An Analysis of Manufacturers??? Supply and Demand Uncertainty

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International Journal of Production Research

Vol. 49, No. 10, 15 May 2011, 30233043

An analysis of manufacturers supply and demand uncertainty based on

the dynamic customisation degree

Shen-Tsu Wang*

Commerce Automation & Management Department, National Pingtung Institute of Commerce,1F, No. 6, Lane 79, Sec. 3, Jiangning Road, Banqiao City, Taipei County 220, Taiwan, ROC

(Received 6 October 2009; final version received 23 February 2010)

Diverse demands regarding products are common; however, manufacturersusually cannot respond immediately to meet such changes upon demand, andthus, customer satisfaction tends to be reduced. Notebook computer manufac-turers adopt a production mode of mass customisation; hence, a certain degree ofdynamic customisation measurements, inherent in different supply chain models,allow manufacturers to evaluate costs and profits in advance. The application ofthe model, as proposed in this study, indicates that the most important factor ofthe customisation degree is product price. The dynamic customisation degree isadjusted based on monitoring indicators, which requires less total cost andproduces greater accuracy in forecast results regarding the prediction model ofcustomer demands. This study develops a dynamic customisation model for totalproduct profits, inventory cost of semi-manufactured products, shortage costsand buffer inventory costs, which are affected by the degree of dynamiccustomisation of the products. It also analyses the supply and demand

uncertainties of the Direct Shipment of the Manufactured Model, as well as theDoor-to-Door Direct Shipment of the End User Model in the notebook computerindustry, as the criteria with respect to a firms customisation degree, costs, andprofits in different supply chain mode operations.

Keywords: notebook computer manufacturers; dynamic customisation degree;supply and demand uncertainties

1. Introduction

Supply chain demand management indicates that diverse demands result in varied products

and components. Manufacturers usually cannot immediately meet these changeable

demands and, thus, customer satisfaction is reduced. Manufacturers lose customers andmarkets and their supply of quality products significantly declines. Since the markets cater

to the retailers and end customers, customer demands become personalised. Many

manufacturers provide their products through dynamic customisation and marketing.

Thus, customers can purchase totally customised products or equip those products in a

selective environment. For instance, they can purchase Dell computers over the Internet.

Chen and Wu (2006) suggested that in the trend to globalisation, core competency can be

defined from the perspective of the supply chain and that core competency should include:

strategic planning, manufacturing process innovation, supply chain management,

logistics management, quality management and R&D innovation ability. In order to survive

*Email: [email protected]


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and be successful, firms must rely on unique competency. One of the definitions of quality

management is that quality means suitability; all products and services are produced at

different degrees and levels. In order to meet customer demand, these different degrees and

levels are constructed intentionally (Montgomery 2005). In order to study the uncertainty of

customers and the supply chain, firms must first recognise the targets in order to segment

customer demand and meet the uncertainty of the supply chain. These demands allow firmsto set up the cost, customisation degree and service demand (Chorpa and Meindl 2004).

Thus, this study has developed a dynamic customisation degree model to allow firms to

recognise the relationships among customer demand, corporate profits, manufacturing cost

and customisation degree.

2. Literature review

With regard to the issue of solving supply and demand uncertainties, Baba et al. (2009)

focused on a single-stage, single-item inventory system, which included non-stationary

demands and lead-time uncertainties. A dynamic reorder point control policy is analysed,

as based on the proposed new approach, with parameters determined upon a given target

level of cycle-service. The empirical results demonstrate that benefits arise from a policy

that allows insights to be gained into other pertinent managerial issues. Kumar and Wilson

(2009) investigated the link between off-shoring, postponement, and inventory, and

applied a set of real-world data to a simplified product, from which the total cost benefit of

each postponement was examined. An efficient method was identified for determining

which uncertain terms in the combined equations would dominate the alterations of the

inventory levels for any given strategy. Wong et al. (2009) analysed postponement based

on the positioning of the differentiation points and the stocking policy. The results of

numerical experiments showed how different operational parameters could influence thechoices of optimal configurations, the preference for early or late postponement, and the

relative savings obtained from employing postponement.

With regard to the models of dynamic customisation degrees, Lau and Lau (2003)

validated the influence of different demand models on inventory prices, and suggested that

analytical results be based on five different demand models. Balkhi and Benkherouf (2004)

proposed a model based on the correlation between demand and inventory degrees, and

between inventory holding costs and inventory, and in addition, assumed that the attrition

rate in the limited planning period was fixed, the replenishment cycle was fixed, and that

shortages were not permitted to influence replenishing the inventory. Brun and Zorzini

(2009) aimed at investigating the relationships existing between postponement andmodularisation practices, as actually implemented by Italian companies, and the

contextual factors inherent in product features. This research adopted a multi-case

study strategy, with statistical techniques applied for data analysis. Two main factors are

identified, namely, product/process customisation and product/process complexity; and

four customisation strategies, differing in terms of supply chain structure, are analysed.

