The impact of process maturity and uncertainty on ... - Semantic Scholar

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Int. J. Manufacturing Technology and Management, Vol. 15, No. 1, 2008

The impact of process maturity and uncertainty on supply chain performance: an empirical study Archie Lockamy III* School of Business, Samford University, 800 Lakeshore Drive, Birmingham, AL 35229, USA E-mail: [email protected] *Corresponding author

Paul Childerhouse School of Management The University of Waikato Te Whare Wananga o Waikato, Gate 1 Knighton Road, Private Bag 3105, Hamilton, New Zealand E-mail: [email protected]

Stephen M. Disney and Denis R. Towill School of Business, Cardiff University, Colum Drive, Cardiff CF10 3EU, UK E-mail: [email protected]

E-mail: [email protected]

Kevin McCormack College of Management, North Carolina State University, 2102 Nelson Hall, 2801 Founders Drive Raleigh, NC 27695-8614, USA E-mail: [email protected] Abstract: The concept of process maturity suggests that a process has a lifecycle that is assessed by the extent to which it is defined, managed, measured, and controlled. Organisational policies, standards, and structures are institutionalised as organisations increase their process maturity, leading to higher levels of process capability. This concept has been applied to supply chains by researchers through the development of a supply chain maturity model for enhanced supply chain performance. In addition, recent studies have shown that improved supply chain performance can also be achieved by reducing supply chain uncertainty. This paper provides an empirical Copyright © 2008 Inderscience Enterprises Ltd.

The impact of process maturity and uncertainty on supply chain performance

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examination of the relationship between process maturity and uncertainty, along with their impact on supply chain performance. Based on this examination, the paper also provides a framework for supply chain improvement. Keywords: modelling; process management; statistical analysis; Supply Chain Management; SCM; supply chain uncertainty. Reference to this paper should be made as follows: Lockamy, A., Childerhouse, P., Disney, S.M., Towill, D.R. and McCormack, K. (2008) ‘The impact of process maturity and uncertainty on supply chain performance: an empirical study’, Int. J. of Manufacturing Technology and Management, Vol. 15, No. 1, pp.12–27. Biographical notes: Archie Lockamy III is the Margaret Gage Bush Professor of Business at Samford University in Birmingham, Alabama, USA. Paul Childerhouse is a Professor at Waikato University in New Zealand. Stephen M. Disney is a Professor at Cardiff University in Wales, UK. Denis R. Towill is a Professor at Cardiff University in Wales, UK. Kevin McCormack is an Adjunct Professor at the North Carolina State University in Raleigh, North Carolina, USA.

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Introduction

Firms in the 21st century are seeking ways to counteract the effects of increasing levels of global competition, demanding customers and employees, shrinking product lifecycles and decreasing acceptable response times on their success in the market place. Historically, competition in many industrial sectors has been based primarily on the efficient and effective deployment of tangible strategic assets. However, competition is now based on capabilities, or “complex bundles of skills and accumulated knowledge, exercised through organisational processes” (Day, 1994). Firms are also extending their enterprises outside of their legal boundaries by forming competitive networks of organisations (i.e. supply chains). These enterprises need to develop strategically aligned capabilities not only within the firm, but also among the organisations that are part of its value-adding networks. This new business approach has caused many firms to now view processes as strategic assets. These firms no longer view themselves as a collection of functional areas, but as a combination of highly integrated processes (Hammer and Champy, 1993; Buxbaum, 1995; Hammer, 1996, 1999). In addition, processes are now viewed as assets requiring investment and development as they mature. Thus, the concept of process maturity is becoming increasingly important as firms adopt a process view of the organisation. This concept proposes that a process has a lifecycle that is assessed by the extent to which the process is explicitly defined, managed, measured and controlled. The process maturity concept is analogous to that of a lifecycle, which occurs in

