Physical distribution costs in construction supply chains: A systems approach

Abstract

The objective of this study is to provide insights into the trade-offs of physical distribution cost patterns in construction supply chains by modelling and measuring these costs. The model of the physical distribution system consists of the following (cost) elements: inventory, transport, handling, and warehousing. It is applied in the supply chain of insulation materials. It is concluded that the model gives a valid explanation for the major cost trade-offs in the supply chain analysed. By using this model, firms involved in the physical distribution of building materials in construction supply chains may find the optimal distribution strategy.

Full text

456 Int. J. Logistics Systems and Management, Vol. 7, No. 4, 2010
Copyright © 2010 Inderscience Enterprises Ltd.
Physical distribution costs in construction
supply chains: a systems approach
Hans Voordijk
Faculty of Engineering Technology,
Department of Construction Management and Engineering,
University of Twente,
P.O. Box 217, 7500 AE Enschede, The Netherlands
Fax: +3153-4892511
E-mail: j.t.voordijk@utwente.nl
Abstract: The objective of this study is to provide insights into the trade-offs
of physical distribution cost patterns in construction supply chains by
modelling and measuring these costs. The model of the physical distribution
system consists of the following (cost) elements: inventory, transport, handling,
and warehousing. It is applied in the supply chain of insulation materials.
It is concluded that the model gives a valid explanation for the major cost
trade-offs in the supply chain analysed. By using this model, firms involved in
the physical distribution of building materials in construction supply chains
may find the optimal distribution strategy.
Keywords: physical distribution; building materials; cost trade-offs.
Reference to this paper should be made as follows: Voordijk, H. (2010)
‘Physical distribution costs in construction supply chains: a systems approach’,
Int. J. Logistics Systems and Management, Vol. 7, No. 4, pp.456–471.
Biographical notes: Hans Voordijk is an Associate Professor of Supply
Chain Management at the Department of Construction Management and
Engineering of the University of Twente, The Netherlands. His research
and teaching activities focus on supply chain management and the adoption of
ICT in the construction industry. Before joining the Twente University,
he was Project Manager at the Netherlands Organisation of Applied Scientific
Research (TNO) and Assistant Professor at Tilburg University. He holds a
PhD in Economics from Maastricht University.
1 Introduction
The logistics performance of supply chains is receiving increasing interest in the
literature (e.g., Gunasekaran et al., 2001, 2004; Lohman et al., 2004; Ramdas and
Spekman, 2000; Tsiakis and Papageorgiou, 2008). Companies look for ways to improve
this performance by integrating their operations across subsequent echelons and separate
functions in the supply chain (Lohman et al., 2004). The supply chain management
literature reports a number of studies on the benefits that a firm derives from linking
performance with suppliers and customers (Carter and Ellram, 1994; Salvador et al.,
2001; Rungtusanatham et al., 2003; Brewer and Speh, 2000; Chen et al., 2006;

Physical distribution costs in construction supply chains 457
Van Nyen et al., 2009). Despite the importance of logistics performance in the
construction industry, empirical data about this performance of this industry is scarce
(Costa et al., 2006; Wegelius-Lehtonen, 2001). In construction, manufacturing, supplying
and constructing parties work together in constantly changing coalitions on different
building projects (O’Brien et al., 1995). For each construction project, physical
distribution of building materials takes place by a different coalition of manufacturers,
warehouses, transport firms and contractors (Voordijk et al., 2000). These firms are
involved in storing and moving both raw materials and components through the supply
chain to its final destination, the construction site (Graham and Hardaker, 2000).
The final destination, the construction site, and the different routes that can be used to
distribute building materials from the manufacturer to this site change with each new
construction project.
Dissatisfaction with the fragmented and temporary organisation of the construction
supply chain has led several firms to offer a total construction package from
design till delivery of a building project. This approach has reduced project uncertainty
by systemisation and integration of the different stages of the building process.
As a result, non-traditional building project management methods such as turn-key
and design-and-build have become more popular. In terms of Green et al. (2005),
the emergence of these non-traditional building project management methods results in a
less-fragmented organisation of the building process and provides possibilities for
supply chain management practices that approximate established practices within other
industries (Vrijhoef and Koskela, 2000). In this context, physical distribution could
play an important role in improving the logistics performance of the building sector.
To improve this performance, insights into the trade-offs of physical distribution cost
patterns in the specific setting of construction supply chains are necessary. Outside the
construction industry, these trade-offs have been studied in several supply chains.
Examples are the capital goods supply chain (Van Nyen et al., 2009), chemicals
(Tsiakis and Papageorgiou, 2008) and the bread industry (Haq and Kannan, 2006).
Despite its potential importance, analysis of trade-offs of physical distribution cost
patterns has not yet received much attention from either the building sector or the
academics. There is a lack of empirical knowledge about the major cost trade-offs at
the level of the construction supply chain.
The objective of this study is to provide insights into these cost trade-offs by
modelling and measuring physical distribution costs in the construction supply chain.
On the basis of these insights, one can select the optimal physical distribution strategy for
these supply chains and achieve cost savings in inventory and working capital costs,
as well as responding more rapidly to customer demand (Handfield and Nichols, 1999).
The key to efficiently managing the physical distribution of building materials in a supply
chain is the total cost concept (Bowersox et al., 2007; Stock and Lambert, 2001).
At the heart of this concept is a trade-off analysis between the elements of the physical
distribution system (Ballou, 2004; Jackson et al., 1994). Therefore, a systems approach
to physical distribution on the level of a supply chain is presented first in this study.
It is assumed that the physical distribution system consists of the following basic
interdependent (cost) elements: inventory, transport, handling and warehousing.
The costs of these physical distribution elements are modelled and quantified in the
second section. Third, this model is applied in a particular construction supply chain.
Finally, conclusions are drawn. On the basis of this study, alternative distribution

