2010-18
Defining Product Customization as Form Postponement
Katsuyoshi Takashima

Defining Product Customization as Form Postponement

1. Introduction

Over the past couple of decades, customization based on computer-aided
manufacturing has become increasingly important in various industries (Kotha 1995;
Lampel and Mintzberg 1996; Feitzinger and Lee 1997; Gilmore and Pine II 1997). This study isintended to explain the relatively recent development of customization, by referring to
the postponement-speculation model. There is a great variety of approaches to thismodel and this paper, in analyzing the subject of customization, focuses on a modified
version of Bucklin’s postponement-speculation model in relation to inventory
management (Bucklin 1965; Bucklin 1966).
The production system that provides a quick and flexible response to customer
orders (in terms of customization) is a significant process innovation in the recent
development of supply chain management. This system enables companies to reduceinventory costs, thereby reducing the level of risk in their production and distribution
operations. The structure can also facilitate competitive advantage, in the form of quick
responses to individual customer orders and the generation of higher added value.
However, customization has long been a part of how many industries operate andis not a particularly new concept. For example, buildings are constructed according toan individual specification, and it has been widely known for some time in theconstruction industry that a quick and flexible response to customer orders is essential
for boosting competitive advantage.

1

In response to the argument that customization is nothing new, it is important topoint out that customization in recent years has been characterized by its emphasis on
reducing inventory costs and risks and in creating higher added value, even in industries
in which the conventional methods of make-to-forecast and mass production havetraditionally been employed. In addition, customization in recent years has beencharacterized by its use of computers in the information processing of orders. These
new methods have made it possible to employ customization in industries in which it
was previously quite difficult due to cost constraints. Furthermore, large-scalecustomization using information technology - specifically termed "mass customization"– is a production system that has been shown in many studies to facilitate competitive
advantage (Davis and Sasser 1995; Robinson and Elofson 2001).
Additionally, many studies associate mass customization with the postponement
strategy of putting off product completion until customer orders are determined (Brown,
Lee and Petrakian 2000; van Hoek 2001). However, a firm consensus has not been reached as

to how to conceptualize customization as postponement or how to handle it inaccordance with the postponement-speculation model. Overcoming the diversity ofcustomization and devising a comprehensive method for explaining this as
postponement is still elusive.
Based on a clear recognition of these issues, this study focuses on conceptualizing
and dealing with customization as postponement and explains a comprehensive
methodology

forapproachingcustomization

on

the

basis

of

Bucklin’s

postponement-speculation model (Bucklin 1966). By noting these points, the paper also
approaches the subject of mass customization using information technology, from the2perspective of costs and the business environment and looks at how this improvescompetitive advantage.

2. Conceptual Development of Postponement-Speculation

According to the postponement-speculation model, customization is the process of
postponing the determination of final form in production until customer orders arereceived. By this line of reasoning, customization is therefore a kind of postponement.
Alderson (1950, 1957) referred to postponement as the activity of leaving changes
to forms and locations of inventory as late as possible and was the first to show
cost-effectiveness through postponement. He presented two reasons for thiscost-effectiveness. First, as the form of a product becomes relatively less differentiated,
it facilitates distribution of a larger lot size with fewer kinds of products, which leads tomore efficient distribution. Second, it is less risky to determine specific forms andcreate inventories that are more likely to satisfy customer needs, once there is morecertainty about what these needs are. In the meantime, Bucklin (1966) defined
speculation as creating inventory as early as possible. He explained that postponement
involves a certain level of optimization due to the efficiency of speculation andincorporated this into the postponement-speculation model. In this phase, Bucklin’s
model focused only on the determination of inventories with regard to the forms andinventories of Alderson’s approach. In addition, according to the model, there was acertain point of equilibrium in the balance between postponement and speculation, in
terms of optimizing the cost of distribution. This constituted a model that showed only3the most properly optimized macro-distribution structure under competitive conditions;
it was not a model that explained competitive advantage.
Following the publication of these studies, there were no notable developments inthis field of study for some time. However, in the late 1980s some researchers began to
conduct studies about supply chain management, using the postponement-speculation
model, which led to a re-evaluation of the concept (Zinn and Bowersox, 1988).
These later studies are characterized by the following two factors: first, the focus
was not on postponement as the change of the equilibrium point in distribution costs, as
Bucklin (1966) had pointed out, but on postponement as a strategy for creatingcompetitive advantage based on postponed production and distribution systems relative
to those used by competitors. This stance focused attention on the improvement ofsystems within individual companies, not on a change in the entire distribution system.
Additionally, recent studies have shown that not every company reaches the optimallevel of postponement-speculation through competition; in fact, there are differencesamong companies in their ability to move to an optimum level, such that only certain
companies end up gaining competitive advantage through postponement.
Secondly, attention focused not only on Bucklin’s model of the postponement of
inventory determination but also on customization and make-to-order as postulated byAlderson (1957). However, this caused the primary focus to be drawn away from
Bucklin’s approach of considering the optimal degree of postponement from the
one-dimensional perspective of delivery time, and moved towards a more complex anddiverse perspective. In this context, discussion emphasized the qualitative differences
involved in postponement and their patterns (Ernst and Kamrad 2000; Yang, Burns and4