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developmental stages. This concept also implies growth in the areas of process capability, richness and consistency across the entire organisation (Dorfman and Thayer, 1997). Reducing supply chain uncertainty leads to enhanced supply chain performance (Childerhouse and Towill, 2004). Supply chain uncertainty refers to the inability of the supply chain to effectively respond to customer demand and product variation requirements due to unanticipated volatility in the system’s value streams (Childerhouse and Towill, 2004). Mason-Jones and Towill (1998) have identified four sources of uncertainty associated with supply chains: process uncertainty, supply uncertainty, demand uncertainty, and control uncertainty. These sources of uncertainty adversely affect material and information flows (i.e. value streams), which directly impacts supply chain performance (Childerhouse and Towill, 2002). Process uncertainty affects an organisation’s internal ability to meet production targets. It is established by understanding yield ratios and lead-time estimates for each work process. Also, if the value stream for a given process is competing against other value streams for resources, then the interaction between these value streams must be studied and codified. Supply uncertainty results from suppliers who fail to consistently meet stated delivery, quality and other critical requirements. It can be evaluated by examining supplier delivery performance, lead-times, and quality levels. Demand uncertainty is associated with specific customers in relation to schedule variability and the transparency of information flow. It is indicated by the degree to which an organisation can consistently meet customer demand requirements. This is analysed by collecting data on customer orders, deliveries and forecasts. Finally, control uncertainty is concerned with how decisionmaking affects an organisation’s ability to transform customer orders into production targets and supplier orders. It is investigated via the analysis of customer orders, production targets, and supplier orders, along with a thorough understanding of the control systems that are used to convert customer orders into production targets and corresponding supplier orders.

1.1 Purpose The purpose of this paper is to present research findings that suggests a significant relationship between process maturity, process uncertainty, and supply chain performance. In addition, a supply chain process maturity model is introduced in the paper that can be used to help facilitate improved supply chain performance. The paper also presents a diagnostic tool for assessing supply chain uncertainty called Quick Scan (QS), along with a framework for supply chain improvement. This paper contains: Ɣ

a discussion on the concept of Business Process Orientation (BPO)

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a discussion on the concept of process maturity

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the BPO maturity model

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the Supply Chain Management (SCM) maturity model

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a discussion on the concept of supply chain uncertainty

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the QS methodology

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statistical results relating process maturity and uncertainty

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analytical results relating process maturity and performance

The impact of process maturity and uncertainty on supply chain performance Ɣ

conclusions regarding the impact of process maturity and uncertainty on supply chain performance

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a framework for supply chain improvement

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suggestions for future research.

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Business Process Orientation

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The concept of BPO is based on the work of Porter (1985), Deming (Walton, 1986), Davenport and Short (1990), Hammer and Champy (1993), Grover et al. (1995), Coombs and Hull (1996) and Hammer (1996, 1999). This body of work suggests that firms can enhance their overall performance by adopting a ‘process view’ of the organisation. Although many firms have adopted the BPO concept, little to no empirical data existed to substantiate its effectiveness in facilitating improved business performance. To address this issue, McCormack and Johnson (2000) conducted an empirical study to explore the relationship between BPO and enhanced business performance. The research results showed that BPO is critical in reducing conflict and encouraging greater connectedness within an organisation, while improving business performance. Moreover, companies with strong measures of BPO showed better overall business performance. The research also showed that high BPO levels within organisations led to a more positive corporate climate, illustrated through better organisational connectedness and less internal conflict. In addition, the study revealed the following key BPO elements: Ɣ

Process management and measurement – metrics that include aspects of the process such as output quality, cycle time, process cost and variability, as compared to the traditional accounting measures.

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Process jobs – jobs that focus on processes as opposed to functions, and are crossfunctional in responsibility.

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Process view – the cross-functional, horizontal picture of a business involving elements of structure, focus, measurement, ownership and customers.

