,

,

,

,

The objective function minimizes the sum of following costs:

The annual shipment cost of pre-carriage (1), including other relevant logistics cost like

handling charges at suppliers side; annual shipment of main carriage, including other

relevant logistics cost like BAF and custom clearance fee etc, and annual fixed

operational costs of distribution centers (2); annual shipment cost of on forwarding

carriage (3), including other relevant logistics cost like handling charges at

warehouses side; and annual inventory costs in warehouses (4).

The constraints include,

(5) ensures that each supplier i is assigned to exactly one hub j

(6) ensures that each hub j is assigned to exactly one hub k

(7) ensures that at most one shipping option is chosen for each pair of origin-

destination port

(8) ensures that supplier i is assigned to a hub j with shipping option m, only if the

hub is open with the shipping option m, and hub j is linked to an open hub k under

shipping option m

The idea of using available shipping options at each hub allows us to overcome the

complexities involved in the non-linear analysis of concave shipping cost and inventory

holding cost. Such costs can be pre-computed for each shipping option for each pair of

origin-destination ports, and for all suppliers and warehouses related to the hub links.

Our model is reduced to a linear binary integer program with decision variables which

tells us directly the opening/closing of hubs; selection of shipping option; assignment

of material flow from origin to destination port; and assignment of suppliers to

consolidation hubs. In addition, the selected shipping option also sets the inventory

replenishment cycle for each open hub and the suppliers assigned to it, as well as

provides information on the expected inventory of each component i at the warehouse

.

3.2 Solution Methodology

As in a supply chain with four echelons namely supplier, consolidation center,

deconsolidation center and consignee warehouse, the model is a linear binary integer

program (BIP), where the computational difficulty depends on its number of binary

decision variables. If there are binary decision variables, then the solution space

will increase exponentially by . The binary decision variables in this model are ,

and . For a material flow network optimization problem with six suppliers, four

potential origin ports, two potential destination ports, two warehouses and three

shipping options per pair of consolidation hub/ origin port and destination port/

deconsolidation hub (that is, = 6, = 4, = 2 and =2, = 3), there will be a total

of 108 binary decision variables, and the solution space will be 2108 = 3.24532

solutions.

The branch-and-bound method is used for solving such BIPs. We know that for very

large problem size, the computational time will increase. We have implemented the

model using the Lingo solver and branch-and-bound is used to solve the model. We

tested the model with the problem described above on an Intel Pentium 4, 2.4 GHz

PC with 256MB of RAM, and the computation time is only 1 second involving 81

iterations.

When designing such a network, it is not common to expect large numbers of

potential origin-destination/consolidation-deconsolidation ports ( - ), warehouses ( )

and available shipping options ( ). Only a large number of suppliers are expected in

reality. With the computational efficiency provided, we can expect a relatively large

problem size of 50 to 100 suppliers to be solved within reasonable computational

time.

4. INDUSTRY CASE STUDY

4.1 Company profile

Bosch has been present in Singapore since 1923. The company delivered several

‘firsts’ products from Power Tools and Automotive Aftermarket into the Southeast

Asian market. With the acquisition of Diesel Electric in 1973, Robert Bosch (SEA) Pte

Ltd (RBSI) was born. Following the set-up of new business divisions and expanding

services, a new building was built and commissioned as the new regional

headquarters in 1996.

Today, Bosch is represented in Singapore by four companies - Robert Bosch (SEA)

Pte Ltd, Bosch Rexroth Pte Ltd, Bosch Packaging Technology Pte Ltd and BSH Home

Appliances Pte Ltd, of which Bosch has a 50 per cent interest.

Since the 1920s, Bosch has been present in Southeast Asia through various sales

and representative offices as well as manufacturing plants.

We have been active in Bosch’s three business sectors namely automotive

technology, industrial technology, as well as consumer goods and building technology.

The vast potential and development of market in the region saw the establishment of

the Southeast Asian regional headquarters in 1996.

Today, Robert Bosch (SEA) Pte Ltd (RBSI) is the headquarters for the region and is

located in Singapore, with subsidiaries in Malaysia, Philippines, Thailand, Vietnam

and Indonesia.