Backhouse 2004; Boone, Craighead and Hanna 2007), rather than the best optimization levelof postponement-speculation.
Zinn and Bowersox (1988) classified postponement into five categories: labeling,
packaging, assembly, manufacturing and time. Other than the time category, these areall examples of form postponement, where the form of different products can be basedon customer orders. Van Hoek (1997) largely grouped form postponement into either
assembly postponement, involving products with different formulation and peripherals,
or labeling/packaging postponement, involving products with standard formulation but
different peripherals.
In addition, Pagh and Cooper (1998) focused on form postponement from theperspective of make-to-order and considered direct distribution also to be a form oflogistics postponement. Based on these combinations of postponement, they presented
the following four patterns of postponement strategy: the full speculation strategy; thelogistics postponement strategy; the manufacturing postponement strategy; and the full
postponement strategy.
Meanwhile, Waller, Dabholkar and Gentry (2000) differentiated customizationand make-to-order; that is, they focused on the strategy of not making products until
customer orders were received as an example of make-to-order without customization.
They regarded make-to-order in itself as an object of postponement, and looked atcustomization as a separate factor that further affected costs, both as the result of thecosts of storing inventory and as the result of delivery lead times that changed alongwith the postponement.
These arguments about diverse types of postponement raise two separate points.

5

The first is whether it is possible to consider various activities, such as labeling,
packaging, assembly and manufacturing, as differences in the degree of postponementin addition to being distinct types of postponement; for example, whether it is possible
to express the postponement of packaging as representing a greater degree of
postponement than the postponement of labeling.
The second point is how to approach the difference between customization andmake-to-order. Make-to-order, as a system, can exist with or without customization.
Customization involves postponing the decision about what form the final product will
take, while make-to-order without customization is simply postponement of the creationof certain products on the assembly line. These two patterns have different meanings as
far as postponement is concerned. They should be regarded not as a one-dimensional
form of postponement but as two separate aspects of postponement in their own right.
As a means of addressing these differences in the qualitative diversity of
postponement, this study takes the perspective of quantitative, not qualitative,
measurement of postponement to explain customization as postponement. Based on thisapproach, this paper describes how decision-making about customization as a kind of
postponement is undertaken. In observing decision-making based on the quantitative
measurement of postponement levels, the study examines a modified version of
Bucklin’s postponement-speculation model, in relation to customization.

3. Characteristics of Bucklin-type Postponement-Speculation Model

Bucklin (1966) considered that speculative inventories increase the average

6inventory cost per unit of product, but that they also facilitate an overall cost reduction
by shortening delivery times. By this line of reasoning, in assessing how much
speculative inventory to hold, it is desirable to minimize the two costs that are affectedby the level of speculative inventories. This leads to a rational evaluation of the entire
cost of distribution involving manufacturers, distributors and consumers.
As noted above, Bucklin’s postponement-speculation model leads to a degree of
postponement that facilitates minimization of costs in the entire supply chain. The
model is characterized by two cost curves that show contrasting changes according to
the degree of postponement in relation to the overall cost.
The average inventory cost curve is premised on the positioning of the most ideal
inventory sites and the choice of distribution structure. When the optimal degree of
postponement is determined, the most suitable inventory locations and distribution
structure are chosen. That is, the cost curve is based on centralized or decentralized
inventories and direct or indirect distribution in logistics (Twede, Clarke and Tait 2000).
By using this simulation, the cost curve that traces the lowest cost at a certain
postponement level can be selected. In this way, with upstream postponement (definedas centralized inventories and direct distribution) and downstream postponement(defined as decentralized inventories and indirect distribution) as secondary points, theprimary focus is placed on the determination of time postponement.
Bucklin’s