3. Construction of a dynamic customisation model

This section introduces the limitations of the research scope, parameter definition and

model construction, and multiple and non-linear customisation model construction, plusthe dynamic construction of customisation degree.

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3.1 Research scope limitations

(1) Issues in the organisations have not been included.

(2) After-sales service has not been included.

(3) Customisation degree reflects on the model cost.

3.2 Parameter definition and model construction

Salvadoret al. (2004) divided the supply chain into high and low customisation degrees,

according to the types and numbers of products and the customers agreed upon waiting

time. Each product that the firm produced (i 1, 2, . . . , n) underwent Nprocesses during

manufacturing to completion. In order for manufacturing to be successful, a buffer is set

before each manufacturing process, which secures inventory, assuring the manufacturing

process will not stop due to shortages. Prior to manufacturing process M, each product

shared identical manufacturing processes; however, a difference occurs during theM 1th

manufacturing process. According to Lee and Tang (1997), this model assumed the servicedegree as more than 90% satisfied, as corporate quality guaranteed a high service degree

(Hart 1995). In order to fulfil this service level and meet the different demands of

customers, the costs and parameters that are mentioned in Section 3.2.1 had to be

maintained at a certain level (Zinn and Bowersox 1988, Pagh and Cooper 1998).

3.2.1 Parameter definition

Dki average demand of product iin the supply chain upon customisation

degreek, i 1, 2,. . .

, n;ki demand standard deviation of product i in the supply chain upon

customisation degree k, i 1,2, . . . , n;

Aki average shortage cost of product i in the supply chain upon


Eki average supply amount of product i in the supply chain upon


tkmM assigned cost for each unit of the mth manufacturing process, in

addition to the Mth co-manufacturing process in the supply chain,

based upon customisation degree k; total manufacturing process

includes Nstages, m 1, 2, . . . , N;

HkmM inventory holding costs for each unit of the mth manufacturing

process, in addition to theMth co-manufacturing process in the supply

chain, based upon customisation degree k; total manufacturing

process includes Nstages, m 1, 2, . . . , N;

LkmM lead time of the mth manufacturing process, in addition to the Mth

co-manufacturing process in the supply chain, based upon customisa-

tion degree k; total manufacturing process includes N stages,

m 1, 2, . . . , N;

SkmM ordering costs of the mth manufacturing process, in addition to the

Mth co-manufacturing process in the supply chain, based upon

customisation degreek; total manufacturing process includes Nstages,m 1, 2, . . . , N;

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PkiM average price for productiwith Mth co-manufacturing process in the

supply chain, based upon customisation degree k, i 1,2, . . . , n;

W total production,WPn

i1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2DkiS

ki=H

ki

p , i 1, 2, . . . , n;

z security factor;

ckm in a supply chain of customisation degree k, when the mth

manufacturing process becomes the increased/reduced average invest-

ment cost of the co-manufacturing process, total manufacturing

process includes Nstages, m 1, 2, . . . , N;

U customer demand without the consideration of customisation;

R sensitivity to customer demand on price, 0 R 1;

pDki 4Eki in the supply chain upon customisation degree k, with regard to

product i, the probability that the average demand is more than the

average supply, i 1, 2, . . . , n;

c1 total of basic manufacturing cost;

d prosperity, d 1,2, . . . , 5;

P(d) probability of prosperity,d 1,2, . . . , 5.

3.2.2 Description of individual models without consideration of customisation degree

(1) Total revenue of product (F), as shown in Equation (1):

Xni1

PkiM W: 1

(2) Inventory cost of semi-manufactured goods (HI): upon the influence of lead time

in supply chain of customisation degree k, the semi-manufactured goods inventory

cost, as shown in Equation (2):

HkmM LkmM W: 2

(3) Shortage cost (SI), as shown in Equation (3):

Xni1

Aki pDki 4E

ki D

ki E

ki: 3

(4) Buffer inventory cost (BI): according to Peterson and Silver (1979) and Riezeboset al. (2003), as shown in Equation (4):

HkmM W

2 z

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiLkmM 1

q : 4

(5) In supply chain upon customisation degreek, when producti(i 1,2, . . . , n) share

M co-manufacturing process (05M N), total inventory cost (HI BI), as

shown in Equation (5):

HM XMi1

Hkii Lkii WXMi1

Hkii W

2 zk1,2,...,M

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiLkii 1

q : 5

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Total inventory cost of (HI BI) when product i is in individual production

(N M), as shown in Equation (6):

HNM XNM

iM

Hkii Lkii W X

NM

iM

Hkii W

2 z X

NM

iM

ki ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiLkii 1q

" #: 6

(6) Manufacturing process divided into N processes; business cost (DI) of each

manufacturing process, as shown in Equation (7):