458 H. Voordijk
strategies in construction supply chains can be evaluated. An optimal distribution strategy
for a particular firm can be found by comparing these alternative strategies.
2 A systems approach to physical distribution
At the centre of our analysis is the physical distribution of products in the construction
supply chain: the flow of building materials from the initial producer through to delivery
at the construction site. Since physical distribution in a supply chain encompasses
activities by different independent firms, with potentially conflicting objectives,
the system analysed extends beyond the legal limits of one firm. A supply chain
is, in itself, a system with the purpose of managing the orderly flow of material.
Given that, in such a chain, the decisions of one firm affect the decisions of another,
the supply chain is a more appropriate unit of analysis than an individual firm is.
Therefore, a systems approach is a critical concept in this study. The systems approach
essentially accepts that all functions, or activities, need to be understood in terms of how
they affect, and are affected by, other elements and activities with which they interact.
Utilising the systems approach, attention is focussed on the interactions among the
elements of the physical distribution system of a supply chain: inventory, transport,
handling and warehousing (Ballou, 2004; Stock and Lambert, 2001). Given a supply
chain, transportation provides place utility. The requirement for transport between
facilities in the supply chain is determined by the inventory policy followed by
firms in this network (see for example Shu and Sun (2006), Tang and Yang (2008)).
Within warehouses, material handling is an important activity and accounts for a great
deal of the physical distribution costs in terms of operation and capital expenses
(Bowersox et al., 2007). The basic patterns of physical distribution strategies in a supply
chain can be categorised by the number of levels present in a chain. The two-level
model involves direct transfer between producer and final destination (a direct system).
The three-level model includes an additional party, i.e., a wholesaler or a trader
(Chiang and Monahan, 2005). This so-called echelon system implies that the flow of
products proceeds through a series of consecutive locations as they move from their
origin to the final destination. The essential characteristic is that finished goods are
stocked at one or more points in the supply chain.
In contrast to echelon patterns, involving a third party, there are physical distribution
systems where the final customer is served from one of the limited numbers of central
stock-holding locations owned and managed by the manufacturer (Bowersox et al.,
2007). In direct systems, firms have a central inventory from which customer orders are
filled, and items are shipped directly from the supplier to the customers without going
through distribution centres or wholesalers (Simchi-Levi et al., 2003). The expenses
of operating a distribution centre are thus avoided, and lead times reduced. A major
disadvantage can be that transportation costs increase since smaller trucks must be sent to
more locations. For this reason, direct shipment is the most common method when a
customer requires a full truckload of materials, since intermediate warehousing would not
reduce transportation costs.
The costs of physical distribution activities often move in opposing directions.
A trade-off occurs when an increase in one cost category, owing to a change in
distribution strategy, can be compensated for by a decrease in another cost category
producing lower total costs (Crnkovic et al., 2008). Stock and Lambert (2001) give the

Physical distribution costs in construction supply chains 459
example that savings resulting from favourable purchase prices on large orders
may be offset by greater inventory costs. The successful identification of cost
trade-offs is the crux of efficient management of a physical distribution system
(Moynihan and Padmanabhan, 2006; Jackson et al., 1994; Kumanan et al., 2007; Stock
and Lambert, 2001). This study focuses on trade-offs on two levels: inter-organisational
and functional. Inter-organisational trade-offs are those between firms in the building
materials’ supply chain: manufacturers, intermediary parties and contractors. Functional
trade-offs concern trade-offs between elements of the distribution system of building
materials: inventory, transport, handling and warehousing. The optimum use of total cost
analysis involves measuring all the relevant costs and then operating in such a way that
total costs are minimised.
Physical distribution decisions in supply chains can be described in terms of choices
that have to be made between different routes to distribute products, each alternative
having a different impact on logistics performance in terms of costs and customer
satisfaction (Tavasszy, 1999). The scale of the impact will depend on characteristics of
the goods (value density, weight/volume ratio, etc.), on the level of service offered and
on a number of external circumstances (for example, market characteristics such as
competition, market volume and geographical scope).
Figure 1 shows the key goods and service attributes that together determine physical
distribution costs (Tavasszy, 1999). These costs are made up of inventory, transport and
handling costs, the third level in the figure:
• Inventory costs are related to the number and location of stock facilities
and the safety stock kept.
• Transport costs depend primarily on the mode of transport, vehicle size, shipment
size and the distance between locations.
• Handling costs are related to the number of times a product is lifted in the supply
chain. Major subcategory of handling costs is the costs of warehouses.
Figure 1 Service and goods’ attributes and physical distribution costs
Source: Tavasszy (1999)
The second row down in Figure 1 indicates the attributes of the goods that are
relevant to these cost categories. One of the key attributes in determining an optimal
distribution route is the value density, namely the value of a product in relation to its