postponement-speculation

model

is

limitedtoinventory

postponement-speculation, but this study focuses on a modified version that can beapplied to form postponement-speculation. This modified model takes thecharacteristics set out in the following paragraphs from Bucklin’s original model.

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First,

the

model

expresses

postponement-speculation

by

means

of

a

one-dimensional variable and considers increasing the degree of postponement to be thesame as lowering the degree of speculation; full speculation is defined as zero
postponement (Waller, Dabholkar and Gentry 2000).
Second, the model is based on cost curves showing two different shapes according
to different degrees of postponement. One of them is the cost curve concerning process
efficiency and the other is the risk and uncertainty cost curve. As postponement levels
rise, the former increases and the latter decreases.
Third, the minimal cost point is specified on the basis of the total of these costs
and the optimum degree of postponement is determined at that point. That is, thepostponement strategy is intended to facilitate postponement at the most suitable level
rather than maximize postponement for its own sake. A company that has achieved the
optimum level can enjoy competitive advantage until its competitors reach the samelevel.
Fourth, the model focuses on time postponement and is based on the premise that,with regard to upstream and downstream postponement, as defined above, the mostrational choice is made to achieve a certain degree of time-postponement. That is, theoptimal degree of time-postponement measured by certain criteria is based on the mostrational inventory locations and distribution structures in logistics.
Some researchers have conducted studies on postponement strategies thatcombine both geographical dimensions and upstream/downstream dimensions inrelation to form postponement such as customization (van Hoek 1999). For example,
Feitzinger and Lee (1997) focused on Hewlett-Packard’s printer manufacturing8

operations. As its chosen method of customization, the company conducted the final
assembly process at its distribution centers in many parts of the world and distributed
the products to customers from these centers. Feitzinger and Lee (1997) explain that the
point of product differentiation was moved geographically closer to consumers, that is,
postponed. However, Twede, Clarke and Tait (2000) approached decentralized
inventories in terms of logistics speculation and regarded Hewlett-Packard’s
customization as a “postponement strategy” combining manufacturing postponement
and logistics speculation.
In any example like this, the most important point of process innovation is theapproach based on time postponement, which facilitates customization with a fast
response to customer orders. The choice of centralized or decentralized methods ofconducting the final assembly process is directly related to the process innovation thathas the lowest cost. In addition, the choice of direct distribution results from thenecessity of directly negotiating with customers. In these senses, place postponement
and upstream/downstream postponement, which involve the choice of centralized or
decentralized inventories and direct or indirect distribution, are determined rationally on
the premise of a certain level of time postponement (cf. Rabinovich and Evers 2003).

4. Customization as Form Postponement

4.1. The Difference between Make-to-order and Customization
In the postponement-speculation model, bringing forward activities to an earlier
point is called speculation while delaying activities until a later point is called

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postponement. If this formula is applied to make-to-forecast, the method of starting
production “earlier”, before customer orders are actually received, can be regarded as
speculation and make-to-order, which is the pattern of starting production “later”, after
customer orders are actually received, can be considered as postponement. By the same
token, the pattern of determining product specifications and making standardized
products “earlier”, before customer orders are actually received, is speculation; and thepattern of making customized products “later”, after customer orders are actually
received, is postponement. That is, both make-to-order and customization can be looked
upon as postponement.
To determine the point at which to start taking action for customer orders is toconsider how to flexibly respond to the needs of individual customers. That is,
make-to-order and customization can be defined as flexibly responding to the needs ofindividual customers when they are specified, by setting the determination of productforms at as late a point of time as possible.
However, it is necessary to note the difference between make-to-order andcustomization. With regard to product manufacturing, the two patterns of
make-to-forecast and make-to-order exist on the basis of the relative positions ofcustomer orders and the production initiation point. In addition, the pattern of
make-to-order is categorized into make-to-order of standardized products andcustomization (make-to-order of customized products). Customization depends on thecriteria for different product specifications for each customer order. The following two
ways realize different product specifications for each customer order.
The first customization method is to assemble parts in the assembly line, in

10responsetoindividual

customerorders,

on

the

basis

of

prior

designs.