XNm1

tkm W: 7

(7) Total of basic manufacturing cost, as shown in Equation (8):

cIHIBISIDI: 8

(8) Uncertainty demand function specification.Since the per-unit time submits to the Poisson distribution, as assumed, the

probability distribution of the demand quantity can be shown by the following

normal distribution equation (Chu and Lin 2004), as in Equation (9):X1n0

Pnn fT

x S n

, 9

where n and Xare independent stochastic variables; n is the variance; and X can be a

variance or a series (X 0), if stochastic variable Y is the sum of n and X, Y is the

probability distribution. The pmf of discrete stochastic variable n is Pn

(n), the pdf of

continuous random variable Xisfx(x), and we define the Z-transform ofPn(n) as P

T

nZ,and the S-transform offx

(x) as fTnS.

3.3 Construction of multiple and non-linear customisation models

This study included a basic profit model based on Mukhopadhyay and Setoputro (2005),

but revised it with respect to the direct-sale suppliers profit, setting aside the suppliers

profit function and substituting different demand functions.

(1) Multiple linear demand with consideration of customisation degree

It was assumed that the demand function was a multiplying linear demand and that

customer demand was completely affected by customisation degree. This depends

on the prices of linear demand directly multiplied by the customisation degree.

Customer demand upon consideration of customisation degree, as shown in

Equation (10): Xni1

Dki URPki k: 10

Corporate profit, as shown in Equation (11):

Fc1 c1kURPk

i

k: 11

@2=@k2 2c1URPki: Since c1 4 0, @

2=@k25 0:



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Maximum profit and the fittest degree of customisation, as shown in

Equation (12):

k c1 P

ki

2c1: 12

Comparing Door-to-Door Direct Shipment with End User Model, Direct

Shipment after Manufactured Model revealed a lower customisation degree.

Thus, it was suitable for the multiple linear demand model.

(2) Non-linear demand upon consideration of customisation degree

It was assumed that the influence of the customisation degree on demand was

increasing. When customisation increased, demand also increased. It affected the

price-dependency demand equation. When the customisation degree changed,

the demand would considerably change. With the increase of customisation degree,

the equations of basic linear demand would increase by k. We assumed that the

demand was one to square times of the basic linear demand. Demand mode was

the 1 k equation of the demand depending on the prices.

Customer demand (with consideration of customisation degree), as shown in

Equation (13):

Xni1

Dki URPki

1k: 13

Corporate profit as shown in Equation (14):

Fc1 c1kURPki

1k: 14

@2

@k2 URPki

1k logURPki2c1 c1Pki c1k logURP

ki:

SinceURPki4 0, the condition of@2=@k25 0 is:

c14 Pki logURP

ki

2 1k logURPki

maximum profit results. The fittest customisation degree is shown in Equation (15):

k 1Pkic

1

1

logURP

k

i

: 15

Because of the trend towards mobile and wireless electronics, the notebook computer

industry has kept up with PCs in terms of calculation functions. Besides, with the

continuous low-price strategies of companies such as HP, Dell and Acer to replace PCs,

the demand for notebooks went from around 20 million in 1999 to 30 million in 2002 and

to 40 million in 2004. The growth was stunning. In 2005, the demand reached 60 million.

The application of demand should be estimated by a non-linear demand model. With the

orders of brand firms in 2005, the global share of the notebook computer industry in

Taiwan broke through 80% (Chen 2006). The product demand in the case of company B

also revealed the growth of non-linear demand. Door-to-Door Direct Shipment to End

User Model revealed a higher customisation degree. Thus, it was more suitable for thenon-linear demand model.

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3.4 Arrangement of significance of assessment factors through grey relational analysis

After the calculation of customisation degree in Section 3.3, grey relational analysis can be

used to probe the significant factors on customisation degree. The degree of relationship

among subsystems or elements could be evaluated through grey relational analysis (Deng

1982), and important factors influential to the development trend are then found to

influence the major features of the system as in the following steps.

Step 1: Normalise original data: normalise by dividing the original data xik with the

mean value of this sequence, as shown in Equation (16):

rik xikPN

k1xik

N

, i a, . . . , d, k A, . . . , N: 16

Step 2: Designate the standard sequence and calculate the difference sequence: take the

mean value as a standard sequence, i.e., sequence 0, the difference sequence D0ikindicates

the absolute difference of elements k between the other sequence i and the standard

sequence 0, as shown in Equation (17):

D0ik r0k rik , i 1,2, 3, . . . , k A, . . . , N: 17

Step 3: Calculate maximal difference Dmax and minimal difference Dmin, as shown in

Equations (18)(19):

Dmaxmaxi,k

D0ik 18

Dmin mini,k

D0ik: 19

Step 4: Calculate grey relational coefficient 0ik: the relational coefficient 0ik is

defined below, of which &is the adjustment factor, as shown in Equation (20):

0ik Dmin & Dmax

D0ik & Dmax: 20

Step 5: Calculate the grey relationship 0i between every sequence and the standard

sequence: the grey relationship 0ias shown in Equation (21):

0iXNkA

0ikN

: 21

Step 6: Conduct sequencing according to the grey relationship.