460 H. Voordijk
weight and volume. The top row shows other factors or attributes related to the services
that, together with the goods’ attributes, determine the costs of each alternative
service configuration. A desire to improve service levels will often result in increasing
physical distribution costs given a product of constant value density.
Distinct from handling costs, the costs of warehouses can be analysed separately
(Stock and Lambert, 2001). Warehousing costs are the cost associated with land and
buildings. It is, therefore, assumed that the physical distribution system consists of the
following basic interdependent cost elements: inventory, transport, handling, transaction
and warehousing. For each element, the major variables determining these costs are
modelled in the next section.
3 Modelling physical distribution costs of building materials
In this section, factors affecting different categories of physical distribution costs are
modelled. This model consists of ‘robust’ equations. In these equations, order data of a
contractor or trader in a construction supply chain is the major input. The quantity of this
order is measured in cubic metre but can be replaced by tonnes or number of pieces.
The model is presented in terms of dependent and independent variables. The dependent
variables are the costs of different physical distribution elements. The independent
variables are the factors affecting these costs.
3.1 Inventory costs
Inventory costs are determined by interest costs, based on the value density of the
inventory (i.e., the costs of capital tied up), and costs owing to obsolescence and spoilage.
The formulation of inventory policies is fundamental (the number and location of stock
facilities, and the stock kept for safety reasons). Obsolescence costs can be calculated on
the basis of past experience. Inventory costs can be based on the average quantity of
stock held per year of a certain product expressed in Euros per unit of stock. For example,
inventory costs of insulation material can be expressed in €/m3.
Independent variables: Quantity ordered in m3 (Q), average number of days in stock
(sdx), price per m3 (P), interest, risk and obsolescence tariff (Ix, rix, Ox), with x ε {m, t, c},
m = manufacturer, t = trader and c = contractor. The same equation is used for direct and
echelon systems, for each link of the chain the value may vary. Sdm shows the number of
days a good is stocked at the manufacturer. Variables without subscript give a value for
the whole supply chain.
Formula: Sdx × Q × P × (Ix + rix + Ox)/365.
3.2 Handling costs
Handling costs are associated with loading and unloading, internal transport, stocking up
and stocking out, picking and assembly packing. They depend primarily on the volume
and nature of the throughput, the packing density, stock rates, together with the handling
methods employed. Major cost categories are labour (pickers, packers) and handling
equipment costs. The labour costs can be calculated by a tariff per minute multiplied
by the number of minutes needed for a unit of product. This unit can be a cubic metre,

Physical distribution costs in construction supply chains 461
a tonne, a pallet, etc. Costs of equipment (use of a lifting truck, a crane, or other
equipment) can be calculated in the same way.
• Internal transport and stocking up and out
Independent variables: Variable time per m3 for internal transport (intTx), stocking up and
out (stuTx, stoTx), order quantity in m3 (Qm3), wage costs per hour (Wc_intx, Wc_stux,
Wc_stox), equipment use (Eq_intx, Eq_stux, Eq_stox: 0–1 variables), equipment costs per
hour (Eqc_ intx, Eqc_ stux, Eqc_stox). These equations for the costs of internal transport,
and stocking up and out are analogous with x ε {m, t, c}.
Formula: Wage costs internal transport (Wc_intx/60) × (intTx × Qm3)
Equipment costs internal transport (Eqc_intx/60) × (intTx × Qm3).
• Loading and unloading
Independent variables: Order quantity in m3 (Qm3), fixed (un-)loading time for a truck
(LoFTy, UnlFVz), variable (un-)loading time per m3 (LoTy, UnlTz), wage costs per hour
(Wcx), equipment use (Eq_loy, Eq_unlz: 0–1 variables), equipment costs per hour
(Eqc_loy, Eqc_unlz), carrying capacity and average payload of a truck (capy, apy).
Fixed loading costs depend on the carrying capacity and average payload of a truck.
The equations are loading and unloading costs analogous with x ε {m, t, c}, y ε {m, t},
z ε {t, c}. Certain loads from manufacturer to trader are used to get the truck fully loaded.
In other words, certain goods are transported to the trader when there is a truck available.
In that case, a smaller amount of the fixed (un-)loading time for a truck is attributed to the
order than would be justified by the order quantity (factor 0.7).
Formula: Wage costs loading
(Wcx/60) × (LoFTy × [Qm3/(capq × apq)] + LoTy × Qm3)
Equipment costs loading
(Eqc_loy/60) × (LoFTy × [Qm3/(capq × apq)] + LoTy × Qm3)
When M_lay = yes(1), in other cases the equipment costs are 0.
3.3 Transport costs
Transport costs depend primarily on the type and amount of goods carried from location
to location, the mode of transport, vehicle size, consignment size and the distance
between locations. Transport costs vary significantly with the number and locations of
manufacturers, traders, and contractors and the services provided (frequency of rides).
For the calculation of transport costs, three route types can be distinguished: direct
delivery from factory to building site, from the factory to the trader and from a trader
to the contractor or construction site. For each route, stopping and kilometre costs can be
calculated. Stopping costs are related to finding the address and waiting times.
With direct distribution, all the transport costs are attributed to one delivery. With group
deliveries, transport costs are divided over different deliveries by a groupage factor.
Independent variables: Ordered quantity in m3(Q), order quantity of trader at
manufacturer (Qi) (Q <= Qi), carrying capacity truck (cap), average payload (ap),
number of full truck loads (ftl), size of a non-full truck load (nftl) (Qpart), costs per
stop (sc), groupage factor (gf), distance in kms (D), costs per km (ck). In the route