Mass-customization means providing customized products for many customers on thebasis of these efficient operations. The second method is to design specific products,
one by one, in response to each customer order.

This latter method can reflect

customer needs even in the product development process, including the design operation,
which enables the needs to be reflected throughout the entire production process. The
former method, on the other hand, can reflect customer needs only in the final assembly
process.
If these two patterns are integrated as postponement methods, the combination of
make-to-order and customization can be categorized into the following three patterns:(1) make-to-forecast of standardized products; (2) make-to-order of standardized
products; and (3) customization (make-to-order of customized products). An important
point is how to set each postponement level for make-to-order and customization. For
example, make-to-order of standardized products is postponement on the basis ofproduction, but it is speculation on the basis of customization.

4.2. Continuous Measurement of Customization
The next focus is how to continuously measure customization as form
postponement. The continuous scale of the length of delivery time was used in
Bucklin’s postponement level of inventories, but customization cannot be represented interms of length of time. This is because, in the process of customization, the point at
which the form of a product is specified is determined by whether it is before or after
customer orders are actually received and it has no relation to length of time. Therefore,11

in order to convert the time categories of 'before' and 'after' customer orders into
continuous variables, levels of customization are measured by two scales: product ratios
and process ratios.
Product ratios show the percentage of customized products over the entire productrange. For instance, if customized products make up half of all customer orders andstandard products constitute the remaining half, the product ratio of postponement is
50% (cf. Baligh and Richartz 1967).
Process ratios deal with production and procurement costs relating to individual
customer orders as a proportion of the overall production and procurement costs. Thismeans that the process ratio is the cost percentage associated with the customization
process of a certain product, that is, the percentage of production, procurement anddesign costs that are affected by individual customer needs (cf. Yang and Burns 2003).
For example, if customer orders affect the entire process of product design, thechoice of all parts and materials and the assembly line, the product will be completely
customized and the process ratio of postponement is 100%. In contrast, if productdesign is undertaken before customer orders are placed and flexiblility in manufacturing,in response tocustomer orders, is limited to the final process, the process ratio of thisproduct is comprised of the production cost of the final process as a proprotion of theoverall production, procurement and design costs.
Therefore, the variety of postponement, such as labeling, packaging, assembly,
and manufacturing, which Zinn and Bowersox(1988) presented, can be considered as
differences in degrees of postponement, measured by process ratios, rather thanqualitative differences of postponement. For the customization of packaging only, the12the packaging cost as a proportion of the overall cost determines

its postponementlevel and this postponement level is smaller than that of the customization of the entire
production process.
The choice of product ratios and process ratios as postponement levels depends on
industrial competition characteristics and corporate strategies. For example, in asituation where there is a wide variety of customer needs, customization creates a
substantial difference for competitive advantage and product ratios work well as a scale.
On the other hand, process ratios are more effective where significant customization
through production process innovation is required without undermining customer
satisfaction.

5. Determination of the Optimal Degree of Postponement

5.1. Two Cost Curves
It is necessary to consider two cost curves to determine which is more accurate formeasuring the optimal level of customization with regard to the Bucklin-type
postponement-speculation model.
These curves are illustrated by the cost function of C=f (p) with the postponementlevel p set as an independent variable. The cost C, which is a dependent variable,considers not only manufacturers’ costs but also the costs involved in the entire supply
chain, including customers’ procurement costs and suppliers’ production costs. The
optimal level of customization in the entire supply chain can be determined by the totalof these costs.