3.5 Dynamic construction of customisation degree

With regard to assumptions on business based on the total scores of monitoring indicators

of the Council for Economic Planning and Development (Republic of China), this study

divided business into five levels: blue (recession), blue and yellow (unsatisfactory business),

green (stationary business), yellow and red (slightly prosperous business) and red (overlyprosperous business).



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Based on the significant factors on customisation degree obtained by grey relational

analysis, this study compared manufacturing costs of manufacturers fixed and dynamic

customisation degrees. With different business and customisation degrees, there are

different total related costs. k of minkc1k, d is an optimal solution k. Thus, firms can

develop customisation degrees for different kinds of business.

External environment is unpredictable and firms cannot immediately change themanufacturing model. The probability of different kinds of process d is P(d). After the

firms select customisation degree k, E(k) means that regardless of business, the total

expected cost upon customisation degree k is E(k), and the total expected cost of static

decision minkEk E, as shown in Equation (22):

Ek X

dfd1,...,d5g

Pd c1k, d: 22

When firms can change the manufacturing model without restriction, B refers to the

total expected cost of a different customisation degree k in different kinds of business.

Total expected cost of dynamic decision minkBk B, as shown in Equation (23):

Bk X

dfd1,...,d5g

Pd c1k, d: 23

When B5E, it demonstrates that if firms can immediately adjust the customisation

degree and supply chain structure with the change of business, they will minimise the cost.

This study used two notebook computer manufacturers as examples to compare the

construction of customisation degree upon the supply chain structure of static and

dynamic decisions in order to function as the criterion for the firms when encountering

uncertain business.

3.6 Comparison of different models with regard to customer demand prediction

This study adopted one of the models proposed by Lau and Lau (2003) and compared the

customisation degree of different demand models (Equation (24)) with multiplying linear

and non-linear demand models of this study, and evaluated the correctness of the

prediction model by tracking signals controlled by the said model, as shown in

Equation (24):

d abp, a and b are positives, 24

where:

pa

b;p

m ab

2

:

where:

m unit manpower cost;

p unit retail price;

d prediction of the amount of customer demand.

Bias estimation of predictors in this study is calculated by tracking signals (TS)

controlled by the prediction model. TS is the rate after dividing mean absolute deviation(MAD) by total accumulated bias, as shown in Equations (25)(28): upper and lower

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values of the control are between 4 and 4. is between 0.1 and 0.5. In this case, is 0.2

(Heizer and Render 2004):

Bias, e:

ePni1AiFi

n : 25

Mean absolute deviation, MAD:

MAD

Pni1 AiFij j

n 26

MADt MADt1 ej jMADt1 : 27

Tracking signals, TS:

TS

Pe

MADt: 28

Where:Ai true value;

Fi prediction value;

n period;

t iperiod.

4. Description of cases

The notebook computer industry involves issues related to supply and demand

uncertainty. This study has suggested two operational models for the notebook computerindustry (Companies A and B), as described in Sections 4.1 and 4.2, to function as the test

criteria of the model in Section 3.

4.1 Door-to-Door Direct Shipment to End User Model: notebook computer

manufacturing industry

The Door-to-Door Direct Shipment to End User Model was formed due to the demands

from OEM (original equipment manufacturing) customers for service close to their

markets. The aim was to reduce lead time and the loss of inventory price reductions by

offering customised finished goods and door-to-door shipments to OEM customers. Thismodel mainly uses air freight. Transportation costs are higher to achieve timeliness. The

key to the supply chain management in this model is the management relationship among

OEM manufacturers, logistics companies and customers, as shown in Figure 1 (the solid

lines indicate logistical flows whereas the dotted lines indicate information flows).

According to this model, all the production processes are completed by Taiwanese

OEM manufacturers and all the products are directly shipped to the purchasers. This is

now the model most often adopted by Taiwanese notebook manufacturers. In this model,

international brand-name companies negotiate supply issues with component suppliers,

and the procurement of components is then taken over by Taiwanese manufacturers. As

this model involves transportation services, it may boost profits. For international brand-name companies, this model eliminates the burden of inventory.



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Taiwanese manufacturers do not ask their parts suppliers to set up dispatch centres

close to their manufacturing or assembly facilities. When Taiwanese manufacturers have

demands for components, the dispatch centres of their suppliers ship products directly to

the Taiwanese assembly plants for the final manufacturing or assembly of products.

