Discussion Paper No.149
Why and how firms conduct specific supply chain
integration strategies? Considering theconfigurations of the customer order decoupling point
and supply chain integration
Nguyen Kim Ngan, Nozomu KawabataNov. 17, 2023

TOHOKU MANAGEMENT & ACCOUNTING RESEARCH GROUP
Discussion Paper

GRADUATE SCHOOL OF ECONOMICS AND
MANAGEMENT TOHOKU UNIVERSITY

27-1 KAWAUCHI, AOBA-KU, SENDAI,

980-8576 JAPANWhy and how firms conduct specific supply chain integration strategies?
Considering the configurations of the customer order decoupling point andsupply chain integration
Nguyen Kim Ngan,
Tohoku University, Graduate School of Economics and Management
Nozomu Kawabata,
Tohoku University, Graduate School of Economics and Management
Abstract
This study develops a theoretical framework of the supply chain integration (SCI) strategy selection in
manufacturing industries which use custom parts. Then, it examines the framework by collecting data fromdealers, manufacturers, and suppliers in three Japanese motorcycle supply chains in Vietnam. The empiricalevidence shows that customer order decoupling point (CODP) positioning and SCI implementation should
always be considered together, not separately. To achieve organizational strategic objectives and improve
operational performance, selecting suitable configurations of CODP and SCI strategy is important.
Generally, using full SCI combined with make-to-order (MTO) approach demonstrates superior
performance, achieving both efficiency and flexibility. However, demand volume and demand volatility
are market factors that influence the feasibility of CODP and SCI configurations. When suppliers use MTO,
the prerequisite for MTO with full SCI at manufacturers is that demand volume must be big enough to ask
for supplier integration (SI). In other words, SI is an enabler for MTO at manufacturers when suppliers use
MTO; and SI is constrained by demand volume. When manufacturers can use MTO with full SCI, demand
volatility then affects the level of SCI and consequent performances. Specifically, when demand volatility
is high, the level of SCI needs to be increased, but the risk of instability in production and labor allocation
also increases.
Keywords: supply chain integration, customer order decoupling point, production planning, operational
performance, Vietnam

1. Introduction
The study on supply chain integration (SCI) is essential and draw increasing attention among
researchers (Wiengarten et al., 2016; Khanuja & Jain, 2019). The desire to improve and respondto increasing uncertainty prompt organizations to make SCI with their supply chain (SC) partners(Khanuja & Jain, 2019). The integration of sales, production, and procurement is believed tofacilitate the seamless information processing throughout the supply chain, which improves
quality-cost-delivery and flexibility performance (Wong et al., 2011). However, SCI is noteffective and applicable to all firms in the same way (Shou et al., 2018). SCI is also complicatedand involves inter-firms’ efforts, costs, and risks (Wong et al., 2015). So far, there are someproblems that yet to be solved in SCI research.
Although the area of SCI is mature, most of studies focus on SCI-performance, and factors
affecting SCI type and level (Flynn et al., 2010; Wong et al., 2011; Wong et al., 2015; Khanuja &Jain, 2019). Surprisingly, there is no single study examines why and how firms select a specific
SCI strategy. Meanwhile, this study is utmost important to deeply understand the SCI process andprovide implications for SCI implementation. The lack of understanding the SCI strategy selection
process may affect the research settings in prior studies and become the reason for inconsistent
results of SCI performance. For examples, some researchers found the positive effect of customer
1