13

To achieve optimization throughout the entire supply chain, manufacturers do notalways need to control the activities of customers and suppliers, and the best conditions
can be obtained by competition. That is, the model is based on the assumption that eachcompany in the supply chain controls its own activity to form a more efficient system.
The Bucklin-type postponement-speculation model distinguishes between twocontrasting costs according to the degree of postponement. His model of inventories
involves the cost of moving goods to the buyer and the cost incurred by the buyer inholding an inventory (Stern and El-ansary, 1988). However, the model of customization
involves the processing cost of production tasks (production costs) and the costs
concerning demand uncertainty (uncertainty costs). That is, production costs anduncertainty costs are devised as primary costs that change along with the postponementlevel, and other cost conditions are determined independently from the postponementlevel. Evaluating the sum of these two costs makes it possible to obtain the
postponement level with the lowest average cost.
Based on these premises, the following section examines the form of two cost
curves that illustrate the changes in those costs according to the postponement level ofproduct ratios and process ratios.

5.2. Cost Curves Concerning the Product Ratios of Customization
5.2.1 Production Costs
With regard to the relationship between product ratios of customization andproduction costs, the higher the ratios are, the more production costs will increase. Two
factors can affect this mechanism. The first is the inevitable effect of make-to-order on

14

customization. In make-to-order, its difficulty in predicting customer orders is likely toraise the production cost. When a large number of orders are concentrated on aparticular period of time, manufacturers are required to have proper production
capability for handling the bulk of these orders. In contrast, when there are only a small
number of orders, they have much surplus capacity for production. By this line ofreasoning, the higher product ratios become, the more difficult it is to produce goods inline with production plans, which leads to higher production costs.
The other factor is the effect of lower production efficiency due to the stream ofdiverse products in the assembly line of customization. Customization is the method offlexible response to individual customer orders of various product specifications. This
undermines the economy of scale in the production operation and requires a higher levelof production costs.
Furthermore, it is expected that the higher product ratios become, the higher theincrease rate of production costs will become. This is because the greater diversity of
products made to order in the assembly line requires a higher level of expertise inproduction capability. In addition, the process of customization after receiving customer
orders requires a lot of time. In general, in situations in which customers are lesstolerant of having to wait longer to receive their product, manufacturers face strong timepressures and are obliged to employ quicker production methods, which escalates costs(Zinn and Bowersox 1988). In particular, in industries where a huge number of orders

concentrate on a particular period of time, manufacturers are more likely to reach thelimit of their production capability, which causes a sharp increase in production costs.
As a result, generally speaking, the production cost curve, which depicts the rate of15

increase in production costs in relation to the product ratios of customization, is likely toshow a rising, concave-up shape.
5.2.2 Uncertainty Costs
Uncertainty costs are intended to deal with the uncertainty of customer demand in
the case of standardized products that do not involve customization. In this scenario,
manufacturers are burdened with the costs and risks of diverse inventories resulting
from a great variety of customer orders, as well as with costs associated with predicting
demand for customer orders (Yang, Burns and Backhouse 2004). In addition, the uncertainty
cost on the part of customers includes opportunity cost due to the supplier’s failure toprovide goods that meet their needs, as well as other costs for additional processing by
the customers.
Uncertainty costs usually emerge in relation to the production of standardized
products, as opposed to where full customization is employed, as customization
provides products in response to individual customer orders. As the product ratios ofstandardized goods increase and the product ratios of customization decrease,
uncertainty costs increase.
Additionally, if the product ratios of standardized goods are higher, it is rational tostandardize orders with easier predictability, lower inventory risks and lower demandprediction costs. For example, if steady receipt of orders from customers with similar
preferences is expected, the strategy of preparing inventories by manufacturing
standardized goods is more likely to be used (Rabinovich and Evers 2003).
In this way, if things start off with a 100% product ratio of customization and the
product ratios of standardized goods are subsequently raised, the next step is to move on

16

to orders with difficult predictability. This means that, as the product ratios ofcustomization decrease, uncertainty costs rise increasingly. Therefore, uncertainty costs
will depict a decreasing concave-up curve.
5.2.3 Total Cost
Supposing these above-mentioned conditions are in place with respect toproduction costs and uncertainty costs, then two cost curves can be depicted andconcave-up cost curves can also be obtained with regard to the shape of cost curves
based on the total of these two costs, as illustrated in Figure 1.
The lowest point E of the total cost curves shows the postponement level X thatcan be the most cost-saving, and setting the product ratios of customization at that levelis likely to be the most efficient and competitive. That is, if a company’s product ratios
of customization are higher or lower than this optimization level, they will beshouldered with higher costs, which will affect their competitiveness. As a result, thecompany will adjust its operation to the optimized level.