462 H. Voordijk
factory–trader, the volume Q is part of a larger volume Qi that is delivered to the traders’
warehouse (therefore Q <= Qi). When Qpart > 0, independent variables are the number of
addresses in one trip (adr), trip costs (tc) and distance between unloading addresses
(D_unladr). The number of addresses in one trip with nftls (adr) depends on the quantity
of an nftl in relation to the loading capacity of truck. The groupage factor (gf) shows that
the trader orders large quantities at the manufacturer.
• Factory – Construction site
Stop costs are the number of stops × costs of each stop. Kilometre costs: first part of
the costs are ftl’s (round-trip distance × costs/km × number of ftl’s); the second part are
costs of nftl’s (costs are divided proportionally over the number of addresses visited).
The costs of a trip with various nftls are the number of kms × costs/km; the number of
kms = round-trip kms + the number of addresses visited minus 1 × the average distance
between the addresses.
Formula: Stop costs ftl × sc or (ftl + 1) × sc, when also nftl
km costs (D × 2 × ck × ftl) + tc/adr
where tc = (D × 2 + (adr – 1) × D_unladr) × ck
(trip costs of nftl deliveries)
and adr = (cap × ap)/Qdeel with Qdeel = Q – ftl’s.
• Factory – Trader
Option one (Qpart = 0) is an order of one or more ftls and no nftls. Option two (ftl = 0,
0 < Qpart < 0.4 × cap) is a relatively small order used to fill a truck that is not fully loaded.
In that case, less transport costs are attributed to the order than would be justified by the
order quantity. Option three (Qpart > 0.4 × cap) is a larger nftl or one (or more) ftl’s.
Formula: Stop costs ftl × sc or ftl × sc + sc/gf, when also nftl’s
km costs when ftl > 0 Qdeel = 0: (D × 2 × ck × ftl)/gf
when ftl = 0 0 < Qdeel < 0.4 × cap: 0.7 × tc/(adr × gf)
when ftl >= 0 Qdeel> 0.4 × cap:
[(D × 2 × ck × ftl) + rk/adr]/gf
where gf = Qi/Q
and tc = (D × 2 + (adr – 1) × D_unladr) × ck
and adr = (cap × ap)/Qdeel with Qdeel = Q – ftl’s.
3.4 Transaction costs
Transaction costs are associated with order receipt, order entry and order processing.
In this model, transaction costs are estimated and calculated by the number of different
articles of each order. It is assumed that the purchasing department of a contractor
or other customer always orders their goods at the trader of building materials.
Because transaction costs are not the focus of this study, the equation is kept simple.