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Process maturity

The concept of process maturity proposes that a process has a lifecycle that is assessed by the extent to which the process is explicitly defined, managed, measured and controlled. It also implies growth in process capability, richness and consistency across the entire organisation (Dorfman and Thayer, 1997). As an organisation increases its process maturity, institutionalisation takes place via policies, standards and organisational structures (Hammer, 1996). The process maturity concept has been developed and tested relative to the software development process (Harter, Krishnan and Slaughter, 2000) and the project management process (Ibbs and Kwak, 2000). Lockamy and McCormack (2004b) authored one of the first published studies that examined process maturity relative to SCM. In investigating the maturity concept relative to the software development process, the researchers used an assessment instrument developed by the Software Engineering Institute (SEI) (2002)

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along with outcome measurements (e.g. quality and cycle time) developed specifically for the study. The researchers found that the net effect of process maturity was a reduction in overall software development cycle time and software development effort. In examining project management process maturity, Ibbs and Kwak (2000) used the basic concepts contained in the SEI model and developed specific questions from the project management institute’s body of knowledge. This model represented five levels of project management maturity. The model was then used to examine the level of maturity across several industries. The relationship between maturity and performance was examined through interviews with participants. Although statistical relationships between maturity and performance were not examined, the interview results indicated a general acceptance that higher levels of project management maturity resulted in improved project performance. As organisations increase their process maturity, institutionalisation takes place via policies, standards and organisational structures (Hammer, 1996). Building an infrastructure and a culture that supports the BPO methods, practices, and procedures enables process maturity to survive and endure long after those who have created it. Continuous process improvement, an important aspect of BPO, is based on many small evolutionary rather than revolutionary steps. Continuous process improvement serves as the energy that maintains and advances process maturity to new maturity levels. The proposed relationship between process maturity and BPO is shown in Figure 1. Figure 1

Relationship between BPO and process maturity

As processes mature, they move from an internally focused perspective to an externally focused system perspective. A maturity level represents a threshold that, when reached, will institutionalise a total systems view necessary to achieve a set of process goals (Dorfman and Thayer, 1997). Achieving each level of maturity establishes a higher level of process capability for an organisation. This capability, as shown in Figure 2, can be defined by:

The impact of process maturity and uncertainty on supply chain performance Ɣ

Control – defined as the difference between targets and actual results, noting the variation around these targets.

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Predictability – measured by the variability in achieving cost and performance objectives.

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Effectiveness – the achievement of targeted results and the ability to raise targets.

Figure 2

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Relationship between process capability and maturity

The BPO maturity model

Lockamy and McCormack (2004b) developed the BPO maturity model illustrated in Figure 3 via an analysis of the concepts of process maturity, BPO, and the capability and maturity model created by the Software Engineering Institute at Carnegie Mellon University (SEI, 2002). The model and a description of each maturity level are shown in Figure 3. It is important to note that each maturity level builds a foundation from which to achieve the subsequent level. Therefore, an organisation must evolve through these levels to establish a culture of process excellence.

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Supply Chain Management maturity model

The BPO maturity model illustrated in Figure 3, discussions with supply chain experts and practitioners, and supply chain survey data organised by variables relating to different maturity levels, was used by Lockamy and McCormack (2004b) as a basis for developing the SCM maturity model illustrated in Figure 4. The model proposes five levels of maturity, and each level contains characteristics associated with process

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maturity such as predictability, capability, control, effectiveness and efficiency. The following is a brief description of each SCM maturity level: Ɣ

Ad hoc – The supply chain and its practices are unstructured and ill defined. Process measures are not in place. Jobs and organisational structures are not based on horizontal supply chain processes. Process performance is unpredictable. Targets, if defined, are often missed. SCM costs are high. Customer satisfaction is low. Functional cooperation is also low.

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Defined – the basic SCM processes are defined and documented. Jobs and organisation basically remain traditional. Process performance is more predictable. Targets are defined but still missed more often than not. Overcoming the functional silos takes considerable effort owing to boundary concerns and competing goals. SCM costs remain high. Customer satisfaction has improved, but is still low.