integration (CI) on operational performance (Frohlich and Westbrook 2001; Droge et al. 2004),
but others showed no significant effect (Devaraj et al., 2007). Or while some found positive effectof supplier integration (SI) on efficiency (Devaraj et al., 2007; Prajogo & Olhager, 2012), others
found significantly negative relationship (Abdallah et al., 2014). Until recent years, van Donk &van Doorne (2016) explored different customer decoupling points (CODP) locations may use
different SCI types and levels to achieve the organizational goals. Particularly, make-to-order(MTO), make-to-stock (MTS), and assemble-to-order (ATO) firms use relatively high integrationlevels in upstream, downstream, and internal dimensions, respectively (van Donk & van Doorne,2016). Therefore, we would expect that considering CODP positioning may help to develop theframework to investigate the SCI strategy selection and implementation process.
Besides, prior studies also face common limitations in data collection. Most studies either
used the data of manufacturers (Lu et al., 2018; Zhao et al., 2011), or suppliers (van Donk and van
Doorne, 2016), or data of random manufacturers and suppliers not in the same SC (Munir et al.,2020) to build the constructs for SCI. These settings are not sufficient (Wikner & Bäckstrand,2018), and provided very limited insights into a firm’s SCI strategy selection process. Regarding
context, prior studies majorly collected data from developed countries (Tsinopoulos and Mena,
2015) or China (Lockstrom et al., 2011; Khanuja & Jain, 2019). Thus, it is necessary to conduct
more research in other emerging markets to verify the arguments in prior studies (Khanuja & Jain,
2019).
To fill the above research gaps, this study collects data from suppliers, manufacturers, anddealers of the same supply chains in an emerging country to answer the research question: whyand how are firms using specific SCI strategies in terms of production planning?
To implement the research purpose, we firstly review the literatures of CODP and SCI todevelop the theoretical framework of SCI strategy selection process. Then, we use the multiple
case study of Japanese motorcycle supply chains in Vietnam to visualize the SCI process anddiscuss the answer to the research question. The case selection was driven by its relevance to thestudy purpose and the SCI research. Japanese motorcycle supply chains in Vietnam wereconfirmed to have characteristics of SCI such as intensive information sharing for Just-in-time(JIT) production (Mishima, 2010; Fujita, 2013; Ngan, 2020b). Thus, they are appropriate for the
SCI research. The three Japanese manufacturers concerned together account for more than 90% of
the market share. The small number of major players representing the industry provides a good
opportunity for in-depth research. Moreover, Khanuja & Jain (2019) reviewed 110 papers andcalled for more studies in emerging markets. The motorcycle industry is an industry whose
production and market centers are in emerging countries, and Vietnam’s motorcycle industry is
the World’s fourth largest in terms of market size and production scale (Marklines, 2022). Vietnam
is also among the fastest-growing economies (The Harvard Gazette, 2023), which is worth theattention. Meanwhile, its context is not mentioned in prior studies (Khanuja & Jain, 2019).
Different from the contexts in prior studies on SCI, the uncertainty in Vietnam is considerablyhigh with counterfeit parts, unique market characteristics (Ngan, 2020a), and frequent changes ofindustrial policies (Mishima, 2010; Fujita, 2013; Kawabata, 2015). The important position in
emerging markets and high uncertainty environment makes Vietnamese context useful to
supplement our understanding of SCI.
With the above case selection, this study deals with the supply chains of manufacturingindustries whose core components are custom parts. In such SCs, suppliers of key components
have to use MTO (Mishima, 2010; Fujita, 2013). Throughout the paper, we consider this constraint2while analyzing SCI strategies. Since the industries using custom parts occupy a very important
position in the manufacturing industry, the theoretical significance is not compromised.
Like previous research, this SCI study adopts organizational information processing theory(OIPT) (Shou et al., 2018). Accordingly, it will investigate an information-processing system
between manufacturers and external SC partners coping with task interdependence to respond tomarket requirements. The originality of this study is to provide the theoretical framework and thevisualization of SCI processes between suppliers, manufacturer, and dealers. Based on that, it
analyzes the case studies and draw implications. The results of this study show that (1) CODP
positioning and SCI implementation should always be considered together, not separately; (2) the
operational performance is different between configurations of CODP and SCI strategy; (3)demand volume and demand volatility are the constraints on the feasibility of CODP and SCI
configurations; (4) When suppliers use MTO approach, supplier integration (SI) is an enabler for
MTO at manufacturers; however, (5) SI is constrained by demand volume. Based on these findings,
the managerial implications are also discussed at the end of the study.
2. Literature review
2.1. Supply chain integration (SCI)
Regarding the definition of supply chain integration (SCI), the organizational information
processing theory (OIPT) approach sees SCI as an open system that emerges when core firms needan information processing mechanism to coordinate many different tasks between intra- and interorganizations (Galbraith, 1974; Shou et al., 2018). Information integration is the core of SCI anda foundation for integration of physical flows between SC partners (Sahin and Robinson, 2005;
van der Vaart & van Donk, 2004; Prajogo & Olhager, 2012). The purpose of SCI is to reduce
uncertainty and maximize the value for the end users at high speed and low cost (Flynn et al.,2010). Based on this definition, this study will investigate the information sharing and coordination
process between SC partners to answer the research question.
Generally, SC researchers encouraged the investigation of three dimensions of SCI,
including internal integration (II), supplier integration (SI), and customer integration (CI) (Flynn
et al., 2010; van Donk & van Doorne, 2016). II relates to information synchronization between
internal functions and collaboration between internal departments. II is the foundation for external
integration (Flynn et al., 2010; Zhao et al., 2011). CI and SI mean the collaborative andsynchronized processes between a core firm and its key customers and suppliers respectively(Flynn et al., 2010). Specific features of SI, CI, II discussed in prior studies are summarized inTable 3 (section 4) and are used to illustrate the SCI activities of each case in this study. In thisstudy, three dimensions relate to triad supplier-maker-dealer relationship. Since dealers are direct
customers of makers in automotive industries, maker-dealer dimension is the customer dimension.
2.2. Customer order decoupling point (CODP)
Traditionally, customer order decoupling point (CODP) is where a specific customer order isinvolved in the production or delivery stage (Giesberts & van der Tang, 1992; Hoekstra & Romme,
1992; Olhager, 2003). CODP is also considered as a main stock point, which buffers against
demand fluctuations (Wikner & Johansson, 2015). Different CODPs lead to different masterplanning levels (Olhager, 2003, 2010). Make-to-stock (MTS) focuses on standardization and theeconomy of scale to reduce production cost and delivery lead times; however, it faces inventory3risks if the forecast is inaccurate. In contrast, make-to-order (MTO) focuses on customization andthe responsiveness to demand changes, reduces inventory risks, but faces long delivery lead times
and reduced economies of scales. Assemble-to-order (ATO) is an alternative to MTS when partsand components are fabricated in advance, but final assembly line is delayed until receiving
customers’ orders. Compared to MTS, inventories of progressing parts at ATO are less costly thanfinished products (Olhager, 2010). There are obvious tradeoffs between the flexibility and reduced
inventory risks of MTO versus production efficiency and short delivery lead times of MTS.
Engineer-to-order (ETO) is when customer order is involved in the engineering and design stage.
However, ETO is considered as a special case of MTO because the material flows of ETO andMTO are considered identical from the SC perspective (Olhager and Prajogo, 2012).
There is also an extended framework of CODP including production dimension andengineering dimension to explain the cases where some engineering adaptations are made tocustomer orders (Wikner & Rudberg, 2005). However, as Japanese motorcycle manufacturers only
apply engineer-to-stock in Vietnam (i.e., product design already exists before a firm receive
customers’ orders), we only focus on the production dimension of CODP (i.e., MTO, ATO, MTS).
2.3. Relationship between CODP positioning and SCI implementation
Although SCI field is mature, it is surprisingly hard to find a study on SCI implementation process.
Reviewing 110 SCI papers in major journals, Khanuja & Jain (2019) found that tremendous studies
focus on enablers, SCI-performance link, and contingency factors on SCI- performance. However,extant studies do not explicitly investigate the SCI process or illustrate the relation between SCI
dimensions and enablers of SCI (Khanuja & Jain, 2019). Thus, there is a lack of the thoroughunderstanding of why and how firms implement a specific SCI strategy. When scrutinizing theliteratures, we found some arguments that SCI is used to reduce uncertainty to enable
postponement and customization in SC design (Yang & Burns, 2003; Mikkola & Skjøtt-Larsen,
2004). Meanwhile, postponement is closely related to CODP positioning, which is the strategic
consideration when designing and managing SC operations (Olhager, 2010). Therefore, we
extensively review the studies on the relationship between CODP and SCI.
The choice of CODP is affected by the demand volatility and the ratio between production
lead times and required delivery lead times (Olhager, 2003). The ratio between required delivery
lead times (D) and production lead times (P) were first identified as D/P relation (Shingo, 1981),
then was changed as P/D ratio for practical reasons (Mather, 1984; Olhager, 2003), and finally S/Dratio to reflex the more general supply lead time (Wikner, 2014). The supply lead time S is thetotal production lead times considering supplier lead times (i.e., purchasing parts and delivery fromsuppliers) (Wikner & Rudberg, 2005; Sun et al., 2008; Wikner & Johansson, 2015; Wikner &Bäckstrand, 2018). If S is shorter than D, MTO become feasible because all activities can be
conducted within the time required by customers (Wikner, 2014). However, if S is longer than D,
MTO is not feasible, so firms have to choose MTS or ATO and perform some activities before
receiving customer orders (Wikner, 2014).
Conceptually, Yang & Burns (2003) discussed the importance and implications of
postponement for CODP positioning. According to their arguments, postponement encourages a
new way of thinking regarding product design and inter-firm process design. This is because when
considering postponement, firms must decide which components would be standard or
customizable, which SC partners would do each task, what activities are forecast-driven or
customer order-driven, and where inventories would be located. In their arguments, SCI is4considered as an enabler for postponement implementation. On the other hand, postponement
helps locate the CODP. In this way, postponement implementation is closely related to CODP
positioning and SCI strategy, however, they do not directly discuss the relationship between CODP
and SCI.
The subsequent empirical studies finally provided some hints. Using two cases, van der
Vaart & van Donk (2004) argued that upon order-winning criteria (i.e., cost or flexibility), firms
can choose MTS or MTO and stage of integration (i.e., shared resources without commitment or
integrative planning stage). Compared to MTS, MTO firms often require higher supplierintegration (SI) levels to cope with uncertainties. Based on observing a supply chain consisting ofa vendor and a manufacturer, Sahin and Robinson (2005) examined the performance of SI in an
MTO system by using a mathematical model and simulation approach. They found that in MTO
systems, SI through information sharing and inter-organizational physical flow coordination helps
to substantially improve cost performance of the whole supply chain. Olhager and Prajogo (2012)
compared the performance of improvement initiatives between MTO and MTS firms and also
found that SI significantly improves the performance for MTO, but not for MTS plants. On theother hand, internal process improvement initiatives significantly improve performance for MTS,
but not for MTO. Van Donk and van Doorne (2016) empirically investigated the impact of CODP
location on SCI type and level. They explored that MTO, ATO, and MTS firms have relatively
high levels of SI, II, and CI, respectively.
Table 1: Research gaps
Yang
&
Burns
(2003)
van
der
Vaart
&
van Donk(2004)
Sahin andRobinson(2005)
Olhager &Prajogo(2012)
van Donk& van
Doorne(2016)