Therefore, with this model, distributors or consumers place orders with international

brand-name companies to fill their demands and international brand-name companiestransfer these orders to local assembly plants established by Taiwanese manufacturers

close to the markets. Regional assembly plants complete the product assembly and ship

products to end-consumers directly through professional logistics companies.

Before May 2002, the factories of Company A produced nearly all the computers in

Taiwan and China. Other parts that required localisation, such as keyboard and packaging

parts, were assembled in local facilities. After August 2002, in order to reduce R&D and

part costs, as well as shorten the shipment time, Compaq asked its suppliers of key

components, including hard-disks and CD-ROMs, to modularise their production by

developing modules common to many models. The previous model of the shipment of the

completed computers was changed into the separate shipment of bare-bones andmodularised components to be assembled by the assembly plants. The first to try this

model was a factory in Scotland. However, factories in Asia Pacific and the US still

shipped completed computers most of the time. Only a few shipments to US government

agencies are in modularised operations. For Company A, such a change required a strong

capability in assembly and testing in all the assembly plants. It meant that operational

expenses were higher. As far as the order-fulfilling efficiency was concerned, if parts could

be ready, Company A could manage to complete orders within two days without major

difficulties. However, as the readiness of parts from suppliers is related to the shipment

forecasts provided by OEM customers, if the discrepancy between the forecasts and

timing of orders is too significant, it is not easy for suppliers to fully comply, which affectsorder-fulfilling efficiency.

Figure 1. Door-to-Door Direct Shipment to End User Model of operational method.

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4.2 Direct Shipment after Manufactured Model: notebook computer

manufacturing industry

The goal of the Direct Shipment after Manufactured Model is to reduce lead time and the

losses associated with inventory price reductions. In addition, OEM customers demand

customised services and door-to-door direct shipment. This model often employs a

combination of air freight and marine transportation. It is a major challenge to deal with

operational cycles and coordination of logistics, as shown in Figure 2 (the solid lines

indicate logistical flows whereas the dotted lines indicate information flows).

The greatest difference between the Direct Shipment after Manufactured Model and

the Door-to-Door Direct Shipment to End User Model is that the former accepts orders

from international brand-name companies, but provides only bare-bones products and

does not complete the final assembly. Products are shipped to the assembly plants of

international brand-name companies for final assembly and software installation. The

international brand-name companies ship products directly to distributors or consumers.

Dell is one company that adopts this model with its Taiwanese OEM manufacturers.

To reduce the loss of inventory price reduction for components, Dell came up with the

concept that notebooks should not be shipped out of factories in complete units. Rather,

Company B should work with other suppliers to develop ways to modularise hard-disks so

that it was easier to insert CPUs into motherboards during the subsequent assembly

process. Therefore, Company B changed from the shipment of completed notebooks to

separate shipments of bare-bones and component modules to the assembly plants assigned

by Dell so that Dell does the final assembly and software installation. This model not only

created more flexibility for Company B, but also enabled it to avoid the risks associated

with the price reduction of components. This was the result of Dells creativity, Company

Bs research efforts and the support of the suppliers. Company B was able, within a very

short time, to ship notebooks of different models from its factories within two or threedays. This impressive outcome was the result of the modularisation of key components.

This strategy was, in fact, very similar to that employed in the modularised production

of desktops. Hard-disks, CPUs and CD-ROMs are such examples. When these

manufacturers worked with Dell in 19951996, Company B had already started the

development of components with suppliers. Later, Dell used this same model of the

modularisation of key components. Under this model, after receiving orders from

customers, Company B assembled the modularised components and conducted product

tests; the preparation of the shipment was largely done. Before the placement of orders,

OEM customers always provided the forecast shipments of individual models for different

regional markets based on a 16-week outlook. Therefore, the production of modularisedcomponents was a preparation to allow more buffers in the final shipment of products.

4.3 Comparison of different shipment models

This study decided to compare the quantitative parameters of the Door-to-Door Direct

Shipment to End User Model (producing eight kinds of products) and the Direct Shipment

after Manufactured Model (producing two kinds of products), as shown in Table 1.

According to Sections 4.1 and 4.2, as well as Tables 2, 3, and 4, unit product prices,

processing costs, inventory cost, and lead time of purchase and customer demanddeviation estimated by the Door-to-Door Direct Shipment to End User Model were



12/22Figu

re2.

DirectShipmentafterManufacturedModelofoperationa

lmethod.

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Table 1. Parameter relationship between Door-to-Door Direct Shipment to End User Model (1)and Direct Shipment after Manufactured Model (2).