Physical distribution costs in construction supply chains 463
Independent variables: costs of an order (ocx), number of order lines (olx) with
x ε {m, t, c}.
Formula: ocx/olx.
3.5 Warehousing costs
Warehousing costs are the cost associated with land (rental costs), buildings (rent, rates)
and services (electricity). These costs can be estimated from market rents to avoid a fully
depreciated warehouse influencing a realistic cost estimation (a controversial aspect of
warehousing costs is the appropriate charge to place on invested capital). The size of a
warehouse can be defined in terms of floor area or volume. Using the floor area ignores
the varying capability of warehouses to store products vertically. Cubic space refers to
the volume available within a facility. Warehousing costs are based on the average stock
of a certain product over a year. These costs can be expressed in €/m2 of warehouse or
shed. A stacking factor can be used being the volume available (m3) per square metre of
floor space (i.e., the storage height).
Independent variables: Average number of days in stock (sdx), space needed in m2
(Q_m2
x), storage costs per m2 in warehouse inside (WKx) and on a stacking place outside
(SKx), % of m2 in warehouse (m%x) with x ε {m, t, c} and where Q_m2
x = Q/m3 space
per m2 ground space (the stacking factor). A multiplier of 1.3 ascribes corridors and
loading and unloading space to the warehousing costs.
Formula: (WKx × m%x + SKx × (1 – m%x)) × sdx × Q_m2
x × 1.3.
4 Estimating physical distribution costs in a construction supply chain
Supply chain management in the construction sector means that these parties agree upon
the way in which production and information flows are organised (Christopher, 1992;
Maloni and Benton, 1997; Meijboom, 1999). Akintoye et al. (2000) define supply chain
management in construction as
“the process of strategic management of information flow, activities, tasks and
processes, involving various networks of organisations and linkages (upstream
and downstream) involved in the delivery of quality construction products and
services through the firms, and to the customer, in an efficient manner.”
Figure 2 illustrates a generic construction supply chain structure consisting of the
following three major systems.
• The first system is the manufacturing of building materials and components.
On behalf of the final customer, the designer determines the materials and
commodities that will be manufactured and flow through the supply chain.
• The second major system is the physical distribution of materials. Two basic
distribution strategies can be found in the construction sector: direct distribution
of materials from manufacturer to construction site or distribution through
intermediary parties such as wholesalers or traders (Bowersox et al., 2007).

464 H. Voordijk
• The third major system is construction, the so-called operating system that directly
produces the end product (Bennett, 1985). Construction brings together many
different kinds of work and actors involving different technologies at the building
site. When all construction activities have been performed, the building is delivered
and ready to be used.
Figure 2 A generic construction supply chain structure (see online version for colours)
The different basic chain structures supported by this model developed are shown in
Figure 3. Two basic manufacturing methods are assumed: production-on-order and
production-on-stock. Product flows are distributed by direct or echelon systems.
There are two possibilities when the flow of products is distributed via the trader:
the trader is the location of a stocking point or just a place for cross docking.
Figure 3 Basic structures of the building materials’ supply chain
In the case analysed, the focus is on the physical distribution costs of the supply chain
of insulation materials. A large Dutch manufacturer of insulation materials, a trader of
building materials, and a contractor processing this product in a construction project
provided cost data needed for testing the model, in general, and providing insights
in the trade-offs in physical distribution costs in this specific construction supply chain.

Physical distribution costs in construction supply chains 465
On the basis of a specific product, order, process and costs data of an order for insulation
material, physical distribution costs are calculated and a number of cost matrices
developed. In this case, these costs are measured and modelled for an order of 15 m3 of an
isolation material with a delivery time demanded of four days.
On the basis of the data provided by the manufacturer of insulation materials,
the trader of these materials and the contractor processing them, a matrix results in which
the most important decision variables, order quantity and delivery time are presented in
one figure (see Figure 4). This figure shows that for certain combinations of order
quantity and delivery time, it is (from a supply chain perspective) cheaper to deliver the
materials through the warehouse of a building materials’ trader. For other combinations,
direct delivery from factory to construction site is preferred. Certain combinations of
order quantity and delivery date are impossible: the quantity demanded cannot be
delivered by the trader because the order is too large, while at the same time the supplier
cannot deliver it in the time demanded. These problems could be solved by maintaining
larger local inventories (perhaps another transhipment point), using a central warehouse,
having traders delivered materials to each other (where a trader acts as central warehouse
for other traders for specific products), and shortening the delivery time from the
suppliers.
Figure 4 Optimal distribution strategies for a variety of orders for insulation material
When a matrix as shown in Figure 4 is determined, the focus is on determining crossover
points in the choice between direct delivery and delivery through intermediaries.
An example of these calculations is shown in Tables 1 and 2. These tables are based
on calculations for the order of 15 m3 of insulation material to be delivered within
four days. By comparing the tables, an optimal distribution strategy (i.e., direct or
indirect delivery) for this particular order can be made. In this case, direct delivery of this
particular order results in an increase of €50 for the supply chain as a whole owing to the
differences in transport and handling costs. Direct delivery involves an increase in
transport costs that are not sufficiently offset by a decrease in handling costs.