This study

Research Focus
Implications of postponement
for CODP positioning, SCI, SC
control, capacity planning
The concepts of integration,
shared resources and buyer
focus

Methods
conceptual

SCI
SI,
CI,
II

CODP
MTO,
MTS,
ATO

Data

Context

-

-

SI

MTO,
MTS

2S

-

SI

MTO

1M,
1S

industrial drilling
equipment

216
M

various industries
in Australia

The performance of SI in an
MTO system

Conceptual &two examples
mathematicalmodel&
simulation

Compared the performance ofimprovement initiatives
between MTO and MTS firms

survey research

SI

MTO,
MTS

Impact of CODP location on
SCI type and levela multiple case
study

SI,
CI,
II

MTO,
MTS,
ATO

12 S

Dutch metal partprocessing
industry

Considering CODP and SCI
configurations and theirperformancea multiple case
study

SI,
CI,
II

MTO,
ATO

3M,
7S,
17D

Vietnamese
motorcycle
industry

M: maker, S: supplier, D: dealer
Together these studies importantly suggested that SCI strategy selection may be impacted
by the CODP location (Van Donk and van Doorne, 2016). However, these studies face some
limitations. Yang & Burns (2003) is a conceptual study and not directly link CODP and SCI (Table1). For empirical research, early studies do not consider SI, CI, and II together (van der Vaart &5van Donk, 2004; Sahin and Robinson, 2005; Olhager and Prajogo, 2012), while later ones do not
examine the performance of the integrative efforts (van Donk and van Doorne, 2016) (Table 1).
Besides, their studies were based on data collected from single respondent of each firm, or fromone-sided SC member to build construct for three-dimensioned supply chains (Table 1). This may
potentially result in limited insights or even biased results. Furthermore, they were conducted indeveloped countries (Table 1).
Addressing these research gaps, this study will examine the process of SCI strategy
selection and evaluate performance to answer the research question on why and how firms
implement specific SCI strategies. To overcome the limitations in prior research, this study collects
data from suppliers, manufacturers, and dealers from the same SCs in an emerging market context(Table 1).
2.4. Theoretical framework and measurements
Since SCI is considered as an enabler of postponement implementation (Yang & Burns, 2003;
Mikkola & Skjøtt-Larsen, 2004), SCI strategy may be affected by the CODP decision. Based on
hints from prior studies, we propose the process as follows.
At first, firms need to define the organizational strategic objectives based on market
characteristics and order-winning criteria (van der Vaart & van Donk, 2004). Firms may prioritizedelivery speed and cost (efficiency) or the responsiveness to demand changes (flexibility). Basedon that, firms will choose the most suitable CODP (van der Vaart & van Donk, 2004). Specifically,
when firms face relatively high uncertainty or demand volatility, MTO is a natural choice topostpone production until receiving customers’ orders (Olhager, 2003; Pereira et al., 2022). In low
uncertainty situations and delivery speed and cost are the main order-winning, MTS or ATO is
more appropriate (Olhager, 2003). When firms need to move towards MTO to increase flexibility,
they need to increase postponement. For postponement to work, firms need to implement SCI to
reduce uncertainties to ease the flow of information and materials (Yang & Burns, 2003; van der
Vaart & van Donk, 2004). Conceptually, internal integration (II), supplier integration (SI), andcustomer integration (CI) are required to reduce process uncertainty, supplier uncertainty, anddemand uncertainty respectively (Yang & Burns, 2003).
Figure 1 is established based on the above arguments: market factors affect organizational
strategic objectives, which in turn affect the CODP location and SCI strategy at the core firms (i.e.,manufacturers in this study). Regarding market factors, required delivery lead time, demand
volume, and demand volatility are the most agreeable factors directly affecting the organizational
goals and CODP location (Olhager, 2003; Aktan & Akyuz, 2017; Tookanlou & Wong, 2020).
Organizational strategic objectives relate to whether firms want to prioritize efficiency or
flexibility. Based on that, firms will choose CODP location and SCI strategies (Figure 1).
As mentioned, CODP location is constrained by the ratio between supply lead times andrequired delivery lead times- S/D ratio (Wikner, 2014). However, different from prior studies, thisstudy considers three dimensions, so supply lead time S is not appropriate to use. Since there are
multiple CODPs along the supply chains (Yang & Burns, 2003; Sun et al., 2008), we need toclearly separate lead times into dealers’ required delivery lead times (Dd), makers’ production leadtimes (Pm), and suppliers’ production lead times (Ps). As this study focuses on the industries
whose core components are custom parts and key suppliers employ MTO, so Ps<Pm is the premise.
Then, Ps<Pm<Dd becomes the necessary condition for MTO to be feasible at both suppliers andmakers. When Ps<Pm and Pm>Dd, MTO is not feasible for manufacturers. In this triad, we assume6

