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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