Costs

Total costs
E
Production costs

Uncertainty costs0%

Postponement level
100% (Product ratio of customization)

X

Figure 1. Optimal postponement level of customization17

As noted above, the total cost curve concerning the product ratios ofcustomization has a concave-up shape. However, if the change in either product costs or
uncertainty costs greatly affects the change in the total cost, the total cost curve may
show as either increasing or decreasing. This means that the product ratio ofcustomization can be 0% (100% standardized products) or 100% (100% customized
products), though this is less likely to occur in the postponement-speculation of
inventories.
In fact, there are industries whose products are all standardized or customized. In
the case of industries whose products are all standardized, the production costs forcustomization increase more and the effect of reductions in uncertainty costs throughcustomization is smaller (Figure 2). Conversely, with regard to industries whose
products are all customized, uncertainty cost increases matter even more than do
production cost reductions through standardization.

Costs
Total costs

Production costsE0%

Uncertainty costs
Product ratio of customization
100%

Figure 2. An example of １００％ standardized products

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5.3. Cost Curves Concerning the Process Ratios of Customization
5.3.1 Production Cost
Product ratios have been focused on so far, but this section looks at customization
as form postponement from the perspective of process ratios. Just as in the case of
product ratios, production costs and uncertainty costs can also be used for theexamination of process ratios.
The cost of production increases as process ratios become higher, that is, as thepercentage of customization increases. The main reason for this is that the
manufacturing of customized products is more complex and planned mass production istherefore more difficult.
In addition, if the process ratio of customization is raised, the operation usually
starts with processes in which customized production and procurement are technicallyeasier. At the same time, the higher the process ratios of customization are (asproduction processes after receipt of customer orders increase), the greater the timepressures are , when customers are less tolerant of delays.
Therefore, since the rate of increase of production costs gradually becomes higher,
the production cost curve is likely to show an increasing and concave-up shape in terms
of process ratios.
5.3.2 Uncertainty Cost
The uncertainty cost increases as customized process ratios become lower because
inventory risks and demand prediction costs increase when a make-to-forecast approachis adopted before customer orders have been received. Additionally, standardized
production and procurement is likely to be undertaken when the risks associated with19

potential prediction failure are smaller. For instance, standardization is likely to beappropriate where production and procurement in advance can be transferred to otherproducts or where work-in-process inventory costs are smaller. Therefore, theuncertainty cost curve is likely to show a decreasing concave-up shape in terms ofprocess ratios.
5.3.3 Total Cost
In general, the two curves concerning production and uncertainty costs in relation
to process ratios depict a similar shape to those seen in relation to product ratios, andobtaining the total cost curve leads to the process ratio at which the total production
costs and uncertainty costs are lowest.
This suggests that it is possible to determine the degree to which the production
process should be customized in terms of process ratios, with the intent of creating themost efficient system on the basis of conditions concerning production technology andcustomer demand.
Furthermore, all materials are seldom customized and 100% customization interms of process ratios is relatively rare. However, products that are 100% standardized;that is, products with 0% customization, just like popular commodity goods, arerelatively common. In the case of completely standardized products, uncertainty cost
reductions through customization become smaller. As a result, the total cost curve isconcave-up and increasing, which means that the total cost is lowest in the case ofcustomization with a 0% process ratio.