that makers’ CODP positioning (MTO, MTS, ATO) are not only affected by dealers’ Dd but also
by suppliers’ CODP location and production lead times (Ps).
Market factors

Organizational
strategies

CODP & SCI

DeliveryRequired delivery
lead time (Dd)

Demand Volume

Performance

Prioritize
efficiency/
flexibility or
both?

MTO/MTS/ATO with
SI/ CI/ II, orfull SCI?

Flexibility
Inventory
Efficiency

Demand Volatility

Note: CI = customer integration, II = internal integration, SI= supplier integration,
full SCI = CI + II + SI

Figure 1: The SCI strategy selection process at the core firms
Furthermore, we also assume that firms can flexibly change their CODP positioning using
SCI and postponement. This is because the empirical evidence from Japanese automotive, steel,electrical appliance, and semiconductor industries show that firms can change the priorities
between flexibility and efficiency by adjusting LSP (the lot size of planning) and LLS (the lengthof lead time for scheduling before production) (Okamoto, 2003) (Figure 2). LLS and LSP aremeasured by days. Firms can prioritize flexibility by shortening LLS and LSP and dividing
monthly production into weekly production where market changes can intrude. In contrast, firms
can utilize economies of scale by expanding LLS and LSP (Okamoto, 2003). We use LLS and LSP
to measure Dd, Pm, and Ps. Different from the terms used in production, dealers’ LLSd represents
how far the order must be finalized before the first delivery. Dealers’ LSPd represents the order
lot size. Pm consists of makers’ LLSm and LSPm. Makers’ scheduling lead time (LLSm) is
counted from the day of fixing production planning to the first production day. LLSm is the timenecessary for manufacturers to prepare manpower, machines, input, testing staff, and arrange
production lines. LLSm is vital to production and should be included in production lead times.
Similarly, Ps consists of suppliers’ LLSs and LSPs.
Dd = LLSd + LSPd
Pm = LLSm + LSPm
Ps = LLSs + LSPs
Decomposing lead times as above help us to measure exactly the lead times and illustrate
the coordination process between suppliers, makers, and dealers.

7Figure 2: LLS and LSP indexes based on Okamoto (2003)
We assume that SCI-performance may be different between CODP and SCI configurations(Figure 1). As mentioned, each CODP location has different organizational objectives andconsequently different utilization of SCI strategies (van der Vaart & van Donk, 2004; van Donk
& van Doorne, 2016). MTO firms rely heavily on suppliers’ quality-cost-delivery performance todeliver customized products at the speed and cost required by the market. Therefore, supplierintegration (SI) plays important role to the performance of MTO firms (Olhager & Prajogo, 2012;
Van Donk and van Doorne, 2016). Meanwhile, MTS firms compete on price and delivery
performance of standardized products. ATO firms increase their competitiveness of price andminor flexibility of customized products (Pereira et al., 2022). MTS and ATO firms often havearm-length relationship with suppliers and less requirement for SI (Sahin and Robinson, 2005;
Olhager & Prajogo, 2012; Van Donk and van Doorne, 2016). Instead, MTS rely on CI to improveforecast accuracy while ATO firms rely on II to solve the internal complexity and combine MTS
parts production with MTO assembly (van Donk and van Doorne, 2016). In this way, different useof SCI strategy may lead to different performance between MTO, MTS and ATO. In this study,
we will evaluate the performance of specific SCI strategies in terms of delivery, flexibility,
inventory control, and production efficiency (Figure 1). These performance indicators arecommonly analyzed in prior studies (Tomino et al., 2009; Wong et al., 2011).
Based on this theoretical framework (Figure 1), we collect data from dealers,manufacturers, and suppliers to illustrate the SCI process and explain why and how firms selectand implement specific SCI strategies. We also evaluate performance to draw valuable
implications.
3. Methodology
3.1. Research design
We apply an embedded multiple case study with inductive reasoning because it is appropriate toanswer the question “why” and “how” (Eisenhardt, 1989; Yin, 2003). A semi-structured interview
was adopted because it allows the adoption of both rigorous guidelines based on literature reviews
and space for new questions that emerge in the field trips (Cohen and Crabtree, 2006). This design
has the advantage to explore the insights into SCI processes and reasons for SCI strategy selection(van Donk and van der Vaart, 2004). Thus, it is popularly deployed for exploratory research in the
SC field (Bevilacqua et al., 2012; Kobayashi et al., 2017; Lockstrom et al., 2011).
3.2. Case selection and data collection
The supply chains of Japanese motorcycle manufacturers in Vietnam are selected as units ofanalysis. Sub-units of analysis are dealers, manufacturers, and suppliers. The dealer interview list
was sorted from makers’ authorized dealer lists published on their websites. Long-standing dealers8were chosen to obtain the data related to SCI and rich information about market characteristics.
The supplier interview list was sorted from the lists of suppliers in Vietnam published on thewebsites of the Vietnamese government and the Japan External Trade Organization (JETRO). We
sorted out tier 1 suppliers based on the “main customers” category in these lists.
Three manufacturers are A, B, and C (Table 2). All were headquartered in Japan but had
motorcycle assembly plants in Vietnam. Exclusive dealers of all manufacturers are local
businesses in Vietnam. The core part suppliers of manufacturers A and B were located in Vietnam,
while those of manufacturer C were mainly located in countries other than Vietnam. The proximity
facilitates the short logistic delivery time of parts from suppliers to manufacturers A and B’s
factories and of completed motorcycles from all manufacturers to local dealers.
Data collection was conducted four times from 2015 to 2018 (Table 2). We started byinvestigating local markets and interviewing dealers in 2015. The first data were analyzed andreported in many seminars and workshops, which resulted in the necessity for the second round ofdata collection. In 2016, we randomly visited the dealers and asked for research interviews. We
stopped the data collection when all data were confirmed consistently, and no new information
emerged. Interviews were conducted in Vietnamese and lasted between 30 minutes and 2 hours.
Most of dealers’ respondents were managing daily businesses and provided rich insights. 17
interviews with authorized dealers are used in this paper (Table 2).
Table 2: Profiles of interviewed firms