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Linked – this represents the breakthrough level. Managers employ SCM with strategic intent and results. Broad SCM jobs and structures are put in place outside and on top of traditional functions. Cooperation between intra-company functions, vendors and customers takes the form of teams that share the common SCM measures and goals that reach horizontally across the supply chain. Process performance becomes more predictable and targets are often achieved. Continuous improvement efforts take shape focused on root cause elimination and performance improvements. SCM costs begin decreasing and feelings of esprit de corps take the place of frustration. Customers are included in process improvement efforts and customer satisfaction begins to show marked improvement.

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Integrated – the company, its vendors and suppliers, take cooperation to the process level. Organisational structures and jobs are based on the SCM procedures, and traditional functions, as they relate to the supply chain, begin to disappear altogether. SCM measures and management systems are deeply imbedded in the organisation. Advanced SCM practices, such as collaborative forecasting and planning with customers and suppliers, take shape. Process performance becomes very predictable and targets are reliably achieved. Process improvement goals are set by the teams and achieved with confidence. SCM costs are dramatically reduced and customer satisfaction and esprit de corps become a competitive advantage.

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Extended – competition is based on multi-firm supply chains. Collaboration between legal entities is routine to the point where advanced SCM practices that allow transfer of responsibility without legal ownership are in place. Multi-firm SCM teams with common processes, goals and broad authority take shape. Trust, mutual dependency and esprit de corps are the glue holding the extended supply chain together. A horizontal, customer-focused, collaborative culture is firmly in place. Process performance and reliability of the extended system are measured and joint investments in improving the system are shared, as are the returns.

The impact of process maturity and uncertainty on supply chain performance Figure 3

The BPO maturity model

Figure 4

The Supply Chain Management maturity model

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Supply chain uncertainty

The following definition of supply chain uncertainty is provided by van der Vorst and Beulens (2002): “Supply chain uncertainty refers to decision-making situations in the supply chain in which the decision maker does not know definitely what to decide as he is indistinct about the objectives; lacks information about (or understanding of) the supply chain or its environment; lacks information processing capabilities; is unable to accurately predict the impact of possible control actions on supply chain behaviour; or, lacks effective control actions (noncontrollability).”

Childerhouse and Towill (2004) examined process, supply, demand, and control uncertainty in the value streams of 32 European firms representing the automotive, electronic, mechanical precision products, and construction industries. The researchers found that supply chain uncertainty significantly contributes to poor customer service levels, excess inventory, long lead times, increased quality inspections, and bureaucratic decision-making processes. In addition, the researchers discovered that improved information flow and smoother material flow simplifies the task of synchronisation and coordination among values streams, leading to better decision-making within the supply chain. Finally, the study provides empirical evidence that reductions in value stream uncertainty have a significant beneficial impact on supply chain performance. In conducting the above-mentioned study, the researchers used a diagnostic tool for assessing supply chain uncertainty called QS. A description of the QS methodology is provided below.

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Quick Scan

QS is a systematic approach used for the collection and synthesis of qualitative and quantitative data from a supply chain (Naim et al., 2002). The methodology has been applied to over 20 different supply chains. QS utilises the following four techniques: questionnaire analysis, process mapping, semi-structured interviews, and modelling from numerical data. A multi-disciplinary team comprised of researchers, site engineers, and site managers spend approximately two weeks at the organisation collecting and analysing its supply chain data. Process mapping is critical to the QS process. It allows for the determination of value stream flows, internal supply chains, and critical interfaces involving both customers and suppliers. This procedure includes the identification of both value added and non-value added processes. An illustration of the primary data collected and analysed during the QS process is provided in Table 1. Off-site data analysis is the next phase of the QS methodology. Brainstorming sessions are conducted by the team to triangulate data from all sources, identify gaps in knowledge requiring further investigation, and to resolve any inconsistencies. A rigorous analysis of the information is conducted which allows key problem areas and issues to be highlighted. Thus, the output of this phase is a clear assessment of: the current status of the organisation and its supply chain; the maturity of its practices and processes; and, their ability to meet current and future customer needs. In addition, the QS methodology provides insight not only on the management of the four sources of uncertainty, but also


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the interfaces between these areas. A diagram illustrating this concept is presented in Figure 5. Table 1

Data analysed during Quick Scan (QS) investigations

Uncertainty source

Typical primary data analysed during QS investigations

Supply side

Purchase Orders (POs) placed on suppliers especially schedule adherence, invoices, call-offs, Bill of Materials (BOM), forecasts, receipts, supplier quality reports, Material Requirements Planning (MRP), lead-times, stock reports.