Apart from being the regional headquarters, RBSI is also home to the Asia Pacific

headquarters for Automotive Aftermarket and Security Systems. In addition,

Singapore also houses the IT Center for Asia Pacific with an IT operations center to

service more than 200 Bosch locations in the region and an IT R&D facility to design

and develop global IT platforms and systems for Bosch. In 2008, the Bosch Group

established its Asia Pacific regional headquarters for Research and Advance

Engineering in Singapore. The Research and Technology Center Asia Pacific (RTC-

AP) will study technology trends and market opportunities in the region to identify

local technology leaders and strategic research subjects.

In October 2009, RBSI moved into the new Robert Bosch SEA headquarter building

(SGP101) which spans over 223,000 square feet and have brought together several

Bosch divisions under one roof. The new building was officially opened on 12 May

2010.

Robert Bosch is a German company that has been around for 125 years, it was

founded by Mr. Robert Bosch in 1886 and the company headquarters is now located

in Gerlingen, Germany. Bosch is the world’s leading technology and Services

Company, with more than 350 subsidiaries and regional companies in over 60

countries in the world. There are some 285,000 associates worldwide and generated

sales of 47.3 billion Euros in fiscal 2010.

Bosch Singapore, also known as Robert Bosch (SEA) Pte Ltd is the regional

headquarters for the ASEAN region. On 12 May 2010, the new regional building of

Robert Bosch (SEA) was officially opened by Mr Lim Hng Kiang, the Minister for

Trade and Industry. The building has a floor area of 223,000 square feet and has

about 600 Bosch associates. Bosch Singapore has achieved a sale of 113 million

Euros in 2010.

The main business divisions in Singapore are the automotive technology, consumer

goods and building technology and finally the Industrial technology.

Bosch automotive aftermarket is one of the biggest in the Bosch group. They

achieved sales of 28.1 billion Euros in 2010 worldwide. Bosch automotive aftermarket

sells products related to the automotive industries such as brake pads, wiper blades

and also starter batteries. Bosch automotive also offers car services. The car services

include maintenance, repairs and diagnosis.

The other business division is the consumer goods and building technology. This

division consists of 3 separate divisions which are the power tools division, household

appliance and the security systems.

The power tools division offers products such as power drills, electronic screw drivers,

and even lawn and gardening tools. Bosch power tools are one of their icons as they

specialize in tools for industrials and home standards.

The household appliance offers products such as fridge, washing machine and iron.

Bosch is the European market leader of household appliances.

Lastly the Security system offers products such as CCTV, alarms and also speakers.

Bosch security products are used all over the world in all types of buildings. From

parliaments to shopping centre, it is very likely to see the Bosch security products

being used.

The Consumer goods and building technology achieved sales of 12.5 billion Euros in

sales in 2010.

Finally, the industrial technology offers products such as solar panels, packaging

technology, services and hydraulics technology also known as Bosch Rexroth. They

are the smaller divisions of Bosch and they have their individual offices in Kallang and

Tuas. The division has generated sales totaling 6.7 billion Euros in 2010.

4.2 Data collection and analysis

4.3 Data input and computation

4.4 Results analysis

5. IMPLICATION FOR BUSINESS PRACTICE

6. CONCLUSION

7. LIMITATION AND FUTURE WORK

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List of Laws and Regulations

ISSN 0583-3655, YEARBOOK OF STATISTICS SINGAPORE 2011,

Department of Statistics, Ministry of Trade & Industry, Republic of Singapore,

(www.singstat.gov.sg )

Webliography

Surname, first name of author(s), title of the article, year of publication, in

exact URL, date of last access

(Use Arial/TNR, size 12, spacing before=12pt)

GRAHAM, J. , “Logistics: Vital to Every Business”, 2000, http://www.going-

global.com, last accessed on 25.12.2011

“Logistics Service Providers - Do They Have A Role In Your Organization?”,

2011, http://www.bestlogisticsguide.com/, last accessed on 25.12.2011

Freight Forwarder, “Logistics Outsourcing Risk and Decision Analysis,” 2010,

http://www.laowee.com/, last accessed on 25.12.2011

GONZALEZ, A., “Reasons Why Companies Aren’t Outsourcing to 3PLs,” 2010,

http://logisticsviewpoints.com/, last accessed on 26.12.2011

Langley C., Allen G., Tyndall G. Cap Gemini Ernst &Young Inc. -

http://www.us.cgey.com, last accessed on 11.12.2011

Accounting for management, “Break Even Point Analysis-Definition,

Explanation Formula and Calculation”, 2011,

http://www.accountingformanagement.com/, last accessed on 17.1.2012

List of other sources