Name

Main products(production volume*)

Maker A

Motorcycle (approx.
2,560,000 units)

Maker B

Motorcycle (notdiscrete)maker C

Motorcycle (approx.
24,000 units)

2 suppliersof B

sprocket, chain; lighting
system; body & chassis;
suspension; brakesystem; wheel &muffler

1 supplier
of C

gears

4 suppliersof A

9 dealers ofA
5 dealers ofB

Motorcycle products,
spare parts, after sales
serviceMaincustomers

Interview dateAug 30, 2017 &Sep 04, 2018

Interviewees’ positions
Managers of Production,
Planning, Sales,
Purchasing, Accounting &Finance, IT, Logistics,
Business Administration
departments
CEO & production
manager

Aug -Sep 2018

CEOs, Sales managers

Aug 2015Mar 2016

Owners, CEOs, financemanagers, mechanic heads,
accounting staffs

Aug 28, 2017 &Aug 27, 2018exclusive
dealers

Aug 29, 2017 &Aug 28, 2018

A is taking 50-93% of sales
B is taking 5095% of sales
C is taking
30% of sales

end-users

3 dealers ofC* Production volume is in 2018.

9

For makers and suppliers, we sent postal letters including a request for cooperation, a
research purpose, and a question list to the CEOs of Japanese firms in Vietnam. In 2017 and 2018,
we got acceptance from three Japanese manufacturers and seven suppliers. Respondents were agroup of managers, including production, purchasing, and sales department managers (Table 2).
They were the key persons in supply chain management and provided detailed information about
each integration dimension. The languages used in the interviews were English, Japanese, andVietnamese. It took approximately 90 minutes for interviews, and 90-150 minutes if factory tours
were allowed. Notetaking and observation were allowed, but taking pictures inside factories andvoice recording were prohibited (except for one recording with manufacturer A in 2017).
3.3. Data coding and analysis
We applied Eisenhardt’s (1989) methods for coding and analysis for a multiple case study. During
and after each interview, we checked and wrote descriptions in Word files. Then, we inputtedquantitative data and coded qualitative data by categorizing the text into worksheets.
Quantitative data include LLS, LSP, Dd, Pm, Ps, inventory (measured by the number ofdays), production volumes, and bit time (i.e., the time gap between two finished motorbikes). They
were used to develop tabular displays, map SCI processes, and elucidate a within-case SCI pattern.
Then, SCI patterns and performance across supply chains were compared. Meanwhile, qualitative
data were coded into worksheets and compared between cases to explore the reasons for SCI
strategy selection.
Triangulation was conducted intensively in various ways. Data systematically recorded inworksheets were crosschecked between each manufacturer and their respective dealers andsuppliers. Any inconsistent information was confirmed at the second interview with manufacturers
in 2018. This helped to reduce errors or biases from both respondents and researchers. Data
collection only ended when there was no inconsistent data. Secondary data were also used fortriangulation, including published statistics, related reports, and online news. Finally, the drafts
were sent back to respective makers and suppliers for their confirmation. The relevance of
respondents, intensive triangulation, and confirmation from respondents together increased thevalidity and reliability of this study.
4. Results
4.1. SCI strategies: SI, CI, II, or full SCI?
Table 3 illustrates that supply chains A and B have full SCI, meanwhile supply chain C only hascustomer integration (CI) and internal integration (II). Supply chains A and B have similar
integration features and have more integrative efforts of II and CI than supply chain C (Table 3).
There is no product development function in Vietnam. Therefore, some features of internal
integration and supplier integration related to product development were not observed (Table 3).
Table 3: Supply chain integration features in three cases
Supply chain integration features
Internal integration (II)Information is shared among all departments

A

B

C

√

√

√

√

√

√

10

Periodic inter-departmental meetings to review manufacturing planning
Synchronized data from purchasing, production, shipping, and sales

√
√

√
√

√
X

Cross-functional teams in problem-solving & process improvement

√

√

√

Cross-functional teams in new product development

X

X

X

Internal management communicates frequently about goals and priorities

√

√

√

√

√

√

Information integration system

√

√

√

Computerized ordering & delivery system

√

√

X

Periodical meetings & Frequent communication

√

√

√

Active involvement in new product development (market research)

√

√

X

End-user satisfaction surveys

√

√

√

√

√

√

Demand forecast (sales plan)

√

√

√

Feedback on quality, design, and delivery performance

√

√

√

Updating market information, market trend

√

√

√

Sharing from manufacturers
Production & delivery schedule

√

√

√

Marketing campaigns

√

√

√

√

√

X

Customer integration (CI)
Customer’s (dealer) collaboration

Sharing from major customers (dealers)
Real-time searching of inventory & point of sales information

Supplier Integration (SI)
Supplier’s collaboration
Information integration system & Computerized ordering system√√

Strategic partnership (long-term relationship)√√

Joint planning in production & purchasing (centralized purchasing)√√

Suppliers’ participation in the product design stage

X

X

Just-in-time delivery√√

Corporate-level communication/meetings√√

Production schedule & capacity√√

Available inventory√√

Cost information (cost improvement)√√

Sharing from manufacturers
Production plan & real-time production schedule

Sharing from major suppliers√√

Demand forecast√√

Real-time inventory data√√

Cost information (centralized purchasing of materials)√√

Performance (Quality-Cost-Delivery) standard√√

Feedback to improve cost, quality, and delivery performance

√

√

11

Process improvement supports to meet manufacturers’ needs√√√: Yes X: No
The features are based on Zhao et al. (2011), Flynn et al. (2010), Braunscheidel and Suresh (2009)
In the interviews, all suppliers confirmed that they used MTO approach. Next, we visualize
the SCI processes in terms of production planning in three dimensions at supply chains A, B, andC (Figures 3, 4, 5).