466 H. Voordijk
Table 1 Physical distribution costs in the direct system
Cost categories Factory Trade Building site Total
Warehousing 13 0 13
Inventory 3 1 4
Handling 89 161 250
Transport 318 0 318
Total 423 162 585
Table 2 Physical distribution costs of delivery through intermediary party
Cost categories Factory Trade Building site Total
Warehousing 4 16 0 20
Inventory 1 3 0 4
Handling 89 81 136 306
Transport 160 45 0 205
Total 254 145 136 535
For each cost category and delivery alternative, the different costs can be specified by
using the model developed. In Table 3, the transport costs of delivery via the trader of
building materials are shown.
Table 3 Transport costs in case of delivery through intermediary party
Factory Trade Building site Total
Quantities ordered in m3 15 15 15
Quantities ordered by trader in m3 30 30
Stop costs order x (€) 15.00 8.33 23.33
Costs for each stop (€) 30.00 15.00
Groupage factor 2 1.8
Km costs order x (€) 144.80 37.04 181.84
Distance in kilometres 180 20
Tariff /kilometre (€) 2.50 2.00
Costs driven kms (part of the load) 1055.00 100.00
Number of full loads 0 0
Part of the load (in m3) 30 15
Number of addresses (route with nfls) 2.6 –
Vehicle carrying capacity 85 30
Average load factor 90% 90%
Groupage factor 2.0 2.7
Total costs transport 159.80 45.37 – 205.17
It has to be stressed that the matrix shown is just one case, determined by the data given
of the parties involved. When data change, the matrix will change. This gives the

Physical distribution costs in construction supply chains 467
possibility to show the effects of changes in the process and related costs and to make
trade-offs on a very detailed level. By varying the height of the variables, the so-called
What–If questions can be studied. The major trade-off in this chain of isolation materials
is the trade-off between handling and transport costs. When these materials are
transported through a wholesaler, handling costs increase, whereas transport costs and
handling costs of the manufacturer and at the construction site decrease.
Several developments in the construction supply chain influence the cost trade-offs
of the physical distribution system. Centralisation of transport and distribution of
building materials by intermediary parties is one of the major developments for supply
chains of building materials in the Netherlands. Most intermediary parties have been
centralising their inventories into central warehouses. This centralisation and the use of
IT result not only in lower inventory costs, a more efficient use of the transport fleet,
but also in an increase in the average length of haul between the central warehouse,
on the one hand, and the construction sites and the outlets, on the other hand.
Another important development influencing cost trade-offs is the increasing importance
of just-in-time-deliveries between local branches of intermediary parties and construction
sites. The retailer does not wait anymore for enough freight to be collected for a fully
loaded truck. As a consequence, only a few construction sites are visited in a round-trip.
At these local branches supplying building materials to construction sites, the frequency
of rides increases and the number of construction sites visited in one ride decreases.
The use of IT, however, improves the efficiency of the transport schedule and decreases
the empty running.
5 Managerial implications
A major problem in determining the optimal trade-off of physical distribution costs on
the level of a construction supply chain is the lack of transparency and the high level of
inter-organisational distrust. This lack of transparency is partly caused by the competition
for control of the supply chain of building materials by intermediary parties and large
contractors. Both parties try to integrate the coordination of the building materials’
supply chain into their core activities. Wholesalers and traders want to control
their distribution channels, whereas contractors want to control the supply of building
materials. From a strategic viewpoint, manufacturers are not true competitors for
the coordination function because their core activity differs from that of the trader
or contractor (production vs. distribution or assembling of building materials). To the
manufacturer, the construction supply chain is a typical example of partner asymmetry:
each partner needs what the other can supply (Harrigan, 1988). Partner asymmetry is not
the case with the trader-contractor relationship since there is competition for the
coordination function. Therefore, supply chain thinking is developing very slowly and
optimising physical distribution on supply chain level is difficult.
Traditionally, contractors in house and office building are inclined to accept work for
every type of project. The dominant strategy of contractors is, in Porter’s strategy
typology, a low-cost strategy for all segments of the market. Supply chain thinking and
optimising physical distribution on supply chain level may result in cooperation between
parties in construction supply chains. This cooperation may cause a shift from the
dominant low-cost strategy for all market segments to a differentiation strategy for one or
a few market segments. Construction supply chain cooperation can result in a cooperative