Product

price

Processing

cost

Lead time

of purchase

Inventory

cost

Standard

deviation of

customer demand

(1) Phi vi P

h1i

vi1

i 1,2, . . . , 8

thim xi th1m

xi1

i 1,2, . . . , 8

m 1, 2

Lhim yi Lh1m

yi1

i 1,2, . . . , 8

m 1, 2

Hhim zi Hh1m

zi1

i 1,2, . . . , 8

m 1, 2

h1 , h2 , . . . ,

h8

(2) Pl1 Pl2

tl1m tl2m

m 1, 2

Ll1m Ll2m

m 1, 2

Hl1m Hl2m

m 1, 2

l1, l2

Relative

relationship

Phi 1 Pli

1 1th1m 2 t

lm

2 1

m1, 2

Lh1m 3 Llm

3 1

m1, 2

Hh1m 4 Hlm

4 1

m1, 2

h1 h2, . . .

h8

l1 l2

Table 3. Direct Shipment after Manufactured Model (non-linear demand) customisation degreeand grey relational ranking.

Factor

Years

2000 2001 2002 2003 2004 2005 2006 0i (ranking)

k 37.5% 51.5% 70.4% 83.3% 86.4% 87.1% 87.9%HI 3380 3600 3690 3720 3800 3900 3910 0.69(3)BI 1780 1890 1930 1990 2000 2090 2130 0.71(1)SI 1200 1050 950 900 880 770 730 0.57(5)DI 8500 7610 7260 6960 6800 6700 6680 0.61(4)PkiM 24,000 24,800 26,800 28,000 28,200 28,250 28,320 0.71(1)

Table 2. Door-to-Door Direct Shipment to End User Model (multi-linear demand) customisationdegree and grey relational ranking.

Factor

Years

0i (ranking)2000 2001 2002 2003 2004 2005 2006

k 56.4% 65.5% 69.8% 80.3% 85.1% 88.6% 92.6%HI 3480 3680 3720 3780 3820 3930 4300 0.82(3)BI 1800 1900 1960 2010 2060 2110 2170 0.83(2)SI 1000 950 850 740 710 650 630 0.56(5)DI 8520 7620 7280 6980 6810 6730 6700 0.66(4)PkiM 31,500 32,700 33,100 35,200 36,210 37,200 39,360 0.87(1)



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higher. Thus, based on the calculation of Equation (12) and Equation (15), the model

would result in a higher customisation degree.

4.3.1 Critical factors of grey relational order on customisation degree

This study calculated the common factors of the Door-to-Door Direct Shipment to End

User Model (multiple linear demand) and the Direct Shipment after Manufactured Model

(non-linear demand) by grey relational order and realised that product price was the most

important factor on customisation degree. Silveira et al. (2001) suggested that mass

customisation aims to provide customised products and services by high output and

reasonable prices in a flexible process. From the manufacturers perspective,

Mukhopadhyay and Setoputro (2005) suggested that modelling design and goods return

would increase customer demand to reinforce customisation degree; however, it would also

increase the manufacturers costs; thus, this study suggested that product price is the

critical factor on customisation degree. The second critical factor affecting the

customisation degree is the inventory cost of a buffering zone. This study suggested thatwhen the inventory cost of a buffer zone is high, customisation degree of the products

Table 4. Comparison of different distribution deviation.

1 normal distribution deviation 1 Poisson distribution deviation

1 Poissondistribution

deviation


deviation


deviation

1 normaldistribution

deviation


deviation


deviation

Direct Shipment after Manufactured Model1590000 397,500 683,700 858,600 397,500 556,500 588,3001840000 460,000 791,200 993,600 460,000 644,000 680,8002180000 545,000 937,400 1,177,200 545,000 763,000 806,6003560000 890,000 1,530,800 1,922,400 890,000 1,246,000 1,317,2004780000 1,195,000 2,055,400 2,581,200 1,195,000 1,673,000 1,768,6006620000 1,655,000 2,846,600 3,574,800 1,655,000 2,317,000 2,449,4008160000 2,040,000 3,508,800 4,406,400 2,040,000 2,856,000 3,019,200

Augmentationof demandstandard deviation

A1 35,050,600 A2 27,868,100

3 normal distribution deviation 3 Poisson distribution deviation

Door-to-Door Direct Shipment to End User Model1800000 666,000 1,152,000 1,422,000 972,000 1,350,000 1,422,0002600000 962,000 1,664,000 2,054,000 1,404,000 1,950,000 2,054,0005580000 2,064,600 3,571,200 4,408,200 3,013,200 4,185,000 4,408,2009960000 3,685,200 6,374,400 7,868,400 5,378,400 7,470,000 7,868,400

13580000 5,024,600 8,691,200 10,728,200 7,333,200 10,185,000 10,728,20015960000 5,905,200 10,214,400 12,608,400 8,618,400 11,970,000 12,608,40025730000 9,520,100 16,467,200 20,326,700 13,894,200 19,297,500 20,326,700

Augmentationof demanddeviation

B1 135,378,000 B2 156,436,800

Multiple B1=A1 3:83 B2=A2 5:61

3036 S.-T. Wang


15/22

would be lower since more inventories in the buffer zone meant that the manufacturers

could not respond to customer demand immediately, as shown in Tables 2 and 3 (Tu et al.