4.2. Why and how do firms implement specific SCI strategies?
Supply chain A’s SCI processes
With big demand volume (Table 2) and low demand volatility, manufacturer A revealed they
actively chose MTO and full SCI to ensure flexibility and maintained LSP big enough for
efficiency. In Vietnam, Japanese manufacturers faced the severe problems of counterfeit parts andproducts. To protect their brand reputation and integrate information with dealers, Japanesemanufacturers only distribute to their own exclusive dealers (Table 2). The information flows andcoordination process are as follows.
Dealers sent four-week sales plans with full specifications of type and color via IT Systembefore the 5th of month N-1 (Figure 3, LLSd1 = 25 days, LSPd =30 days). After that, dealers could
send unexpected changes through email by the 15th of the month N-1 (Figure 3, LLSd2 =15 days,
LSPd = 30 days). Since manufacturer A’s production capacity was lower than the demand volume
in 2018, dealers’ sales plans are only for consideration. Manufacturer A notified the final allocatedamount to dealers by the 25th of month N-1 (Figure 3).
In the maker dimension, the first production-sales meeting was held on the 8th. They fixed
four-week production volume by model and two-week volume by full specifications (i.e., color)(Figure 3, LLSm=22 days, LSPm1 = 30 days by model, LSPm2 = 15 days by full specifications).
They determined the first two-week production schedules and forecast for the remaining two
weeks. Around the 23rd, they updated the dealers’ plan changes, real-time inventory data, andsales trends. Based on that, they held a second meeting to determine the second two-week
production schedule (Figure 3). Thus, they can adjust production schedule twice a month.
In the supplier dimension, suppliers started purchasing the long-lead time items (e.g.,materials and import components) in month N-2 or N-3 to prepare for the production of month N
based on manufacturer A’s forecast (Figure 3). In month N-1, suppliers received fixed production
schedules of four-week volumes by model and two-week volumes by full specifications around 15
days before production (Figure 3, LLSs=15 days, LSPs1 = 30 days by model, LSPs2 = 15 days byfull specifications). Suppliers prepared manpower, materials, components, and production lines tomake Just-in-time (JIT) delivery after receiving weekly actual orders.
Supply chain B’s SCI processes
Manufacturer B faced higher demand volatility and had to pursue MTO with full SCI and small
LLS and LSP to increase flexibility. In 2017, They used to use MTS and equally divide the monthly
production to improve the stability in labor arrangement and production schedules. However, highinventory unfortunately accumulated at the end of that year, stocked from November 2017 until

12March 2018. To respond to high demand volatility, they had to back to MTO from 2018. Highdemand volatility forced manufacturer B to use smaller LLP and LSP to adjust production schedule
up to 4 times per month (Figure 4, Table 4). Dealers sent orders every two weeks by full
specifications via the IT system (Figure 4, LLSd =15 days, LSPd = 15 days). In maker dimension,
manufacturer B fixed a two-week volume by model and a one-week volume by full specifications
after receiving dealers’ orders (Figure 4, LLSm = 15 days, LSPm1 = 15 days by model, LSPm2 =7 days by full specification). The second week was open to revision (i.e., color change) based on
manufacturer B’s review of inventories at factories and dealers. In the supplier dimension,
suppliers received fixed two-week volumes of parts by models 15 days before production in monthN-1 (Figure 4, LLSs1 = 15 days, LSPs1 = 15 days by models). One week before production,
suppliers fixed a one-week volume by full specifications based on manufacturer B’s weekly orders(Figure 4, LLSs2 = 7 days, LSPs2 = 7 days by full specifications).
Notably, besides production postponement, manufacturer B also applied delivery
postponement. They divided orders into many small deliveries and delayed some if the inventory
piled up at dealers. This helps to avoid product surpluses at dealer networks which cause the market
price to fall.
In SCs A and B, MTO approach and the full SCI process is illustrated through the dashedline in Figures 3 & 4. According to interviews, due to low capability and management knowledgeof Vietnamese dealers and suppliers in initial years, both manufacturers A and B have invested insupplier and dealer capability development programs besides the integrated IT system. These
investments were huge but necessary to build trust and encourage SC partners for the information
sharing and collaborations for coordinated processes.

13

Figure 3: SCI process of manufacturer A in 2018

Figure 4: SCI process of manufacturer A in 201814

Supply chain C’s SCI processes

Figure 5: SCI process of manufacturer C in 2018
Manufacturer C revealed that due to very small demand volume (Table 2), it was unable to neither
encourage their suppliers of core components to locate in Vietnam or ask Vietnam-based suppliers
for investments in production lines dedicated to manufacturer C for JIT delivery. Since the corecomponents suppliers were not located nearby and SI could not be conducted with overseas
suppliers, the LLS and the time required to transport the components from overseas to Vietnam
was longer (Figure 5, Table 4). Therefore, manufacturer C had no choice but to choose the ATO
method to meet the required delivery lead times and avoid high stock of finished products.
Manufacturing postponement was applied by stocking parts and only starting assembly after
receiving dealers’ orders. In this situation, manufacturer C utilized internal and customer
integration to update market data and review the production plans (Table 3).
Around the 10th of month N-1, dealers sent four-week orders by full specifications via
emails (Figure 5, LLSd=20 days, LSPd = 30 days by full specifications). Around the 10th of monthN-3, manufacturer C fixed production volumes by full specifications for month N (Figure 5, LLSm
=80 days, LSPm=30 days by full specifications). Every two weeks, manufacturer C held
production meetings, considering dealers’ feedback, to review the production plan. A daily
assembly schedule was determined in month N-1. In 2018, manufacturer C produce sport bikes(80%) and scooters (20%). Orders of sport bike parts were sent to suppliers in Japan and ASEAN
countries 3 months before production (Figure 5, LLSs1 > 30 days, LSPs= 30 days). Orders ofscooter parts were sent to Japanese and Taiwanese suppliers located in Vietnam two weeks before
production (Figure 5, LLSs2=15 days, LSPs= 30 days). While parts were delivered to the
manufacturers within one day in supply chains A and B, it took about 45-60 days for manufacturer
C to receive core parts from overseas suppliers after they finished all production orders.