Demand side

Delivery frequency, echelons to end consumer, marketplace variability, stage of product life cycle, supply chain ordering decisions, forecast accuracy.

Process side

Scrap reports, cycle times and variability of cycle times, production targets and output, downtime reports, stock consolidations, BOM with cost information, capacity planning, and asset register.

Controls side

Time series of customer orders, supplier orders, demand forecasts, Kanban logic, batching rules, MRP logic, call-offs, purchase orders, BOM number of variants, delivery frequency and the number of completing value streams.

Figure 5

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The four sources of uncertainty monitored via Quick Scan

Process maturity and supply chain uncertainty

The impact of process maturity and uncertainty on supply chain performance was examined via a joint analysis of two individual databases. The first database contained information used to establish linkages between Supply-Chain Operations Reference model planning practices to supply chain performance (Lockamy and McCormack, 2004a). This database was also used to construct the supply chain maturity model illustrated in Figure 4. The information was collected via a supply chain process maturity measurement instrument that incorporated the BPO maturity attributes exhibited in Figure 1. The instrument was administered to 523 key informants of the Supply Chain Council (SCC) representing 90 firms based in the USA. Fifty-five usable surveys were

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returned for a response rate of 10.5%. An analysis of non-response bias was made to determine its impact on the data. The analysis revealed no discernible impact. The second database contained uncertainty information collected via an analysis of the value streams of 32 European firms representing the automotive, electronic, mechanical precision products, and construction industries. The QS methodology was used to collect the information for 23 of the firms, while in-depth structured interviews with supply chain managers were used to collect uncertainty information within the remaining firms. This information has been used to establish statistical relationships between uncertainty and supply chain performance (Childerhouse and Towill, 2004).

8.1 Database integration The integration of the process maturity and uncertainty databases was accomplished by conducting an assessment of the European value stream maturity levels via the supply chain process maturity measurement instrument. A total of 18 of 23 firms in the uncertainty database whose information was collected via the QS methodology provided sufficient information for conducting the maturity assessment. The maturity scores were plotted against performance information contained in the uncertainty database to examine the relationship between maturity and performance for the dataset. In addition, the maturity scores for process maturity and uncertainty databases were compared to detect inconsistencies. Finally, regression analysis was conducted to establish statistical relationships between process maturity and uncertainty variables.

8.2 Joint database analysis The first step in conducting an analysis of the joint databases was to compare the maturity score distributions of the individual databases. These distributions are illustrated in Figures 6 and 7. An examination of these figures revealed that the maturity score distributions appear to be very similar for both datasets. Figure 6

Maturity scores for process maturity database

The impact of process maturity and uncertainty on supply chain performance Figure 7

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Maturity scores for supply chain uncertainty database

Linear regression analysis was then used to examine relationships between process maturity and uncertainty variables. Table 2 contains the adjusted R2 values for each relationship. All of the relationships illustrated in Table 2 were significant at the 0.01 or less level. The analysis also revealed that inverse relationships existed between the variables. The relationship with the largest R2 value (0.84) was between the control uncertainty and process structure variables. A large R2 value (0.80) was also observed between the control uncertainty and jobs variables. In addition, a strong relationship existed (R2 = 0.49) between the supply uncertainty and process structure variables. Finally, demand uncertainty (R2 = 0.51) and control uncertainty (R2 = 0.48) had a strong relationship with the measures variable. Table 2

Process maturity variables and supply chain uncertainty relationships: adjusted R² values

Process maturity variables

Process uncertainty

Supply uncertainty

Demand uncertainty

Control uncertainty

Process structure

0.33

0.49

0.11

0.84

Documentation

0.17

0.43

0.16

0.32

Jobs

0.41

0.35

0.25

0.80

Measures

0.27

0.37

0.51

0.48

Values/beliefs

0.23

0.42

0.45

0.40

In an effort to examine the relationship between process maturity and supply chain performance, performance ratings generated using the QS methodology for the 18 firms from the uncertainty database were plotted against their corresponding maturity scores. The QS performance ratings are based on an analysis of the following attributes for each firm:

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1

material flow integration levels

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process, supply, demand, and control uncertainty scores

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material flow complexity levels

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supplier, manufacturing, distribution, and total lead times.

