Supply Chain Integration Using a Maturity Scale: Review of Existing Frameworks

Supply Chain Integration Using a Maturity Scale: Review of Existing Frameworks

Gilbert Aryee, Chandra Lalwani,and Mohamed M. Naim

Logistics Systems Dynamics Group, CardiffBusinessSchool, CardiffUniversity.

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Abstract

The paper examines the concept of the supply chain and its management. Central to this concept is the notion of process integration which indeed epitomises what the supply chain stands for. Supply chain integration is considered within the framework that this occurs in stages and there exists a positive correlation to organisational performance. The role of technology is highlighted.

A maturity scale is proposed by integrating four previous models on supply chain integration. The contributions of these models to the maturity scale are discussed. These allow comparisons to be made with the view to highlight the contributions the maturity scale is expected to make to the literature.The conclusion drawn from the comparisons is that the maturity scale not only integrates ideas from the other models (e.g. a maturity progression in terms of technology, people, processes, etc) but also incorporates the concept of ‘process knowledge’.

1. Introduction

The debate on supply chain integration has spanned over two decades. The seminal work by Stevens (1989) gave stage-based and maturity dimensions to the subject. The benefits of an integrated supply chain include the capability to deal with dynamic market pressures manifested through issues of mass customisation, time compression, agility and leanness of the supply chain, to name a few. Continuing the debate along the stage-based perspective, Frohlich and Westbrook (2001) posed the question, “which types of integration lead to the greatest overall performance?” To answer this question one has to seek an understanding of the factors that contribute to an integrated supply chain.

Throughout the discussions on stage-based supply chain integration, many contributory factors have been proposed by researchers to provide a typology on the subject. Contributory factors to supply chain integration are differentiated into the ‘hard’ issues (such as technology) and the ‘soft’ issues or infrastructural developments (e.g. relations, attitudes, etc). Developments and evolving technologies coupled with a change in relations and attitude facilitated integration of the supply chain (Stevens (1989). Through technologies, coordination of business processes within and across organisational boundaries has been made possible. This is made easier when accompanied by collaborations on the ‘soft’ issues.

The technologies in question are the administrative component of advanced manufacturing technology (AMT), and following the definition by Adler (1988). This comprise technological systems such as materials requirements planning (MRP), manufacturing resource planning (MRPII), enterprise resource planning (ERP), and supply chain event management (SCEM), which have evolved in order to address integration needs starting within the organisation and progressing into the supply chain. These are classified together as manufacturing planning and control systems (MPC). To link these MPCs across the supply chain information and communication technology (ICT) such as electronic data interchange(EDI) and web-based interchange(WBI) are used.

Proposing that a fit must exist between the hard and soft issues in order to predict performance, Shah et al. (2002) put forward a model on supply chain integration based on such an alignment or fit. A similar model is suggested by Anderson and Lee (2000) but with more emphasis on the use of the internet and hence e-business to link up with customers and suppliers.

Taking into considerations the various contributions to the literature on supply chain integration, a maturity model is put together. The motivation provided by the various models is to seek out ways to extend the knowledge on the current literature by incorporating into the maturity model aspects not present in all the previous ones. We therefore proposed that technological knowledge and business process skills suggested by Stratman and Roth (2002) as critical to the success of ERP, be incorporated into maturity model.

Further, rather than testing for performance improvements through the organisations’ state of integration (e.g. internally integrated, externally integrated with customers and/or suppliers), it is proposed that the activities underlying each of these transitions are themselves tested to find out the true performance drivers since for example an organisation with external integration with suppliers may be performing very well as a result of its internal integration and not due to external integration.

A maturity model is thus developed along the above discussion and comprises supply chain technological integration, infrastructural development, advanced manufacturing technology characteristics, technological knowledge and business process skills, and finally performance. Each of these are defined and measured across the supply chain using a survey and case studies. The results of these studies are given in Aryee (2004). This paper will only compare existing frameworks on supply chain maturity models appearing in the literature, which resulted in the maturity model which was tested in Aryee (2004).

2.Migratory Stages of Supply Chain Integration Typologies

Various supply chain models are examined with the view to draw on their common attributes and identify gaps to address in the final model for the study.

Models of stage-based integration of the supply chain appear in the literature where process integration across the network occurs in stages as information is shared between the echelons of the supply chain (Stevens (1989); Frohlich and Westbrook (2001); Berry et al. (1999);Anderson and Lee (2000); Berger and Gattorna (2001); Evans et al. (1993). Two notable examples of such models are provided by Stevens (1989) and Anderson and Lee (2000). These are illustrated in figures 1 and 2 respectively. These examine process integration from within an organisation and into the supply chain. The main difference in the two models is the emphasis place on technologies such as the internet in the model by Anderson and Lee, being a fairly recent model.

By combining the models, two issues emerge; technology and infrastructural developments. By infrastructural development, organisational and attitudinal issues are considered. The notion here is that technology and infrastructural development act in concert to bring about the desired integration which takes place in stages.