154.3. Performance comparison
Table 4: CODP, SCI strategies & Performance Comparison
SCI
Dealer
Maker
Supplier

A

Level

LLS
25; 1522
22
15
15

full specModelfull specModelfull spec

B
LSP
30
30
15
30
15

LLS
15
15
15
15
7

C
LSP
15
15
7
15
7

LLS
20

LSP
30

80

30

Local (20%): 15
Import (80%): >30

30

Delivery, CODP, SCI patterns
55
30
52
30
0.95
1
MTO
MTO
Full SCI (CI, II,
Full SCI (CI, II,
SI)
SI)

Dd
Pm
Pm/Dd
MethodSCI strategies

50
110
2.2
ATO
II and CI

Flexibility
Dealers’ orders (times/month)
Makers’ production planning(times/month)

2

2

1

2

4

1

Inventory
Maker’s safety
stock

Part

0

0

Local (20%): 1-2 days
Import (80%): 7-14 days

Product

hourly7-14 days

2-3 days

Production efficiency
stableunstable
23 seconds
24 seconds
25 seconds
27 seconds
NA
NA

Labor arrangement
stablemopedstop production
Bit time
scooter
5 minutes
sport bike
6 minutes
NA: not available information
Bit time: the time gap between two finished motorbikes in a production line
Unit of LLS, LSP, Pm, Dd: day

Dealers’ required delivery lead times (Dd=LLSd+LSPd) and production lead times(Pm=LLSm+LSPm) are calculated in Table 4. Regarding delivery performance, all firms met Dd,
which was 55, 30, and 50 days in supply chains A, B, and C, respectively. Pm/Dd ≤ 1 enabled the
MTO at manufacturers A and B. Figures 3 and 4 demonstrate that SI helps to shorten Pm andbecome the enabler for MTO at manufacturers A and B. Meanwhile, manufacturer C could not
make SI to shorten the whole supply lead times and had to adopt ATO. All manufacturers applyinternal and customer integration to improve forecast accuracy (Tables 3&4).

16

Regarding flexibility, dealers of supply chains A and B could update their orders twice permonth (Figures 3 & 4). Manufacturer B had the highest flexibility thanks to the small LLS andLSP with four production adjustments per month, which was followed by manufacturer A with
two adjustments per month (Table 4). Meanwhile, supply chain C did not have these kinds offlexibility. These results demonstrate that manufacturers A and B with MTO and full SCI (CI, II,SI) had more flexibility than manufacturer C with ATO and II and CI alone (Table 4).
Then, we look at inventory. Manufacturers A and B had 0 inventory of parts (Table 4).
Instead, manufacturer A asked their suppliers to typically keep three-day safety stock. Meanwhile,
manufacturer C suffered from storing 7-14 days for imported parts for sport bikes (>80% of
production) (Table 4). For the inventory of finished products, manufacturer A tried to supply tothe dealers on an hourly basis. In contrast, manufacturer B kept 7–14 days of finished vehicles atfactories (Table 4). Manufacturer B’s relatively high safety stock of finished products even using
MTO is because of the delivery postponement and high demand volatility mentioned above.
Manufacturer C kept 2-3 days of finished products (Table 4). Since manufacturer C was holding
many imported parts for assembly, they could reduce the safety stocks of finished products. In
total, the inventory of both the parts and finished motorcycles at manufacturers was hourly at A,
7–14 days at B, and 10–19 days at C. These results illustrate that MTO firms with full SCI may
control inventories better than ATO firms with only internal and customer integration.
Regarding production efficiency, manufacturers A and B applied process improvement andsynchronous equipment in mass production to reduce the bit time by around 23–25 seconds, and
24–27 seconds, respectively (Table 4). Meanwhile, manufacturer C had a bit time of 5–6 minutes.
Manufacturer C revealed no intention to speed up the bit time due to the small volume (Tables 2,4). Besides, manufacturers A and C reported their efficient arrangement of labor while
manufacturer B faced difficulties. Manufacturer A revealed that their demand volume was bigger
than the supply capacity, and they faced very low demand volatility. Thanks to that, manufacturer
A could limit the change rate within ±10 percent of yearly production plans to reduce the negative
impact of change on its suppliers’ production plans. Manufacturer C’s stable demand volumes andcustomer integration facilitated high forecast accuracy. In contrast, manufacturer B reported thatthey faced high demand volatility, which resulted in unstable arrangements of labor. According tointerviews with tier 1 suppliers, the instability in manufacturer B’s production schedule also led tosuppliers’ production instability and inefficient labor arrangement. Thus, MTO combining withfull SCI can mitigate the tradeoffs between efficiency and flexibility only under acceptable demand
volatility associated with big demand volume (case A).
5. Discussion
In three Japanese motorcycle supply chains in Vietnam, all suppliers produce custom parts andchoose the MTO approach, which is the upstream constraint. On the other hand, all manufacturers
must meet the required delivery time, which is a downstream constraint. Under these constraints,
we discussed why and how core firms use different configurations of CODP and SCI to achieve
their organizational strategic objectives.
The results explicitly showcase that CODP positioning and SCI implementation areconcurrent and interrelated processes. Therefore, they should be considered together, notseparately. Importantly, the operational performance is different depending on configurations of
CODP and SCI. Specifically, using full SCI (CI, II, SI) combined with MTO may lead to the highperformance of delivery, flexibility, inventory control, and efficiency (case A, Table 4). However,17the prerequisites are that (1) the demand volume is large, (2) demand volatility is relatively low,and (3) a rather long LLS and large LSP are acceptable (case A, Table 2, Table 4). When demand
volume is relatively smaller and demand volatility relatively higher, short LLSs and small LSPs
are needed to increase flexibility (case B, Table 4). In this case, the tradeoffs occur: core firms
may have high performance of delivery, flexibility, and inventory control but face the instabilityof labor arrangement and production scheduling (case B). When demand volume is even smaller,
it becomes impossible to locate suppliers in proximity or SI with remote suppliers. In this situation,
combining ATO with CI and II is the only measure to mitigate the tradeoffs between efficiency
and flexibility without threatening the delivery lead times required by the market. In this case, themanufacturer may face the problem of high inventory risk (Case C). Thus, we developed three
propositions:
Proposition 1: CODP positioning and SCI implementation are concurrent and interrelated
processes.
Proposition 2: The operational performance is different between configurations of CODP
and SCI.
Proposition 3: Demand volume and demand volatility are the constraints on the feasible
configurations of CODP and SCI.
We take a closer look at the unique nature of the supply chains where the core components
are custom parts and suppliers must adopt MTO. The supply lead times consisting of suppliers’and manufacturer’ lead times would be very long if manufacturers use MTO and their production
only starts after suppliers finish all of production lots. Thus, supplier integration (SI), which allows
suppliers and manufacturers to proceed and synchronize their production almost simultaneously,
is important to reduce suppliers’ lead times and satisfy downstream constraints. However,investments in supplier integration (SI) are costly. First, suppliers need to invest in manufacturerspecific production lines and manufacturers have to invest in initiatives to build trust for effective
coordination. Second, the supplier’s production line must be in proximity to facilitate JIT delivery
to the manufacturer’s assembly plant. The feasibility of these two investments is affected by thedemand volume.
If the demand volume is large enough, the manufacturer can request SI to shorten the
production lead time (Pm) by decomposing product specifications into coordinated processes with
suppliers (Cases A and B). SI and JIT production can help shorten Pm so that Pm/Dd<1. Thanks
to that manufacturers can choose MTO to increase flexibility under market uncertainty, especiallyin emerging markets.
When demand volume is small, manufacturers cannot achieve supplier integration (SI)(case C). If suppliers adopting MTO are not functionally integrated with the manufacturer, theoverall Pm of the manufacturer will be lengthened because of the long delivery lead times ofcomponents. Moreover, suppliers without dedicated production lines do not give priority to
production for manufacturer C, so manufacturers’ orders must be sent early. Consequently, themanufacturer’s production plan must also be finalized early, which prolongs the LLSm (Figure 5).
Manufacturers’ production lead times (Pm) becomes longer, so Pm/Dd>1. Thus, manufacturers
have no choice but to use MTS or ATO to meet dealers’ required delivery lead time (Dd). Thus,
we have:18