468 H. Voordijk
form of organisation not only directed towards minimising costs, but also striving for
joint value maximisation. Cooperation between parties in construction supply chains
makes it possible to present a ‘total product’ with quality guarantees to the market.
This shift towards the strategy for product differentiation may have far-reaching
implications. Opportunistic behaviour is replaced by mutual trust. Instead of bounded
rationality, know-how is transferred between firms for product development. When this
development takes place, the nature of competition in the building industry will
fundamentally alter.
6 Conclusions
The objective of the study is to evaluate alternative distribution strategies with different
impacts on costs by measuring physical distribution cost patterns. An optimal distribution
strategy for a particular firm can be found by comparing these alternative strategies.
To provide this insight, we modelled costs of a whole supply chain of building materials.
In developing this model, a two-stage process was followed:
• first, the most important elements of physical distribution system have been analysed
• second, for each element of the physical distribution system, the factors affecting
the costs of such an element have been modelled.
It can be concluded that this model seems to give a valid explanation for the major
trade-offs in a particular supply chain. When manufacturers and contractors are capable
of dealing with large lot sizes, the cost per unit of distributing goods in a two-level chain
is relatively low. With small lot sizes, a two-level chain will usually generate high
distribution costs. An additional level means increasing handling costs because an extra
transhipment takes place. The transport costs decrease when the order is small, because
of the groupage factor and the use of smaller and cheaper lorries with drivers who know
the local surroundings. Besides the order quantity, another major variable determining
direct or indirect distribution is the value density of a product.
The model can be refined on the following points. By replacing the manufacturer by a
central warehouse in the model and interpreting the branches of the traders as local
warehouses, the model can also be used to get insight in internal materials flows of an
intermediary party. Distinguishing sales, invoicing and (electronic) ordering costs can
further refine the category of transaction costs. For transport costs, it is assumed that the
factory is located in the Netherlands or a surrounding country. For most raw materials,
this is the case. International transport of building materials and, related to this, the use of
other modes of transport can be added optionally.
This research showed that analysing trade-offs of physical distribution costs
on the level of the supply chain provides possibilities for lowering distribution costs.
Optimising physical distribution on supply chain level may result in supply chain
cooperation. This cooperation can even result in a cooperative form of organisation
not only directed towards minimising costs, but also striving for joint value
maximisation.

Physical distribution costs in construction supply chains 469
Acknowledgements
We thank the reviewers for their constructive and helpful comments on the paper.
We are also grateful to Frank Baartmans, Gerda Enthoven, and Joek Tempelman for
providing data used in this paper.
References
Akintoye, A., McIntosh, G. and Fitzgerald, E. (2000) ‘A survey of supply chain collaboration and
management in the UK construction industry’, European Journal of Purchasing and Supply
Management, Vol. 6, Nos. 3–4, pp.159–168.
Ballou, R.H. (2004) Business Logistics/Supply Chain Management, 5th ed., Pearson Education,
Upper Saddle River, New Jersey.
Bennett, J. (1985) Construction Project Management, University Press, Cambridge.
Bowersox, D.J., Closs, D.J. and Cooper, M.B. (2007) Supply Chain Logistics Management,
2nd ed., McGraw-Hill, Boston.
Brewer, P.C. and Speh, T.W. (2000) ‘Using the balanced scorecard to measure supply chain
performance’, Journal of Business Logistics, Vol. 21, No. 1, pp.75–93.
Carter, J.R. and Ellram, L.M. (1994) ‘The impact of interorganizational alliances in improving
supplier quality’, International Journal of Physical Distribution and Logistics Management,
Vol. 24, No. 5, pp.15–23.
Chen, Z., Ma, S. and Shang, J.S. (2006) ‘Integrated supply chain management for efficiency
improvement’, International Journal of Production and Quality Management, Vol. 1,
Nos. 1–2, pp.183–206.
Chiang, W.Y.K. and Monahan, G.E. (2005) ‘Managing inventories in a two-echelon dual-channel
supply chain’, European Journal of Operational Research, Vol. 162, No. 2, pp.325–341.
Christopher, M. (1992) Logistics and Supply Chain Management, Financial Times/Pitman
Publishing, London.
Costa, D.B., Formoso, C.T., Kagioglou, M. and Alarcon, L.F. (2006) ‘Benchmarking initiatives
in the construction industry: lessons learned and improvement opportunities’, Journal of
Management in Engineering, Vol. 22, No. 4, pp.158–167.
Crnkovic, J., Tayi, G.K. and Ballou, D.P. (2008) ‘A decision-support framework for exploring
supply chain tradeoffs’, International Journal of Production Economics, Vol. 115, No. 1,
pp.28–38.
Graham, G. and Hardaker, G. (2000) ‘Supply-chain management across the internet’, International
Journal of Physical Distribution and Logistics Management, Vol. 30, Nos. 3–4, pp.286–295.
Green, S.D., Fernie, S. and Weller, S. (2005) ‘Making sense of supply chain management:
a comparative study of aerospace and construction’, Construction Management and
Economics, Vol. 23, No. 6, pp.579–593.
Gunasekaran, A., Patel, C. and McGaughey, R.E. (2004) ‘A framework for supply chain
performance measurement’, International Journal of Production Economics, Vol. 87, No. 3,
pp.333–347.
Gunasekaran, A., Patel, C. and Tirtiroglu, E. (2001) ‘Performance measures and metrics in a supply
chain environment’, International Journal of Production Economics, Vol. 21, Nos. 1–2,
pp.71–83.
Handfield, R.B. and Nichols, E.L. (1999) Introduction to Supply Chain Management,
Prentice-Hall, New Jersey.
Haq, A.N. and Kannan, G. (2006) ‘Two echelon distribution-inventory supply chain model for
the bread industry using genetic algorithm’, International Journal of Logistics Systems and
Management, Vol. 2, No. 2, pp.177–193.