2001, Berman 2002).

4.3.2 Comparison of demand deviation for different models

Customisation degree of the Door-to-Door Direct Shipment to End User Model in this

study was higher. Thus, there were eight kinds of products with more customer demand

deviation. After interviewing the companies, this study decided to use three fixed standard

deviations to decide demand prediction bias. According to Table 4, when demand

uncertainty was higher (higher standard deviation), there would be more customer

demand; customisation degree of the Direct Shipment after Manufactured Model was

lower. In this study, there were two kinds of products with less customer demand

deviation. Thus, after interviewing the companies, this study used one standard deviation

to determine demand prediction bias. Based on three standard deviations of normal

distribution and Poisson distribution, the predication of customer demand was more thanone standard deviation by 3.83 to 5.61 times, as shown in Table 4.

4.3.3 Parameter analysis and dynamic customisation degree decision

According to Tables 5 and 6, with the same parameters, when product price [Pki] is higher,

the product customisation degree would be higher. Thus, with booming business, the

companies should have a manufacturing strategy with a higher customisation degree; the

reduction of inventory cost in a buffer zone could increase customisation degree since

a higher customisation degree would result in less inventory in the buffer zone. The

inventory flow of the products would be faster. According to (BI) reducing the interval ofcosts in the buffer zone shown in Tables 5 and 6, in different situations, the customisation

degree could reach a 100% interval.

In the Door-to-Door Direct Shipment to End User Model in Table 5, the cost of

dynamic customisation degree of the business is: B NT $981,080 (sum of the average

total costs in Table 5). At present, most of the consumers demand a higher customisation

degree. Thus, static customisation degree E NT$ 1,037,080 (NT$ 207416 5),

E B NT$ 56,000. According to the Direct Shipment after Manufactured Model in

Table 6, dynamic customisation degreeB NT$ 753,480 (sum of the average total costs in

Table 6). Most of the consumers demand for higher. Thus, static customisation degree

E NT$ 781,480 (NT$ 156296 5),E B NT$ 28,000. According to the analysis above,total costs of the dynamic customisation degree based on monitoring indicators were lower

than the total costs of a static customisation degree.

4.3.4 Predication comparison between this model and other research models

According to the figures obtained after interviewing the firms, the calculation of Tables 2

and 3, Equation (10), and Equation (13) predicted the amounts of customer demand in

the Door-to-Door Direct Shipment to End User Model and Direct Shipment after

Manufactured Model. According toTSin Tables 7 and 8, estimation bias indicated by this

study was shown in Equation (28) which was acceptable and better than modifiedEquation (24) indicated by Lau and Lau (2003). After comparing the actual demand and



16/22


17/22

demand uncertainty met the Poisson distribution and that demand met normal

distribution, as shown in Equation (9). According to supply uncertainty in Sections

4.1 and 4.2, based upon the definitions of parameters and customisation

introduced in Section 3, this study compared the parameter changes among the

costs, and customisation degree models and analysed the influence of parameter

changes on the firms by managerial meetings in Section 4.3. According to data and

calculation of Tables 7 and 8 in Section 4.3.4, this study showed that the Direct

Shipment after Manufactured Model revealed higher net income. The reason wasthat the model involved the orders of several computer companies. Thus, there

Table 6. Dynamic customisation decision of Direct Shipment after Manufactured Model.

Product price [Pk

i

]Customisation

degree

Mean ofcustomisation

degree(monitoring indicators)

BIreducinginterval

Averagetotal cost

Pi

Pk

i

0:8

Original prices in20002006 1000

44.4% 79.3%(red)

BI: reducing1300

156,29658.6%77.7%90.7%93.9%94.6%95.4%


41.0% 75.7%(yellow and

red)

BI: reducing1500

153,49655.1%74.0%87.0%

90.1%90.9%91.7%

Original prices in20002006

24,000 37.5% 72.0%(green)

BI: reducing1700

150,69624,800 51.5%26,800 70.4%28,000 83.3%28,200 86.4%28,250 87.1%28,320 87.9%


34.1% 68.3%(blue and

yellow)

BI: reducing1950

147,89647.9%

66.7%79.6%82.6%83.3%84.1%


30.7% 64.7%(blue)

BI: reducing11200

145,09644.3%63.0%75.8%78.9%79.6%80.4%

Note: unit of cost is NT $.



18/22Table7.

Door-to-DoorDirectShipmenttoEndUserModelofdem

andestimated.