Proposition 4: In supply chains where the core components are custom parts, supplierintegration is an enabler for MTO at manufacturers.
Proposition 5: Supplier integration is constrained by demand volume.
6. Conclusions
This study examined why and how firms implement specific SCI strategies using the multiple casestudy of the Japanese motorcycle SCs in Vietnam. This study provides various theoretical
implications.
First, this study provides empirical evidence of (1) the concurrent and interrelated processof CODP and SCI strategy and (2) different operational performance depending on theconfigurations of CODP and SCI strategy. For the first time, the concurrent and interrelated
process of CODP and SCI is explicitly examined, which contributes to the deeper understandingof SCI design and implementation. Based on findings, it is crucial to consider CODP location
when examining SCI performance. Ignoring CODP location may be the main reason why priorstudies have inconsistent results of SCI performance (Tarifa-Fernandez & De Burgos-Jiménez,2017; Khanuja & Jain, 2019). MTS firms have less need for SI because their focus is on efficiency,
not flexibility. In a study sample accidentally dominated by MTS and ATO firms, there is a riskof under-estimating the SI performance. In contrast, MTO firms have a greater need for SI because
their focus is on flexibility. Therefore, in a study sample dominated by MTO firms, there is a riskof over-estimating the SI performance and assuming that it applies to all firms. Thus, researchers
should assess operational performance based on the configurations of CODP and SCI strategy
rather than based solely on SCI type. Generally, MTO combined with full SCI demonstratesuperior performance.
Second, empirical evidence also shows that demand volume and demand volatility are theconstraints on the feasible configurations of CODP and SCI. In the supply chains where the corecomponents are custom parts, the prerequisite for MTO with full SCI at manufacturers is thatdemand volume must be big enough to encourage suppliers’ investments in manufacturer-specificequipment for SI and JIT production. In other words, SI is an enabler for MTO at manufacturers
while suppliers apply MTO; and SI is constrained by demand volume. When manufacturers canadopt MTO with full SCI, demand volatility then affects the level of SCI and performances. If
demand volatility is low, manufacturers can utilize long LLS and large LSP to achieve both
efficiency and flexibility. In contrast, if demand volatility is high, short LLSs and small LSPs areneeded to increase flexibility but firms may face the production instability.
There are also managerial implications for managers. Evidence in this study shows that
there is no optimal SCI practice for all cases. Firms should firstly define organizational objectives(i.e., flexibility or efficiency) based on their specific market characteristics (i.e., required delivery
lead times, demand volume, and demand volatility). Based on that, they should select appropriate
CODP location (i.e., MTO, MTS, or ATO) and SCI strategies. Under high market uncertainty,
firms can increase flexibility and maintain certain efficiency by combing MTO and full SCI if theirdemand volume is big enough to enable SI.
The originality of this study is to provide the theoretical framework of SCI strategy
selection and the visualization of SCI process involving dealers, manufacturers, and suppliers.
Future studies can utilize the framework and visualization techniques to deeply analyze andcompare the difference between firms’ SCI strategies. Managers of firms also benefit from this19

framework to re-think and re-design their supply chain integration and coordination processaccording to demand changes.
This study also has their own limitations, which become the avenue for future research.
This study deals with the supply chain where suppliers must adopt MTO due to custom partproduction. In supply chains where suppliers can adopt MTS, the constraints on CODP and SCI
configurations may be alleviated for manufacturers. Future studies should further investigate theimpact of suppliers’ CODP location on manufacturers’ choice of CODP and SCI configurations.
Furthermore, this study does not discuss the configuration of MTS and SCI strategies. Case studies
and survey research which include more diverse configurations of CODP location and SCI strategy
are needed to complement to our findings.
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