470 H. Voordijk
Harrigan, K.R. (1988) ‘Strategic alliances and partner asymmetries’, Management International
Review (Special Issue), Vol. 28, pp.53–72.
Jackson, G.C., Stoltman, J.J. and Taylor, A. (1994) ‘Moving beyond trade-offs’, International
Journal of Physical Distribution and Logistics Management, Vol. 24, No. 1, pp.4–10.
Kumanan, S., Venkatesan, S.P. and Kumar, J.P. (2007) ‘Optimisation of supply chain logistics
network using random search techniques’, International Journal of Logistics Systems and
Management, Vol. 3, No. 2, pp.252–266.
Lohman, C., Fortuin, L. and Wouters, M. (2004) ‘Designing a performance measurement system:
a case study’, European Journal of Operational Research, Vol. 156, No. 2, pp.267–286.
Maloni, M.J. and Benton, W.C. (1997) ‘Supply chain partnerships: opportunities for operations
research’, European Journal of Operational Research, Vol. 101, No. 3, pp.419–429.
Meijboom, B. (1999) ‘Production-to-order and international operations: a case study in the clothing
industry’, International Journal of Operations and Production Management, Vol. 19,
Nos. 5–6, pp.602–619.
Moynihan, G.P. and Padmanabhan, N. (2006) ‘AISLE: analytical integrated software for a logistics
environment’, International Journal of Logistics Systems and Management, Vol. 2, No. 1,
pp.78–95.
O’Brien, M.J., Fischer, M.A. and Jucker, J.V. (1995) ‘An economic view on project co-ordination,
Construction Management and Economics, Vol. 13, No. 5, pp.393–400.
Ramdas, K. and Spekman, R.E. (2000) ‘Chain or shackles: understanding what drives supply-chain
performance’, Interfaces, Vol. 30, No. 4, pp.3–21.
Rungtusanatham, M., Salvador, F., Forza, C. and Choi, T.Y. (2003) ‘Supply-chain linkages and
operational performance’, International Journal of Operations and Production Management,
Vol. 23, No. 9, pp.1084–1099.
Salvador, F., Forza, C., Rungtusanatham, M. and Choi, T.Y. (2001) ‘Supply chain interactions and
time-related performances: an operation management perspective’, International Journal of
Operations and Production Management, Vol. 21, No. 4, pp.61–75.
Shu, J. and Sun, J. (2006) ‘Designing the distribution network for an integrated supply chain’,
Journal of Industrial and Management Optimization, Vol. 2, No. 3, pp.339–349.
Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E. (2003) Designing and Managing the Supply
Chain, 2nd ed., Irwin/McGraw-Hill, Boston.
Stock, J.R. and Lambert, D.M. (2001) Strategic Logistics Management, 4th ed., McGraw-Hill,
Boston.
Tang, K. and Yang, C. (2008) ‘A distribution network design model for deteriorating item’,
International Journal of Logistics Systems and Management, Vol. 4, No. 3, pp.366–383.
Tavasszy, L. (1999) Logistics Families – A Short Introduction, TNO Inro, Delft.
Tsiakis, P. and Papageorgiou, L.G. (2008) ‘Optimal production allocation and distribution supply
chain network’, International Journal of Production Economics, Vol. 111, No. 2, pp.468–483.
Van Nyen, P.L.M., Bertrand, J.W.M., Van Ooijen, H.P.G. and Vandaele, N.J. (2009)
‘Supplier managed inventory in the OEM supply chain: the impact of relationship types on
total costs and cost distribution’, OR Spectrum, Vol. 31, No. 1, pp.167–194.
Voordijk, H., De Haan, J. and Joosten, G.J. (2000) ‘Changing governance of supply chains
in the building industry’, European Journal of Purchasing and Supply Management, Vol. 6,
No. 3, pp.217–226.
Vrijhoef, R. and Koskela, L. (2000) ‘The four roles of supply chain management in construction’,
European Journal of Purchasing and Supply Management, Vol. 6, No. 3, pp.169–178.
Wegelius-Lehtonen, T. (2001) ‘Performance measurement in construction logistics’, International
Journal of Production Economics, Vol. 69, No. 1, pp.107–116.

Physical distribution costs in construction supply chains 471
Bibliography
Christopher, M. and Towill, D. (2001) ‘An integrated model for the design of agile supply chains’,
International Journal of Physical Distribution and Logistics, Vol. 3, No. 4, pp.235–246.
Dubois, A. and Gadde, L-E. (2000) ‘Supply strategy and network effects – purchasing behaviour in
the construction industry’, European Journal of Purchasing and Supply Management, Vol. 6,
Nos. 3–4, pp.207–215.
Gopal, C. and Cypress, H. (1993) Integrated Distribution Management, Irwin, Boston.
Johnston, W.J., Leach, M.P. and Liu, A.H. (1999) ‘Theory testing using case studies in
business-to-business research’, Industrial Marketing Management, Vol. 28, No. 3,
pp.201–213.
Yin, R.K. (1994) Case Study Research: Design and Methods, Sage Publications, London.