Figure 1 Steven’s model of supply chain integration (source: Stevens1989)

Figure 2 Supply chain continuum (Adapted from source: Anderson and Lee, 2000)

Another notion of stage-based supply chain integration is put forward by Frohlich and Westbrook (2001), in what they term ‘arcs of integration’. Here, they proposed varying degrees of integration with either customers or suppliers (or both), as illustrated in figure 3. This model only considered integration beyond the organisation’s boundaries. This is equivalent to the external integration stage of Steven’s (1989) model but carried out at a more in-depth level. In ‘arcs of integration’ Frohlich and Westbrook (2001) suggest that the level of external integration is positively correlated to organisational performance. With respect to organisational performance, the other two stage-based models (i.e. Stevens, 1989; Anderson and Lee, 2000) suggest that better organisational performance occurs as the supply chain is progressively integrated.

Figure 3 Arcs of Integration (Source Frohlich and Westbrook, 2001)

2.1 Linking Technologies and Strategies

In essence, strategies emphasize the need for inclusion in integration mechanisms, business driven initiatives other than technology ones alone to bring about the idea of fit or alignment between strategy and technology (Kathuria and Igbaria (1997); Swamidass and Kotha (1998); Kathuria et al. (1999); Kotha and Swamidass (2000); Somers and Nelson (2003). This has implications for performance as proposed in a matrix by Shah et al. (2002). This infers that firms belong to increasing grouping levels of collaborative strategies and technologies along both a horizontal and vertical direction with respect to increasing performance. The diagonal positioning offers the best fit and hence the best performance. This is illustrated in figure 4. However the notions of fit are debated by Das and Narasimhan (2001) in a process and technology framework with implications for manufacturing performance. They argue that “off-diagonal” positions may offer opportunities for superior performance; a paradox requiring further research. This off-diagonal position of better performance is again confirmed by Stock et al. (2000) involving enterprise logistics and supply chain structure.

IOIS Stages
Supply
Chain strategies / No Electronic Integration / Level 1 Integration / Level 2 Integration / Level 3 Integration
Arm’s Length
Type I
Short term /
Type II
Long term
Type III
Coordination

Figure 4 A Matrix of Supply Chain Management and Interorganisational Information Systems (IOIS) (Source: Shah et al, 2002)

1. A Maturity Model

The model was put together following a thorough review of the literature. It represents an assembly of what is perceived as gaps in the literature on different research views of supply chain integration. A cumulative stance is thus adopted thereby building on past research in the field. Contributory streams to stage-based supply chain integration that appear in the literature are provided byStevens (1989), Berger and Gattorna (2001), Anderson and Lee (2000), and Frohlich and Westbrook (2001). The model takes as its starting point a three-element based supply chain structure of business processes, network structure and management components Lambert et al. (1998). These are then mapped onto three levels of technological and collaborative integration which are defined as optimisation, integration, and synchronisation of processes. Optimisation considers a functional area in isolation, which in effect leads to a sub-optimisation of the organisational unit and the supply chain. By integration a process view is taken of the entire organisation. This stance is taken further into the supply chain to embody customers and suppliers. We therefore have a continuum representing degrees of technological and collaborative integration across the supply chain. Such a continuum describes what Akkermans et al. (2004) refer to as transparency in an evolving supply chain collaboration, “what has remained under-researched so far is how this supply chain transparency is to be achieved in organisational terms, how it evolves over time.” In a cluster analysis involving 12 manufacturing firms, Caldeira and Ward (2002) again observe that adoption of information systems and technologies in manufacturing is an evolving process.

Within each of the stages in this continuum, the appropriate performance measures, business process skills and learning are put in place. The resulting model is depicted in figure 5. Each of the elements within the maturity model will now be discussed.

Supply Chain Supply Chain Performance Process knowledge

Framework Continuum Measures

ICT & MPC

ICT & MPC

Linking the stages to performance metrics

Progression through the stages

Linking to supply chain management framework

P - Business Process
N - Supply Chain network Structure
M - Management Components
A1 - Optimization of processes through technology and infrastructural developments
A2 - Integration and collaboration of processes through technology and infrastructural developments
A3 – Co-ordination and synchronisation of processes through technology and infrastructural developments
ICT – Information and Communication Technologies.
MPC – Manufacturing Planning and Control Systems / B1 - Functional-based performance measures
B2 – Enterprise-wide performance measures
B3 – Cross –Enterprise performance measures
C1 – Functional knowledge/learning
C2 - Organisational knowledge/learning
C3 – Inter-organisational knowledge/learning.

Figure 5 A Stage-based Maturity Model (Aryee et al. (2002)

3.1 Collaborative Technologies

Adler (1988) defines AMT as comprising design-, manufacturing- and administrative automation. Although the administrative component is the main subject of this study, a framework is put forward to examine how AMT in its entirety is integrated across the supply chain. MPC systems are designed to handle this administrative aspect of AMT. A generic model of MPC systems put forward by Vollman et al. (1997) is added by incorporating a design and manufacture database (i.e. CAD) within the organisation. For a more complete integration, quality management is further adapted to the Vollman framework.A whole perspective is thus gained on the complete product design, manufacture, and quality management. This is integrated with existing processes in the original vollman framework and extended into the entire supply chain, bringing in suppliers and customers. This is shown in figure 6. The adaptation made here to the Vollman framework is the inclusion of CAD and quality management components. Further adaptations are also made by bringing in the customers’ (i.e. their purchasing and CAD systems) and suppliers’ systems (i.e. their demand management and CAD systems).