2000

2001

2002

2003

2004

2005

2006

Actu

aldemand

(10,0

00)

159

184

218

356

478

662

816

Actu

alnetincome

(hundredmillion

NTD)

23.97

27.8

1

30.28

46.2

9

63.68

88.8

9

107.2

3

Customerdemand

ca

lculatedin

Equation(10)of

thisstudy

1,5

51,87

9

2,1

11,4

26

2,3

85,58

3

3,4

21,4

40

4,8

30,32

7

6,6

52,0

97

8,1

47,5

20

Customerdemand

ca

lculatedupon

modified

Equation(24)

657,7

27

1,4

59,6

39

1,8

36,66

3

3,4

11,8

22

4,2

38,35

6

5,2

07,1

08

7,1

89,1

79

TSo

fcustomer

de

mandcalculated

in

Equation(10)of

thisstudy

1

2.7

5

3.98

2.5

7

3.39

3.4

3.6

4

TSo

fcustomer

de

mandcalculated

up

onmodified

Equation(24)

1

1.6

2.28

2.9

5

3.93

4.9

5(doesnot

meetstandard)

5.9

(doesnot

meetstandard)

3040 S.-T. Wang


19/22Tabl

e8.

DirectShipmentafterMan

ufacturedModelofdemandestimated.

2000

2001

2002

2003

2004

2005

2006

Actu

aldemand(10,0

00)

180

260

558

996

1358

1596

2573

Actu

alnetincome(hundred

millionNTD)

26.8

6

39.7

3

87.8

3

152.6

9

193.8

7

260.1

7

316.1

2

Cust

omerdemandcalculated

in

Equation(13)ofthis

study

1,8

19,5

09

2,4

49,4

75

5,5

33,843

10,5

98,5

46

13,7

14,8

57

15,7

67,9

45

25,8

88,5

40

Cust

omerdemandcalculated

up

onmodifiedEquation

(24)

0

1,4

04,2

75

5,9

95,186

9,2

49,2

39

10,1

93,1

23

10,4

75,9

34

10,9

75,1

24

TSo

fcustomerdemand

ca

lculatedinEquation(13)

of

thisstudy

1

2.9

3.9

2.8

3.8

2.4

3.4

TSo

fcustomerdemand

ca

lculateduponmodified

Equation(24)

1

1.8

1.8

2.6

197.6

(doesnot

meetstan

dard)

139.1

(doesnot

meetstandard)

72.6

(doesnot

meetstandard)



20/22

were more demands. Besides, product customisation degree was lower and it was

proper in the non-linear demand model. The Door to Door Direct Shipment to

End User Model involved a higher customisation degree, but lower customer

demand (Chen 2006). The reason was that the manufacturers in this model only

accepted the order of one computer firm; thus, it was proper in multiplying the

demand model.

(2) In order to validate the legitimacy of the models, this study used the figures of

notebook computer companies A and B for two models from 2000 to 2007 as the

base. Since there is a lack of literature on dynamic customisation degree, this study

followed the research models indicated by Lau and Lau (2003) for evaluating the

performance. Upon the estimation in Section 4.3.4, the results of Tables 7 and 8

demonstrated that the demand prediction model of customisation degree in this

study was better than that of Lau and Lau (2003).

(3) The study constructed the dynamic customisation degree of the model (considering

the total scores of monitoring indicators of the Council for Economic Planning and

Development) to make the model suitable for industries in manufacturing businesswith a high level of customisation to function as the criterion for other industries.

This study compared the construction of different degrees of customised models

between the notebook computer industries of two different operations. The models

involved the average price of product i, upon the customisation degree of the Mth

co-manufacturing process of the supply chain, as shown in Equation (1), where demand

uncertainties met the Poisson distribution and normal distribution, as shown in Equation

(9). Section 3.3 shows the construction of both multiple and non-linear customisation

models; in Section 3.4, through grey relational analysis, this study compared the influences

of different factors on the customisation degrees of different models; and in Section 3.5,with regard to the dynamic construction of customisation degrees and business

assumptions, as based on the total scores from monitored indicators by the Council for

Economic Planning and Development (ROC), this study divided business into five levels:

blue (recession), blue and yellow (unsatisfactory business), green (stationary business),

yellow and red (slightly prosperous business), and red (overly prosperous business). In

Section 4.3, this study described the managerial definitions as the criteria of notebook

computer industries. This study suggested views different from other related research

regarding the construction of dynamic customisation degree models, and the applications

of different factor analyses in notebook computer industries (Lau and Lau 2003, Baba

et al. 2009, Brun and Zorzini 2009, Kumar and Wilson 2009).

Future researchers can treat the mathematical model in this study as the criterion,

consider different uncertainties of varied production and delivery, include different

parameters and modifications and simulate the analytical results of the figures to provide

suggestions for management decisions in the firms.

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