An analysis of Figure 8 suggests that increased levels of supply chain performance are associated with supply chains that exhibit higher levels of maturity. Figure 8

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Process maturity vs. supply chain performance: uncertainty database

Conclusions

The results of this study lead to the following conclusions regarding supply chain process maturity, uncertainty, and performance. First, the results suggest that reductions in supply chain uncertainty can be achieved by increasing the maturity levels of processes employed within the supply chains. In addition, by increasing supply chain maturity levels and by correspondingly reducing uncertainty, firms can improve overall supply chain performance. The results also suggest that increased levels of maturity in the area of process structure and jobs can significantly reduce the level of control uncertainty within supply chains. Also, increased process structure maturity can reduce supply uncertainty. Finally, reductions in demand and control uncertainty can be achieved by increasing the maturity levels of process measures utilised within supply chains.

10 A framework for supply chain improvement As demonstrated by this study, improvements in process maturity and reductions in uncertainty lead to enhanced supply chain performance. A framework for achieving these objectives is provided in Figure 9. The framework illustrates the uncertainties associated


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with the value streams of 32 European firms contained in the uncertainty database in radar plot format (scaled 1 to 4) and in sequential order from worst practice to best practice (see Childerhouse and Towill, 2002). The ‘scores’ estimated at various stages of the framework are also shown as benchmarks and occupy the four corners of the figure. Note that the area enclosed within the radar plots is an indication of the total uncertainty experienced by an individual value stream. Also, the shape of these areas indicates where uncertainty reduction is an essential next step towards supply chain improvement. Figure 9

A framework for supply chain improvement

In applying this framework to an existing supply chain, the objective is to move the supply chain to the next level of integration (corner radar plots). Improvement requirements are, therefore, identified to reduce specific areas of uncertainties to desired levels. Once this has been accomplished, achieving the next level of integration becomes the new objective, with resultant improvement requirements tailored to further reduce uncertainties to these new desired levels. The process continues until external integration (i.e. best practice) has been achieved. The framework identifies process uncertainty reduction as the first step in supply chain integration since a firm’s own processes offer the most visible and accessible areas for improvement. Reducing supplier-induced uncertainty then follows, since this is normally the next area of greatest influence within the supply chain. Demand uncertainty is reduced in the final stage, as a change of focus is required to achieve customer integration. Finally, control uncertainty is continually reduced throughout the entire supply chain integration process.

11 Future research The empirical relationships of supply chain process maturity and uncertainty to performance contained in this study suggests that improvements in these areas will lead to increasing levels of supply chain excellence. However, a future research opportunity

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lies in determining if these relationships are influenced by industry type. Another research opportunity resulting from this study is to examine if the size of the supply chain has a significant impact on maturity, uncertainty, and performance relationships. Finally, the degree to which supply chain configuration characteristics affect these relationships also warrants future research.

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Porter, M.E. (1985) Competitive Advantage: Creating and Sustaining Superior Performance. New York, NY: Free Press. Software Engineering Institute (2002) ‘The rational unified process and the capability maturity model’, Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA. Available at www.sei.cmu.edu/cmmi/presentations/rup.pdf. van der Vorst, J.G.A.J. and Beulens, A.J.M. (2002) ‘Identifying sources of uncertainty to generate supply chain redesign strategies’, Int. J. Physical Distribution and Logistics Management, Vol. 32, pp.409–430. Walton, M. (1986) The Deming Management Method. New York, NY: Perigee Books.