Berry et al. (1998) posits the pipeline survey methodology which looked at information integration within MPC systems much akin to the collaborative technologies being studied here. Although the Vollman framework starts with resource planning, production planning, and demand management, the focus in this research is on how the demand management process is followed through to fulfil the product delivery process. This is done after establishing the nature of the master production schedule as offered through the end item options such as make-to-order, assemble-to-order and make-to-stock.

MPC technologies have followed an evolutionary path from MRP, MRPII through ERP to recently SCEM. The first three are process-driven but recent focus is shifting onto event-driven technologies as offered in SCEM, with the accompanying benefits of exception handling in the supply chain (Montgomery and Waheed (2001, Ghiassi and Spera (2003). Table 1 shows how the technologies fit in with other elements of the maturity model. A cumulative perspective is taken in examining how these technologies have matured in concert with the rest of the framework.

Figure 6An MPC framework for the supply chain (Adapted from source: Vollman et al, 1997)

In terms of communication technologies EDI and WBI offer connectivity of the business processes present in the MPCs across company boundaries. Web-based

interchange perceived to slow down EDI growth could rather offer opportunities for their joint operation (Stefansson (2002)

Table 1 Elements of the maturity model including MPC technology

Supply Chain
Continuum / Infrastructural Developments / MPC & ICT / Performance Measures / Process Knowledge
Optimisation / Internal / MRP, MRPII / Functional-based / Operational
Integration & Collaboration / Internal / ERP / Enterprise wide / Organisational
Co-ordination & Synchronisation / External / SCEM, EDI, WBI / Across enterprise / Inter-organisational.

3.2Infrastructural Developments

The foregoing discussion on supply chain integration focused on the ‘hard’ issues involving technology use. However, organisational procedures and management methods are a necessary addition to most business frameworks. These constitute the ‘soft’ issues which are referred to as infrastructural development in this study. The importance of infrastructural developments hinges on the fact that solid relations within the supply chain are a prerequisite for any subsequent technological collaboration. We therefore propose an inside-out approach by first developing internal linkages and teamwork within the organisation before extending the relationship into the supply chain. Menachof and Son (2002) sum up the various interactions into two elements; the nature and infrastructure of collaborative relationships. These include trust, risk sharing, and technology transfer amongst others. Infrastructural development in the supply chain is divided into internal and external infrastructure. Internal infrastructure examines the linkages, relationships, organisational structure, and training within the enterprise. External infrastructure looks at relations with suppliers and customers in terms of the length of trading, technology transfer between trading partner, and risk sharing, amongst others.

3.3 Technological Knowledge and Learning

The link between technological knowledge and performance is evident in extant literature. Bohn (1994) postulates a framework of knowledge stages with correlations to efficiencies and effectiveness in the organisation. There is further, an association with learning as being the progression of knowledge over time. In terms of collaboration, Grant (1996) suggests knowledge integration both within and across organisations.

The technologies are primarily brought in to improve the planning and execution of business processes. The appropriate skill level is judged by firstly understanding fully the entire business process before attempting to seek a fit between the technologies’ capabilities and the business process it is supposed to enhance or automate.Cooper and Zmud (1990)and Pagell et al. (2000) propose that a “fit” be accounted for between the technology being examined and the work context which the technology is being introduced. The framework put forward by Bohn (1994) on knowledge stages in process technology is utilised to assess skill levels at the desired unit of analysis.

3.4 Performance Measures

Performance is used as the criterion for successful supply chain integration. In operationalising this construct from the maturity model, certain key contributions from the literature proved instrumental. The model proposed by Kaplan and Norton (1996) highlighted the inclusion of financial, customer, internal business processes, learning and growth perspectives as measures of organisational performance. Others such as Lapide (2000) put forward entire supply chain performance measurements. The issue of considering dynamic change and time-based effects on performance is mentioned by Bittici and Turner (2000) and Jayaram et al. (2000). As a result, the multi items scale chosen to measure performance takes a multi-dimensional and comprehensive view and is grouped into operational and business performance.

Traditionally, business performance is seen in the light of financial measures and includes metrics of returns on investment or assets, sales growth, and market share. This is the starting point in building balanced measures by beginning with the strategic perspective and working backwards to identify operational performance metrics (Whitaker et al. (2002)

Whitaker et al. (2002) describe operational performance as essentially the source of the “why” and “how” that accompanies the “what”. This linkage provides a way to finding what needs to be targeted in order to improve performance. Linking operational performance metric to technology and best practise usage yields measures such as production cycle time, new product time to market, sharing of demand data. The measures selected consist of the following: