To Reduce or Absorb Supply Chain Complexity



To Reduce or Absorb Supply Chain Complexity: A Conceptual Model and Case Study

Purpose - Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems (CAS) or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity, but without providing a thorough discussion about when and why they should be deployed. This research seeks to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity.

Design/Methodology/approach –This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. We illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer.

Findings - Regardless of its type or origin, supply chain complexity can arise due to the strategic business requirements of the BU (strategic) or due to suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that drivers of dysfunctional complexity can remain dormant until the system is stressed.

Practical Implications – The conceptual model presented here provides a more granular view of supply chain complexity, and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response.

Originality/value –We are aware of no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual business unit (BU) should respond to supply chain complexity. As such, this paper furthers our understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.

Key words Strategic complexity, dysfunctional complexity, supply chain complexity

Paper Type Research paper

1. Introduction

The literature on supply chain complexity has expanded over the last three decades, with researchers examining complexity from the perspective of where it derives from in relation to internal and external sources (Bozarth et al., 2009; Serdarasan, 2012), what it looks like in terms of quantifying the number of components/stages in the system (Vachon and Klassen, 2002), and how it can be generated from the dynamic interactions between stages (Sivadasan et al., 2006). Bozarth et al. (2009, p. 79) define supply chain complexity as “the level of detail complexity and dynamic complexity exhibited by the products, processes and relationships that make up a supply chain,” with detail complexity capturing “the distinct number of components or parts that make up a system” and dynamic complexity defined as “the unpredictability of system’s response to a given set up inputs.”

Supply chain complexity, in general, has been portrayed as having a negative impact on operational performance (Childerhouse and Towill, 2004; Hoole, 2005). However, this categorization of supply chain complexity as having only an adverse impact on a business’s performance has been challenged. For example, certain forms of supply chain complexity, such as expanded product lines and higher levels of customization, may be driven by a firm’s strategy to open up new markets and develop new products to increase revenues and profitability (Fisher, 1997; Bozarth et al., 2009). In effect, businesses need to understand the difference between “necessary” and “unnecessary” supply chain complexity, and respond accordingly (Serdarasan, 2012). Whilst much has been written about the what, where and how of supply chain complexity and its deleterious effects on operations performance (Heim et al., 2014), less has been written about when complexity should be accommodated and why particular tactics and tools should be employed. The purpose of this paper, then, is three-fold:

• To make a clear distinction between “necessary” and “unnecessary” supply chain complexity, and show how this distinction builds upon our current understanding of supply chain complexity;

• To identify appropriate organizational responses to supply chain complexity addressing the when and why gaps in the existing literature;

• To put forth a conceptual model for managing supply chain complexity which we illustrate using the case of a packaged goods manufacturer.

As will be discussed in the literature review, we will use the terms strategic and dysfunctional to refer to “necessary” and “unnecessary” supply chain complexity, respectively. Determining whether a driver of supply chain complexity is strategic or dysfunctional comes down to answering the question: “Can an individual firm reduce a particular source of supply chain complexity and still support its business strategy?” Note that this statement intentionally recognizes that from the perspective of an individual business unit (BU), supply chain complexity is not an exogenous variable beyond the firm’s control; rather, firms make conscious decisions about what level of supply chain complexity they are willing to take on. For example, Bozarth et al. (2009, p. 80) identifies “number of suppliers” as a driver of upstream complexity. If a manufacturer can reduce the number of suppliers and still support its business strategy, then it is dysfunctional complexity. If, on the other hand, a large supply base is required to carry out the business strategy, then the manufacturer must look for ways to absorb it.

In Section 2, we provide a detailed review of the existing supply chain complexity literature and make the case for adding a further distinction between strategic and dysfunctional drivers of supply chain complexity. Section 2 also reviews the existing literature regarding organizational responses to supply chain complexity. As the review demonstrates, the existing literature does not provide adequate guidance on when and why managers should respond to the various drivers of supply chain complexity faced by an individual firm.

In Section 3, we provide a conceptual model that seeks to fill this gap. Section 4 illustrates the conceptual model using data from a packaged goods manufacturer in the food industry sector. A triangulation methodology is employed to investigate the case study firm (e.g., longitudinal product data from the firms MRP system, semi-structured interviews and observations) and provides analysis of the firm’s responses to increased levels of supply chain complexity. Sections 5 and 6 contain a discussion of the results, followed by managerial implications and directions for future research.

2. Literature Review

Supply Chain Complexity

The earliest research on supply chain complexity emerged not long after the concept of supply chains as integrated systems of physical flows, information flows and relationships became broadly established in the business literature (Wilding, 1998; Vachon and Klassen, 2002; Choi et al., 2001). These early works built in large part on the established systems complexity literature, which sought to define the nature of complex systems. For example, Simon (1962, p. 469) defined a complex system as “one made up of a large number of parts that interact in a non-simple way”. Others such as Flood and Carson (1988) and Waldrop (1992) echoed this basic idea, with the twin themes of numerousness in interactions and components and “dynamism” (Waldrop, 1992) or unpredictability emerging as key characteristics.

Systems-level vs. Business Unit Perspectives on Supply Chain Complexity. Researchers have generally adopted one of two perspectives when examining supply chain complexity. The first considers supply chain complexity from a holistic, systems-level perspective (Choi et al., 2001; Choi and Krause, 2006; Gerschberger et al., 2012). For example, Gerschberger et al., (2012, p. 1018) note in their research on supply networks (SNs) that “the challenge for investigating complexity in SNs is to identify adequate methods for system representation, to investigate interdependencies between core elements, and the specification of adequate complexity parameters that also respect complexity emerging from the external environment.”

The second perspective considers supply chain complexity from the viewpoint of an individual business unit (BU) in the supply chain, such as a firm or manufacturing plant (Fisher, 1997; Wilding, 1998; Bozarth et al., 2009). From the BU perspective, the focus is not as much on understanding or measuring the degree of entropy characterized by a broadly defined supply chain network; rather, the interest is on how this complexity manifests itself within the BU and at its interface points with its external supply chain partners. It is this view of supply chain complexity that will be the focus of this research and our conceptual model.

Detail vs. Dynamic Complexity. The twin themes of numerousness and dynamism are found in the earliest works on operations and supply chain complexity, with Frizelle and Woodock (1995) investigating the effect of volume and uncertainty within operations; Wilding (1998) including “deterministic chaos” in his supply chain complexity triangle; Vachon and Klassen (2002) including numerousness, interconnectivity and systems unpredictability in their definition of supply chain complexity; and Choi et al. (2001) characterizing supply networks as complex adaptive systems (CAS).

The number of items in a system drives detail complexity. Typical drivers of detail complexity include the number of customers, products or parts found in a BU’s supply chain (Bozarth et al., 2009). As an illustration of dynamic complexity, numerous researchers have noted how through the interconnected nature of supply chains, supply chain complexity at one point in the chain can impact performance at other points in unanticipated ways (Bode and Wagner, 2015; Sivadasan et al., 2006). For example, a change to manufacturing schedules can have a significant knock-on effect on upstream activities. Supplier networks can be sensitive to disturbances in demand patterns which can lead to catastrophic failure of the supply chain to deliver value to the customer due to the cascading nature of complex systems (Choi et al., 2001). The Forrester effect (also known as bullwhip effect) is an example where a small change in demand can propagate through the supply chain leading to a large change in supply. It takes time for the demand/supply oscillations to return to their origin.

Internal vs. External Sources of Complexity. In addition to the type of complexity (detail or dynamic), the literature has also sought to identify where in the supply chain these complexity drivers occur vis-a-vis an individual BU. Serdarasan (2012) provides a comprehensive review of 38 papers that address supply chain complexity. Whilst a few of these works broadened the definition of supply chain complexity to include the complexity of the decision-making process itself as well as environmental factors beyond the control of individual organizations (Serdarasan, 2009; Manju and Sahin, 2011), the vast majority focused on the distinction between supply chain complexity drivers internal to a specific BU, such as excessive setup times or quality problems, versus those positioned at the BU’s supply/demand interface points, such as customer demand variances or variable supplier lead times. Bozarth et al. (2009) divide drivers of supply chain complexity into upstream (i.e., supplier-oriented), internal manufacturing, and downstream (i.e., customer-oriented) complexity. Their analysis of 209 manufacturing plants indicates that drivers of dynamic supply chain complexity—long supplier lead times, supplier delivery unreliability, master production schedule (MPS) instability, and demand variability— tended to have the most pronounced impact.

Strategic vs. Dysfunctional Complexity. Whilst researchers have generally focused on the negative performance impacts of supply chain complexity (Vachon and Klassen, 2002; Bozarth et al., 2009; Heim et al., 2014), it is important to realize why some level of supply chain complexity may be required or even desirable. For example, in his 1997 paper, “What is the right supply chain for your product?” Fisher argues that innovative products require “market-responsive” supply chains that combine flexibility with strategic use of capacity and inventory buffers. Gottfredson and Aspinall (2005) suggest that firms can find the ideal level of product line complexity by starting with a simple single product system and adding products or features until the marginal costs equal marginal revenues. In effect, Gottfredson and Aspinall (2005) argue that under certain conditions, increasing supply chain complexity will be strategically beneficial.

This idea has been echoed by Perona and Miragliotta (2004) and Serderasan (2012), albeit in somewhat abbreviated form. Perona and Miragliotta (2004) suggest that supply chain complexity can be “reduced” or “managed,” thereby shifting the trade-off curve between effectiveness (customer-related performance) and efficiency (cost-related performance). However, the authors do not provide any guidance regarding when a firm might prefer reducing over managing complexity, or why certain tactics might be employed. Similarly, Serderasan (2012) in a review of supply chain complexity drivers distinguishes between “necessary” and “unnecessary” complexity, but does not provide an explicit definition of either.

The terms “dysfunctional” and “strategic” were actually first used in an operations context by Suri (2010) in his book on Quick Response Manufacturing (QRM) to refer to variability within a production system. Suri (2010, p. 4) notes that variability can be either dysfunctional or strategic. Specifically, dysfunctional variability is caused by errors, ineffective systems, and poor organization. Examples of dysfunctional variability are: rework, constantly changing priorities and due dates, and “lumpy” demand due to poor interfaces between sales and customers. In contrast, strategic variability is used by an organization to have a sustainable competitiveness in the market.

It is our contention that this distinction between dysfunctional and strategic also applies to supply chain complexity. We therefore define dysfunctional complexity as supply chain complexity that is not required to carry out the BU’s business strategy and prevents the organization from achieving a higher level of performance. Examples of dysfunctional complexity can include product proliferation beyond that needed to support the organization’s chosen customer base (Gottfredson and Aspinall, 2005), excessive setup times and unreliable supplier lead times (Closs and Nyaga, 2010; Hayes et al., 2005).

In contrast, strategic supply chain complexity represents a level of supply chain complexity that is required to carry out the BU’s business strategy and therefore must be absorbed by the organization’s manufacturing and supply chain activities. Examples can include higher levels of product customization and customer heterogeneity – but only if dictated by the business strategy. In such environments, manufacturers that are able to successfully absorb higher levels of supply chain complexity in support of a distinct business strategy may have an advantage over those that myopically focus on reducing complexity and increasing productivity at the cost of competiveness (Fisher, 1997; Bozarth et al., 2009). Clarity on this matter should aid organizations in deciding when it is appropriate to take action and why certain tactics and tools are more fitting than others.

It is important to note that the distinction between strategic and dysfunctional complexity builds upon Bozarth et al., (2009) conceptualization, which only focuses on the position and detail/dynamic nature of supply chain complexity. As Figure 1 shows, supply chain complexity can be characterized by 1) its position in the firm’s supply chain, 2) whether it represents detail or dynamic complexity, and 3) whether it is required by the firm’s business strategy or not. Furthermore, this extended view of supply chain complexity suggests that two firms with identical types and level of supply chain complexity may react differently based on their individual business strategies.

To summarize the supply chain complexity literature:

1. Supply chain complexity can be characterized by the level of detail complexity and dynamic complexity found in the BU’s downstream customer base, internal processes, and upstream.

2. Drivers of supply chain complexity can originate within the BU (internal drivers), at the BU’s interface with customers and suppliers (external drivers), and within the BU’s decision-making processes and environmental factors.

3. Regardless of its type or origin, supply chain complexity can arise due to the business requirements of the BU (strategic) or due to suboptimal business practices (dysfunctional complexity). Depending upon the interpretation of the complexity, organizations can decide when to intervene and why certain actions or tactics are preferable.

These three themes are important to practitioners and researchers for several reasons. First, different types of complexity may have differential impacts on BU performance and require different solutions. Similarly, identifying the source of the complexity can help organizations isolate, and therefore better manage, the potential negative performance impacts. Finally, differentiating between strategic complexity and dysfunctional complexity provides guidance on which complexities must be absorbed by the BU and which can be reduced.

Take in Figure 1 here

Organizational Responses to Supply Chain Complexity

Complexity Reduction versus Complexity Absorption. In this section, we review the literature related to organizational responses to supply chain complexity. We will draw from three streams. The first stream comes out of the organization design literature, and generally argues that organizations can respond in one of two ways to environmental complexity: (1) reduce the complexity faced by the organization or (2) put in place mechanisms to absorb it (Galbraith, 1974 and 1977; Holland, 1995; Boisot and Child, 1999).

Boisot and Child (1999, p. 237) define complexity reduction as “getting to understand the complexity and acting on it directly, including attempts at environmental enactment.” It is important to note that complexity reduction is not the same as ignoring or oversimplifying the complexity faced by an organization; rather, it is a deliberate choice. As Boisot and Child (1999, p. 238) put it, under a complexity reduction approach, “[organizations] elicit the most appropriate single representation of that variety and summon up an adapted response to match it. Such a strategy leads to specialization informed by relevant codification and abstraction of the phenomenon.” In contrast, organizations can follow a capacity absorption strategy in which they “hold multiple and sometimes conflicting representations of environmental variety, retaining in their behavioral repertoire a range of responses, each of which operates at a lower level of specialization” (Boisot and Child; 1999, p. 238).

Complex adaptive systems (CAS) feature prominently in discussions of how systems can implement a capacity absorption strategy (Holland, 1995; Choi et al., 2001: Choi and Krause, 2006). According to Choi et al. (2001, p. 352), “the term ‘complex adaptive system’ refers to a system that emerges over time into a coherent form, and adapts and organizes itself without any singular entity deliberately managing and controlling it (Holland, 1995).” Whilst Choi et al.’s focus is on complexity emerging from a firm’s upstream network, the concept can be applied to other environments characterized by semi-independent agents.

As Choi et al.’s definition suggests, responses to environmental complexity do not always have to be introduced in a proactive, top-down manner, such as the implementation of a formal sales and operations planning (S&OP) system (Grimson and Pyke, 2007). Rather, organizational solutions can also emerge in a bottom-up, reactive manner via networked, semi-independent agents, tied together by interpretive and behavioral schema (Choi et al., 2001).

It follows then that the appropriateness of a complexity reduction or complexity absorption approach will be contingent on an individual BU’s chosen strategy. For example, in a study of eight firms in a highly complex and turbulent industry, Ashmos and Duchon (2000) found that those who followed a complexity absorption response outperformed those that pursed a complexity reduction response. In contrast, Modi and Mishtra (2011) conducted a longitudinal study of US based manufacturers over a 16-year period, comparing their financial performance. The authors found that “resource efficiency is positively associated with firm financial performance,” but also found some support for “arguments favoring slack [resources].”

Resource Efficiency versus Resource Slack. The second stream we consider here concerns the debate in the operations management literature regarding the proper balance between resource efficiency and slack. Proponents of the resource efficiency view have generally held that a focus on efficiency leads to greater operational stability and improved cost performance (Womack, et al., 1990; Chen, et al., 2005). Alternatively, proponents of the resource slack view suggest maintaining some level of slack resources enhances strategic flexibility and responsiveness (Abernathy, 1978; Rosenzweig and Easton, 2010).

Modi and Mishra’s (2011, p. 255) sum up the different perspectives this way: “In contrast to the literature focused on efficiency, other researchers have instead centered on environmental challenges facing firms, and in this context have examined the moderating role of resource slack in helping firms better adapt to unexpected environmental shifts.” Consistent with Modi and Mishra’s observation, we hold that the use of slack resources is one of the ways in which BUs seek to implement a complexity absorption strategy.

Organizational Mechanisms for Reducing / Absorbing Complexity. The third stream we consider involves actual organizational mechanisms for reducing or absorbing complexity. Whilst much has been written about the various dimensions of supply chain complexity and the pros and cons of complexity reduction versus absorption, we are unaware of any existing work that provides a conceptual framework that individual managers can use to select the correct organizational responses to the supply chain complexity faced by their individual BU. Rather, the works that have come closest to meeting this need have generally consisted of lists of different tactics and tools that can be applied at the BU level (Serdarasan, 2012; Perona and Miragliotta, 2004) or those that have sought to describe, model and/or measure the characteristics and responses to supply chain complexity at the systems level (Choi, et al., 2001; Sivadasan, et al., 2006).

For example, Serdarasan (2012) provides a list of solution strategies, such as reducing the number of products and options, outsourcing partners and distribution centers (what the author refers to as “static complexity”) and using a wide range of supporting tools, primarily IT-based, to increase collaboration with supply chain partners and improve supply chain visibility and information sharing (what the authors call “dynamic complexity”). Perona and Miragliotta (2004) treat complexity “reduction” and “management” as two equally viable approaches. Examples of complexity management “levers” mentioned by these authors include integrated information systems with customers and with suppliers, product modularization, PP&C information systems, and automated production resources. Examples of reduction “levers” include outsourcing of logistics and production activities, and information systems that allow component designs to be reused. Whilst we applaud these authors for addressing the issue of BU-level responses, lists of tactics and tools leave something to be desired. First, these lists do not directly address when and why a particular tactic or tool should be used. Second, as management practices and available technologies evolve, some of these tactics and tools will necessarily become outdated whilst new ones emerge.

In contrast to the above is the literature addressing the systems-level view of complexity, including various approaches to measuring, modeling and ultimately, explaining the behavior of large, multi-agent CAS. Examples include de Leeuw et al. (2013) and Gerschberger et al. (2012) which provide alternative ways of measuring the complexity of supply networks. Other authors have used a wide range of modeling approaches, including agent-based and neural network models (Datta et al., 2007; Wycisk et al., 2008) to analyze the behavior of complex supply chains and networks. Whilst the CAS perspective is useful in understanding how systems respond to complexity, it has notable limitations. For instance, Boisot and Child (1999, p. 238) note that “[H]uman organisations have the capacity to enact some of the representations [of complexity] that they construct for themselves, thus modifying their environment proactively as well as adapting to it,” something that biological systems cannot. Put another way, unlike flocks of birds or schools of fish, individual BUs have some freedom to choose which supply chains they wish to participate in.

To answer the question, then, “how should an individual BU respond to supply chain complexity?” we turn to Galbraith’s (1974, 1977) information processing view of organization design, which considers various organizational design strategies for addressing what he refers to as “task uncertainty.” Galbraith observed that task uncertainty “limit[s] the ability of an organization to preplan or make decisions about activities in advance of their execution” and “increase[s] the amount of information that has to be processed between decision-makers during task execution.” Whilst the supply chain complexity literature and Galbraith’s works are separated by more than two decades, we hold that supply chain complexity is a particular example of task uncertainty, and that Galbraith’s observations hold equally well for environments marked by high levels of supply chain complexity.

Galbraith posits that organizations have two basic strategies for absorbing task uncertainty, and by extension, supply chain complexity: 1) create slack resources or self-contained tasks that absorb the effects of uncertainty, or 2) invest in information systems or lateral relations that enhance the organization’s ability to process information during task execution. Examples of slack resources include inventory buffers or excess capacity, whilst examples of self-contained tasks include focused work cells or plant-within-a-plant (PWP) systems. It should be noted that IP enhancing mechanisms can include IT-intensive information systems as well as lateral relations. For instance, information systems can include such solutions as dynamic production scheduling applications and POLCA systems (Suri, 2010), which provide decision makers with the information needed to react to shifting bottlenecks on the plant floor, whilst lateral relations can include such mechanisms as cross-functional teams that provide a mechanism for quick, informal exchange of information during task execution. Galbraith’s emphasis on the potential usefulness of IP enhancing mechanisms is consistent with observations of Ashmos, et al. (2000, p. 579), who noted that “[c]onnections, especially rich connections, transmit information and enable meaning creating among subunits, thus providing systems with improved capacity to learn”

3. Conceptual Model

In this section we put forth a conceptual model (Figure 2) that describes how an individual BU should respond to supply chain complexity in order to maximize operational performance. The model is based on combining our research findings in the areas of supply chain complexity and organizational responses to complexity. Consistent with the literature, the supply chain complexity drivers faced by an individual BU can differ with regard to their type (detail versus dynamic); their origin (upstream, downstream or internal to the BU), and whether or not they are driven by the business strategy (strategic versus dysfunctional). Second, regardless of their type, origin or strategic nature, supply chain complexity drivers will tend to have a negative impact on operational performance (Bozarth, et al., 2009; Heim, et al., 2014; Serdarasan, 2012). The shaded boxes in Figure 2 capture these relationships.

We noted earlier that what constitutes strategic or dysfunctional supply chain complexity is dependent on the business strategy and can therefore differ across BUs, and can even change over time for a particular BU. A classic example is product line growth, which can occur as a conscious response to market conditions (strategic) or as a result of uncontrolled product proliferation (Hill, 1999; Gottfredson and Aspinall, 2005). Given the potential negative impacts of supply chain complexity, then, individual BUs need to address drivers of supply chain complexity in a logical manner; that is, they need to target their tactics based not only on the type and origin of the complexity drivers, but the strategic requirements of the firm. Consistent with the literature, individual organizations have two basic responses to managing complexity: reduce it or absorb it (Boisot and Child, 1999). As Figure 2 shows, reduction mechanisms seek to directly reduce dysfunctional supply chain drivers, thereby reducing the impact of these drivers on performance.

In contrast, absorbing mechanisms do not seek to reduce strategic complexity drivers, since these drivers are deemed to be necessary to carrying out the business strategy. Rather, absorbing mechanisms seek to moderate (i.e., lessen) their impact on performance. This is consistent with Modi and Mishra (2011.p. 255) who note the “moderating role of resource slack [one absorbing mechanism] in helping firms better adapt to unexpected environmental shifts.” We can further subdivide absorbing mechanisms into two distinct types: those that cushion the organization from the impact of complexity, either through slack resources or self-contained tasks, and those that seek to provide the organization with the information required to handle higher levels of complexity (Galbraith, 1974 and 1977).

A major contribution of our conceptual model is that it provides a more granular view of supply chain complexity and how an individual BU should respond. In particular, the model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. Such a situation is not only possible but likely in today’s business environment, and managers need more precise guidance than what can be currently found in the existing literature.

Consider the case of an assemble-to-order manufacturer that assembles final products using standard components. Given the chosen business strategy, the manufacturer will view responsive final assembly operations as a strategic requirement. The manufacturer may therefore decide to absorb its chosen level of customization through two-level master scheduling and online configuration software (IP enhancing mechanisms) and additional assembly capacity (slack resources). In contrast, the same manufacturer might block any customization proposed at the component level since this source of complexity is inconsistent with the firm’s chosen business strategy. In any case, the plan of action – to reduce or absorb complexity – can only come after a source of supply chain complexity has been identified as strategic or dysfunctional.

Take in Figure 2 here

4. Illustrative Case Study

To illustrate the relationships shown in our conceptual model and to gain insight into how supply chain complexity can be managed at the BU level, we consider the case of an established single-plant producer of packaged goods. The case study covers the period from 2008 to 2012, during which time the study firm adopted an expanded business strategy that dramatically increased supply chain complexity. The study firm subsequently applied a variety of techniques to simultaneously absorb the increased levels of strategic complexity whilst reducing the dysfunctional complexity it faced. The research methodology is explained below, followed by a detailed discussion of the actual series of events.

Research Methodology. This paper engages a longitudinal research approach that is based on a single embedded case study (Yin, 2014). Single case studies are often used in longitudinal studies because they support a greater depth of observation (Yin, 2014) with implications for management best practice and the development of theoretical insights (Ketokivi, 2006; Narasimhan and Jayaram, 1998; Karlsson and Ahlstrom, 1995). Barratt et al (2011, p. 331) suggest that singles case studies “enable the researcher to capture in much more detail the context within which the phenomena under study occur.” Yin (2014) highlights the benefits of utilizing single case studies for longitudinal research as they provide opportunities for unusual research access. The investigated case study provided such access through the availability of current and historical data and the length of service that interview participants had (an average of nearly 7 years).

The research team consisted of three investigators, enhancing the creative potential of the research as team members often have complementary insights which add to the richness of the data interpretation (Eisenhardt, 1989). Interviews were transcribed and coded by two researchers operating in isolation from each other to ensure reliability (Krippendorff, 2004, p. 217). Statistical records, combined with a low level of staff turnover at the study firm, provided an ideal environment for collecting both quantitative and qualitative data. Two investigators focused on qualitative data gathering and the other collected and analyzed the quantitative information. Combining quantitative with qualitative data can help in the avoidance of “elite bias” and “holistic fallacy” about a case (Miles and Huberman, 1994, p. 41). The two forms of data from the study firm supported the researchers in utilizing pattern matching to analyze and elaborate on the theory of supply chain complexity (Barratt et al., 2011).

Data collection was guided by the conceptual model of supply chain complexity (Figure 2). Through the longitudinal data collected, the research team looked for patterns involving a priori determined constructs (Barratt et al., 2011) and used these to provide “a comparison of a pattern of observed outcomes with some pattern of expected values derived from a given theory” (Bitektine, 2008, p. 162). The findings from the analysis were shared and reviewed with the interviewees to assess the validity of the results.

Initial Contact. One member of the research team met the study firm’s supply chain director at a seminar. The director subsequently made contact to discuss issues that his planning and control area was experiencing regarding unstable production plans. The director provided data to the research team on customer demand, Stock Keeping Units (SKUs) and inventory levels. Subsequently the research team presented findings to the study firm’s supply chain management team, highlighting the impact that increased numbers of SKUs and customer demand variability were having on inventory levels. An alternative approach to forecasting was suggested by the research team, which was subsequently developed and implemented by the case study firm. During this feedback session the research team observed that increases in downstream supply chain complexity were evident in the forms of growing demand variability and customer heterogeneity and requested access to observe what actions the study firm took in response to these sources of supply chain complexity. At this point in the relationship, terms relating to “supply chain complexity” were not explicitly used by study firm personnel; instead they mentioned phrases such as “more frequent changeovers” and “less stable” production plans.

Quantitative Data Collection. Longitudinal data was gathered from the study firm’s MRP system which provided information on monthly production volumes, demand variability, and sales and finished goods inventory levels for each SKU. Additional information on raw material and immediate product packaging (bags) inventories, from their principal external supplier which constituted 43% by value of non-raw material supplies, was also captured from the MRP system. Raw materials were sourced from government controlled markets which limited any potential improvements to operating the supply chain and were therefore excluded. The firm had used the same core suppliers for seven to eight years. Other items such as pallets, wrapping and labels were also excluded as these were part of the transportation firm’s responsibilities. The data collected was used to study the nature of demand, growth in volume and SKUs, and inventory fluctuations. Data on customer service levels, production budgeted performance, down-time and supplier performance were extracted from off-line spreadsheets. After investigating the operations of the study firm, data was collected on supplier-held inventories and lead-times. Information was collected for the period 2008-2012 to map the historical trends and also monitor changes implemented by the case study. Archival data provides an unbiased perspective of the plant performance (Flynn and Flynn, 1999).

Qualitative Data Collection. The objectives of collecting qualitative data through semi-structured interviews were threefold: 1) to develop an understanding of the strategic nature of the drivers of supply chain complexity faced by the study firm, 2) to identify the responses the case study firm and key packaging supplier took in response to these sources of complexity, and 3) to give the research team deeper insights into these responses as well as trends reflected in the quantitative data. Data was collected through semi-structured interviews to understand the consequences over a twelve-month period of product line expansion and short-term changes to production schedules for customers, logistics, manufacturing and suppliers. The initial interview protocol was aligned around existing BU-centric supply chain complexity theory and was guided by previous studies in the subject area (Bozarth et al., 2009; Barratt et al., 2011). The initial interview protocol is shown in Appendix A.

Conducting interviews which are based partially on respondents recalling historical events can lead to issues such as post hoc rationalization (Campbell, 1975). To counter this potential bias, interviews were conducted separately with more than one respondent who had been present during this period of time and had experienced the same events. These respondents were identified by the supply chain director. The recorded and transcribed interviews were triangulated for consistency. When differences were noted subsequent interviews, face-to-face or by telephone, were held to clarify the issue. The interviewees represented a group of individuals with two to 15 years of experience in the business. Summary information on respondents is shown in Table 1.

Take in Table 1 here

Business Situation Prior to 2008

Prior to 2008, manufacturing at the study firm consisted of taking six basic types of goods and packaging them into variously sized bags to be sold directly to restaurants. Since the customer base consisted of restaurant clientele, demand levels tended to be stable and predictable, with no single customer’s demand having a disproportionate impact on aggregated demand. The customer base was also homogeneous in terms of their requirements, with quality being a qualifier, and price and delivery being the main order winners (Hill, 1999).

Because of the homogeneous customer base and relatively limited breadth of the product line, the manufacturing process was organized as a standardized, make-to-stock system, with production runs carried out to maintain appropriate finished goods inventory levels based on forecasted demand and safety stock requirements. Demand was deterministic in nature providing a stable environment in which planning and control could operate. According to the operations director, there had been no need for formal sales and operations planning (S&OP) since demand levels were relatively flat and excess capacity could take up the slack.

On the upstream side of the supply chain, the firm had long-term contracts with a packaging suppliers based on forecasted demand levels. The packaging manufacturer in turn would produce packaging based on its most economical batch sizes (some of which represented as much as six months’ worth of demand) and hold finished bags in inventory until reordered by the study firm as part of a once-a-week replenishment cycle. In summary, the historical supply chain for the study firm could be characterized as consisting of relatively low levels of downstream, internal manufacturing and upstream complexity, with minimal lateral communication between the customer, marketing, production and key supplier. Demand throughout the supply chain was nearly deterministic in nature with limited inherent variability. These observations align with the existing literature describing low levels of supply chain complexity (Bozarth et al., 2009)

Increases in Supply Chain Complexity, circa 2008-2010

Downstream Impacts. Starting in 2008, the study firm adopted a business strategy that required it to expand its marketing channels to include food service companies and supermarkets. The new customers brought with them requirements that increased the levels of downstream supply chain complexity faced by the study firm. Manufacturers in the food sector have come under increasing pressure from supermarkets and food service companies to diminish the homogenization of their offerings, reduce lead-times, shorten product life-cycles and provide more frequent deliveries (Taylor and Fearne, 2006; Squire et al., 2009). These customer pressures were exemplified by the operations director: “We are growing our market share. The reason is that we are able to produce a good product, at a good price, but also we are constantly pushing new products and ideas. That is what supermarkets love.” The food service companies and supermarkets saw promotions as critical to unlocking new retail demand; however, the timing and scale of these promotions was not always shared within the study firm, resulting in unanticipated levels of demand variability at the plant.

The impact on supply chain complexity associated with the new business strategy can be seen in Table 2 and Figure 3 below. Table 2 shows that whilst the absolute number of customers decreased by 13%, the number of distinct SKUs serviced by the study firm actually increased by nearly 70% due primarily to customer-specific packaging requirements. This change is indicative of both increasing detail and dynamic complexity. Figure 3 shows a time series of customer orders for one particular product, taken after the study firm expanded the market to include food distributors and supermarkets. Note that the demand includes both traditional replenishment (make-to-stock) orders as well as promotional demands. In this example, the high levels of demand variability from one week to the next are indicative of increased dynamic complexity at the product level.

Take in Table 2 here

Take in Figure 3 here

A second major change was the demand for customized packaging solutions. Specifically, the food service companies and supermarkets demanded customized packaging with customer-specific barcode requirements, notation and pallet configurations as well as shelf-ready packaging. Furthermore, these requirements were subject to frequent changes according to the purchasing manager. The result was that any particular packaging design could have both a short development time and life cycle.

Internal Manufacturing Impacts. The increased levels of detail and dynamic complexity found in the study firm’s expanded markets proved to be incompatible with established manufacturing practices and procedures. In theory, whilst the manufacturer had plenty of capacity to meet average demand levels, with average utilization at 42%, shorter customer lead time requirements created spikes, making it more difficult to get the work done in the required time. In isolation the increases in SKUs would require production “to do more of the same” in terms of changeovers, planning and supply chain management. However, the combination of additional products and the stochastic nature of demand from new customers put the plant under pressure to change its way of working. Rather than develop a stable schedule and stick with it, plant management “hunted” for capacity. According to the supply chain director and planning control manager this reactive approach to customer orders resulted in packaging lines having excessive changeovers, lower value-added times and more downtime for cleaning.

Upstream Impacts. The impact of the increased levels of downstream and internal manufacturing complexity directly affected the study firm’s packaging supplier. Historically the study firm and its supplier signed long-term contracts with “call offs” to be delivered weekly. Under the new business environment, this hands-off approach was undermined as the frequent schedule changes experienced by the plant were transmitted to the supplier, leading to increases in costs, unplanned changeovers on print machines and inventory levels which did not reflect the call-off agreement. Customization of product in terms of labelling, pallet configurations and on shelf presentation required an improvement in new product development (NPD) cycle times for the packaging supplier. High stock levels at the supplier meant that changes to packaging could take 10 to 11 months to work through existing inventories or result in high costs of obsolescence.

Responding to Supply Chain Complexity, 2010-2012

Management recognized that their strategy of adding distributors and supermarkets was essential to the company’s long-term survival. Hence, the strategic complexity introduced by these new customers with customized product requirements, promotions and stochastic demands had to be absorbed. The study firm took actions to both absorb the added complexity whilst simultaneously attacking sources of dysfunctional complexity (long product changeover times, production scheduling instability, low volume SKUs with poor margins and supplier lead-time variations) that had been present beforehand but only became problematic when the system was stressed by the new market requirements. In this section we document these actions and consequences.

Absorbing Strategic Complexity. As part of its response to the increased levels of strategic supply chain complexity, plant management put in place several absorbing mechanisms (Boisot and Child, 1999; Galbraith, 1977). The first change was an IP enhancing mechanism designed to improve demand management. The continually changing environment that the case study firm operated in made the management of production using simple forecast-driven production scheduling difficult (Taylor and Fearne, 2006). When the production and planning control manager recognized that the increased complexity was part of the ongoing business strategy he realized that greater analytic skills were required to improve demand management and inventory replenishment modelling. This led to the development of categorization of customer demand and order variability into three distinct groupings: Make-to-stock (MTS), Make-to-order (MTO) and Promotional orders.

Separating out MTS orders from outlier promotional orders gave management a better picture of the underlying demands. Consider Figure 4a, which reproduces Figure 3. The two red dashed lines in Figure 4a represent demand spikes (outliers) caused by promotional orders for Product 20ABC. Removing the promotional demand from the product’s demand profile leads to the MTS demand profile in Figure 4b. In fact, the coefficient of variation (CoV) for the combined demand in Figure 4a is 1.10, whilst the CoV for the cleansed MTS demand profile in Figure 4b is 0.79, a reduction of 28.2%.

Take in Figure 4 here

Understanding the business requirement for promotions, the planning and control manager opted for an IP approach to absorb the increased complexity as the traditional inventory buffer and capacity alternatives could no longer support the changing customer demands. As the production planning and control manager stated, “Getting a bit more knowledge about the product and an understanding of its real demands, rather than just relying on average sales, really allowed us to reduce stocks and costs.” Table 3 shows the CoV impact of splitting out the promotional orders and the subsequent savings in inventory for eight representative products. The table shows the annual tonnage per product category and the inventory reduction savings generated over the first six-months of operations, from June-November 2012, after demand was split into MTS and promotional orders.

Take in Table 3 here

Following intensive training in forecast modelling, production planners developed production schedules based on generic algorithms to manage the changing demand profiles (Garn and Aitken, 2015). Smoothing the peaks and troughs of demand through the application of new techniques that accommodated product growth and changing manufacturing time slots improved the stability of the short and longer-term schedules for production and suppliers. Consistent with Galbraith’s arguments (1974 and 1977), the enhanced use of information systems and well-trained people has been found to be significant moderators of supply chain complexity (Manju and Sahin, 2015). When the case study management recognized that the increased strategic complexity they faced had to be absorbed and concluded that the historical approach to planning and control was no longer sustainable, they opted for an IP approach. The time not consumed by constant planning change discussions was used to implement a second IP absorbing mechanism: enhanced lateral relations through the introduction of Sales and Operations Planning. S&OP provided a cross-functional decision platform (Grimson and Pyke, 2007; Serdarasan, 2012) that could intervene and moderate the impact of increasing strategic complexity.

A third example of an absorbing mechanism was to refocus the production lines based on packaging size, effectively creating self-contained tasks (Galbraith, 1974 and 1977) that helped absorb the impact of increased downstream complexity. Focusing production has been shown to improve manufacturing productivity, and is a key tenant of the theory of swift, even flow (Schmenner, 2012). Following analysis of demand and capacity across product categories, self-contained production lines could moderate the impact of increased SKUs by absorbing some aspects of changeovers into the manufacturing configuration.

Finally, packaging proliferation driven by the downstream customers forced the study firm to restructure its relationship with its main packaging supplier. Increasing SKUs and increased demand variability were causing stress to both parties. After considering his options, the purchasing manager selected an IP enhancing mechanism to absorb the increased strategic complexity. Both parties agreed to replace the previous call-off approach with a vendor-managed inventory agreement that provided the supplier with direct visibility into plant inventory levels through the transmission of daily inventory figures. The enhanced lateral information exchanges, coupled with twice weekly replenishments and print pooling, led to packaging inventory falls of over 40% over an eight-month period from May-December 2012 as well as reductions in new product development times.

Reducing Dysfunctional Complexity. The study firm also worked to reduce drivers of dysfunctional complexity. Short-notification of changes increased the number and frequency of changeovers leading to capacity losses and excessive changeover times. Through lean manufacturing training on SMED (single minute exchange of dies) the firm reduced changeover times by over 60% (Bicheno, 2008).

Reducing the dysfunctional complexity caused by poorly managed changeovers was a positive step forward. However, the firm was also experiencing problems driven by its legacy production scheduling system. In the past customers had placed orders against an agreed lead-time of seven days for standard restaurant products or against a schedule for export-specific items. This legacy system, supported by finished goods inventory levels of 14 to 21 days, had allowed the development of manufacturing schedules with a two-week fixed horizon. The futility of trying to establish a two-week plan was captured at the time in the remarks of the production supervisor, who stated, “Every week we sit down with the planners and agree to the plan for the next 10 days. Before I get back down stairs it has changed. It’s a waste of time.”

When it was recognized that this approach was no longer feasible the organization determined to change. An analysis of manufacturing processing times and inventory profiles in February 2012 suggested that a reduced three-day fixed window for production, planning and logistics would be practical. Rather than operating to a longer, unstable plan it was agreed that a three-day rolling plan supported by quick-changeover resources would provide enough time to prepare the raw material, clean the equipment, reconfigure labor levels, process and pack the product in time for shipment. Other options including daily schedules (Serdarasan, 2012) were considered, but arranging labor on such short notice was not feasible. Operating with a shorter – but stable – window reduced the perceived level of dysfunctional complexity faced by the team in terms of delivering the agreed schedule.

The increasing volatility in demand, with an average SKU CoV exceeding 1.8, led to the development of greater flexibility in planning and production scheduling. Through enriched analytical skills and greater exploitation of software an IP enhanced absorption mechanism was developed. This improvement supported the introduction of a stable but flexible production schedule. Reducing the horizon of the plans frozen period diminished dysfunctional unplanned changeovers while providing the flexibility of aligning production capacity to changes in demand within a shorter window. Absorbing the impact of increased demand volatility on production performance had altered a buffering approach based on inventory and spare capacity to one that was achieved through IP enhancing mechanisms. Aligning production control and planning activities with the context of increasing strategic complexity forced a shift in the type of absorbing mechanism deployed.

A similar result was seen at the packaging supplier. Through mapping the print processes a detailed analysis of downtime led the supplier to conclude that the largest causes of lost time were color and schedule changes. Improved communications with the study firm addressed the latter whilst matching print runs by color matching print runs reduced the down time in setting up its print machine for the supplier. The result was smaller batch sizes, reduced inventory in the supply chain, and shorter response times for new packing changes. In the end, simultaneously reducing dysfunctional complexity, while enhancing lateral relations through improved communications, benefited both firms.

Reduction of dysfunctional complexity related to the growth in SKUs was another area that was addressed. When products were recognized as having a low volume and providing little or no contribution, they were removed from the product range, mirroring Gottfredson and Aspinall’s (2005) suggestion that firms stop adding to their range when new products or features have marginal costs which exceed marginal revenues.

5. Discussion

Table 4 summarizes the major drivers of supply chain complexity faced by the case study firm, what mechanisms were used to address them, and the resulting impacts on operations performance.

Take in Table 4 here

The experiences of the case study firm confirm the importance of BUs taking into account not just the type (detail or dynamic) and origin (upstream, downstream, or internal) of supply chain complexity drivers facing the organization, but also their strategic nature. Managers at the case study firm correctly decided to absorb some complexity drivers whilst simultaneously working to reduce others. Furthermore, whilst one might argue that some complexity drivers might fall exclusively into either the dysfunctional or strategic category regardless of the business situation, this is not true for others, such as variable demand levels or higher levels of customization. Managers must explicitly understand the difference before implementing a mechanism aimed at reducing or absorbing a particular source of supply chain complexity.

Complexity Dormancy and Synergies across Absorption and Reduction Efforts. One important insight gained via the case study is that supply chain complexity drivers, or at least their impacts, can lay dormant until they are exposed by changing business conditions, much like the “iceberg of problems” being hidden by a sea of inventory (Bicheno, 2008). Furthermore, the implementation of various absorption or reduction mechanisms can be mutually supportive in ways that are not easily predictable. For example, the case study firm’s efforts to split demand forecasts into MTO and MTS components (absorbing strategic promotional order demand) supported the introduction of a fixed three-day production schedule for MTS orders (reducing dysfunctional complexity). As another example, the application of the SMED approach eliminated a significant aspect of lost manufacturing time, thereby supporting production lines focused on specific package sizes (self-contained tasks). The combined effect of lower and fewer setups meant that the dedicated lines did not exceed 75% of available capacity, on average. As a result, production was able to increase monthly output 20.2%, from 51 tons per month to 61.3 tons.

Implications for Theory and Practice

From a practitioner perspective, our research reinforces the idea that even seemingly isolated efforts within the operations and supply chain area to address supply chain complexity must take into account the business or marketing strategy of the firm. This is consistent with long-standing research in the operations field (Hill, 1999; Hayes, et al., 2005). Nevertheless, when it comes to how a BU should respond to strategic supply chain complexity, we foresee two trends practitioners should be aware of. The first is a shift over time toward greater use of IP enhancing mechanisms over buffering mechanisms. When Galbraith’s (1974, 1977) information processing view of organization design was first formulated in the 1970s, information systems consisted primarily of mainframe computers running in batch mode, and inter-firm relationships tended to be either adversarial or hands-off. The relative weakness of these IP enhancing mechanisms at the time made buffering mechanisms, such as excess capacity or inventory, seem more viable (Figure 2). But much has changed in the decades since, with information systems becoming exponentially more powerful and cost effective, and the ability to manage relationships with supply chain partners (i.e., lateral relationships) becoming an important and recognized part of a firm’s capabilities. As such, we see firms increasing their use of IP enhancing mechanisms whilst simultaneously reducing their use of buffering mechanisms (Figure 2).

This leads to a second anticipated trend: More complex BU strategies. This is consistent with Roehirich and Lewis’ (2014, p. 223) observation that “organizations should not try to reduce complexity, but rather respond via more complex strategies.” Certainly, such efforts across an industry will serve to push out the productivity frontier (Porter, 1996), thereby providing customers with more value at the same or lower cost. But businesses that currently use buffering mechanisms to support strategic supply chain complexity will need to proactively reduce their reliance on these mechanisms, or see themselves undercut by rival firms taking advantage of advances in information technologies and relationship management practices.

From a research perspective, the case study results presented in this paper provide some empirical support for our conceptual model. Instead of blindly reducing sources of demand variability or alternatively adding expensive capacity or IT systems to absorb all sources of complexity, the study firm followed a nuanced approach that combined both absorption and reduction approaches. The result was significant operational and market-based improvements with minimum investment which realigned the firms’ supply chain to address the increasing complexity it was facing.

Nevertheless, we acknowledge that the findings derived from a single, in-depth, case study require further research to produce generalizable results. In addition to testing the model across a larger sample size, more research is needed to understand how efforts to address supply chain complexity can play out over time. Two specific questions come to mind:

1. The case study revealed that there may be reinforcing synergies between various complexity absorption and reduction mechanisms. If so, are these synergies unique to a particular firm’s situation, or are there common “patterns of implementation” that might be broadly applicable across firms or even certain industries? For example, combining setup time reduction efforts (reducing dysfunctional complexity) with focused product lines (an absorbing mechanism) seems an obvious pairing, but are there others? Having established a more refined view of supply chain complexity in this paper, we could see a deeper dive into the existing literature as a first step toward answering this question.

2. Similarly, whilst we argue that firms can simultaneously address downstream, internal and upstream supply chain drivers, changes in business strategy typically manifest themselves first via changes in the products and customers served by an organization (Hill, 1999). These downstream changes then reverberate through the organization and upstream to the supply base. If this is true, is there a possible benefit to addressing downstream complexity drivers first and then assessing the impacts on internal and upstream complexity prior to taking additional action? What firm and environmental factors might determine whether or when such a “wait and see” approach is more appropriate?

3. Finally, in our discussion of implications for practitioners, we suggested that over time, firms seeking to address strategic supply chain complexity should anticipate greater use of IP enhancing mechanisms over buffering mechanisms as the former continue to become more sophisticated and cost effective. Whether or not the empirical evidence supports our contention -- and if true, how this anticipated trend plays out across different environmental settings – remains a question for future research.

6. Conclusions

In this paper, we derived a conceptual model of supply chain complexity that provides a more granular view of supply chain complexity and how an individual BU should respond. We then illustrated the model using longitudinal data from a case goods manufacturer. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. Such a situation is not only possible but likely in today’s business environment, and managers need more precise guidance than what can be currently found in the existing literature. It is our expectation that decision makers will be able to use the conceptual model introduced here to better structure the firm-level debate about when and why to react to increased levels of supply chain complexity.

In his article “What is Strategy?” Michael Porter (1996) noted that whilst operational effectiveness (i.e., “doing things right”) is important, it is not the same as “doing the right things.” Operational tactics and tools must be carefully chosen and applied based on the business strategy. This is particularly true for many firms today that are facing increased levels of supply chain complexity. Yet works in this area have tended to focus either on the overall behavior of complex adaptive systems consisting of multiple organizations loosely linked together via behaviors guided by shared schema (Holland, 1995; Choi et al., 2001: Choi and Krause, 2006) or on identifying specific tools and techniques, but without a clear discussion about what supply chain complexity drivers they target, and when they should be employed (Serdarasan, 2012; Perona and Miragliotta, 2004). In her review of supply chain complexity drivers, Serdarasan (2012) identifies 33 different “solution strategies” for dealing with supply chain complexity, ranging from process automation to forming partnerships with key suppliers. Such research is certainly important, but we contend it is even more important for managers to first understand the strategic nature of the supply chain complexity faced by the firm, whether it represents detail or dynamic complexity and where in the supply chain it originates therefore, supporting managers in deciding when to intervene and why certain tactics are appropriate.

• Appendix A – Interview protocol

General Questions

• What is your role in the firm and what area of responsibilities does in cover?

• Which departments do you interface with?

• Do you deal with suppliers and/or customers? If so how often and on what subjects?

Dysfunctional and strategic complexity

• Have you noticed a change in the number of SKU’s?

• What effect has the change in customer base had on your area of operation?

• Has the planning and controls used to support the business altered?

• What effect have the SKU and customer changes had on your schedule stability?

• What effect have the SKU and customer changes had on your suppliers?

• What impact have the changes had on the reliability of the supply chain in terms of customer service?

Response to dysfunctional and strategic complexity

• What has your firm changed in response to these SKU and customer modifications?

• Has production capacity changed to address the new product and customer needs?

• Have roles and responsibilities altered as a result of SKU and customer changes?

• What process and relationship changes have occurred with suppliers?

• What changes would you judge as positive or negative for the firm and why?

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