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6. Customization Based on Information Technology

In essence, the Bucklin-type postponement-speculation model is intended to show
the competitive equilibrium point. However, the modified model set out in this paper
explains the creation of competitive advantage based on process innovation. Morespecifically, it means that, by making changes to production costs through process
innovation in a certain industry, total costs can be reduced and, at the same time, thepoint at which these total costs are lowest will move in relation to the degree of
postponement. Through this reduction, particular companies can enjoy competitive
advantage over other companies using existing systems, until competitors reach theirlevel.
For example, one of the factors that affects the introduction of customization invarious industries is the use of information technology in production systems. Thisphenomenon is explained by the Bucklin-type postponement-speculation model in thefollowing section.
The development of information technology facilitates the sending of orders
processed by computer to an automated production line, which assembles parts
according to the information contained in the order. In addition, this technology can also
be used to process information about the procurement of parts. It facilitates a quick andefficient make-to-order system, with minimum inventories of parts, through the sharing
of information concerning production and inventories with suppliers. This
information-technology-based innovation in production systems boosts operational
capability on the production line of both manufacturers and parts suppliers, which in21

turn controls the rising slope of the production cost curve. For example, if the
production costs of customization are lowered by a certain ratio, the reduction effect ofcomputer-aided information processing increases as the product and process ratios ofcustomization increase. Additionally, economies of scale work well for information
systems and, as the product and process ratios of customization increase, greater cost
reduction is likely, compared with conventional methods.
As Figure 3 illustrates, the optimal postponement level transfers to postponement
along with the change in the production cost curve, through the introduction ofcomputer-aided production systems. By this line of reasoning, the ratio of customized
products is raised in terms of product ratios; and the ratios of production andprocurement in response to customer orders increase in terms of process ratios, leading
to more customized product specifications.
In addition, the introduction of computer-aided production systems facilitates thelowering of costs in relation to the optimal degree of postponement, as well as
facilitating the postponement of customization itself. These process innovations are
expected to take place in many industries. However, a great variety of know-how isnecessary for the introduction and efficient operation of an innovative system andcompanies that are first to launch these new systems are likely to enjoy the benefits ofefficient customization compared with their competitors (Lee 1998; van Hoek, Vos andCommandeur 1999; Yang and Burns 2003). Large-scale customization based on costadvantage constitutes the core essence of mass-customization.

22

Costs
Old total costs

E

Old production costs

C

New total costs
E'C'

New production costs

0%X

Uncertainty costs
100% Postponement level
X'(Product ratio or Process ratio)

Figure 3. Impact of information technology on form postponement

Moreover, in a situation where customer demands in a certain market segment arebecoming diversified, the uncertainty cost concerning these customers is different fromthat of other customers and a greater degree of postponement is likely to be the ideal.
The customization strategy of targeting these customers will be effective.
In this way, the postponement-speculation model explains how particularcompanies can gain competitive advantage through customization in an industry inwhich the conventional pattern of make-to-forecast was predominant.

7. Conclusion

The preceding studies about postponement-speculation sought to explain thequalitative diversity of all forms of postponement, including physical distribution, by
typological models. Several patterns of postponement strategies have been presented on23

the basis of those models.
However, a notable point about postponement strategy concerns not only the typeof postponement, but also the degree of postponement that should be pursued under any
given set of circumstances; that is, what the optimal degree of postponement is. To
examine the latter subject, it is necessary to set a theoretical framework to consider the
optimal degree of postponement in relation to individual forms of postponement, suchas physical distribution, make-to-order of standardized products and customization.
Based on this critical thinking, this study focuses attention strongly on
customization, regarding it as a postponement level and has examined the optimal levelfrom the perspective of the Bucklin-type postponement-speculation model. This modelis based on the assumption that the total cost to achieve a certain postponement level,measured by the scales of product ratios and process ratios, is the sum of all production
costs and uncertainty costs. The paper has drawn a framework in which the optimal
degree of postponement can be judged on the basis of those two costs.
This model shows how a company can achieve postponement and cost advantage
by making changes to production costs through computer-aided innovations in the
production system. In addition, the model is helpful in considering the targeting of
postponement strategy, with a focus on different variables affecting uncertainty costs
according to the customer segment in question. The model can also explain the
differences in the diffusion of postponement strategy by industry, on the basis of two
different cost conditions.
This study focuses on customization as form postponement, but the optimal levelof "customization-free make-to-order" can also be explained by a similar model, when24the interpretation of production and uncertainty costs is slightly modified. That is, it ispossible to speculate on where the optimal level of product and process ratios of
make-to-order is, on the basis of production and uncertainty costs. This subject ofmanufacturing standard products in response to customer orders is significant,especially when considering the issues of JIT (just-in-time) and QR (quick response) in
the supply chain.
[2010.3.9 968]

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