Operations in today’s demand chain management framework

Abstract

Demand chain management (DCM) can be defined as “extending the view of operations from a single business unit or a company to the whole chain. Essentially, DCM is a set of practices aimed at managing and co-ordinating the whole demand chain, starting from the end customer and working backward to raw material suppliers. There are two fundamental objectives: (1) to develop synergy along the whole demand chain, and (2) to start with specific customer segments and meet their needs rather than focus on internal optimisation” (Vollmann et al., 2000). The focus is clearly customer-centric, as defined early on by Brace (1989), in explaining the concept of a demand chain as “… the whole manufacturing and distribution process may be seen as a sequence of events with but one end in view: it exists to serve the ultimate consumer”. The concepts of demand chain and DCM are relatively new ‘buzz’ words, and in essence they are based on supply chain models (Langabeer, 2001). It is important to mention here that the rapid uptake of technology, and in particular the Internet, has resulted in the continual evolution of the demand chain concept with a shift away from supply chains towards demand chains and DCM. The main stimulus behind this has been the shift in power away from the supplier towards the customer (Soliman and Youssef, 2001). A common focus of most demand chain tools is combining supply chain information with analysis of customer interactions, transactions, and demands for goods/services to make more accurate sales and market demand forecasts (Zellen, 2001). However, one of the most challenging tasks facing organisations is how to integrate e-business capabilities into their demand chain processes to give customers a number of services to make their transaction more efficient and fulfilled. Although supply and demand chain processes have different focuses, and therefore, different drivers, there are a number of drivers that are relevant to both processes such as economic changes, progress in management theory, technological advancements, and technology and communication standards. The demand chain focuses on the product from the point of view of the customer—what the customer wants and needs (Joy, 2001). To excel in the demand chain, it is essential that the information is accurately captured through the supply chain (Costello, 2001). The demand chain uses technology to focus on the consumers’ actual demand behaviours. As such, the supply chain focuses on efficiency, whilst the demand chain looks at effectiveness and revenue generation, cost effectiveness, and planning capability (Langabeer and Rose, 2001). In traditional businesses, the supply chain involves storing, shipping and selling the product, and can only ever be as efficient as the sales forecast (McCarthy, 2001). As the Internet is enabling the consumer and providing them with more knowledge, the focus is shifting towards a more “customer-centred supply chain” (Kuglin, 1998). The Internet has shifted the balance of power from the supplier to the consumer—effectively creating the demand chain (McCarthy, 2001). The demand chain as a result is the most significant business model to emerge over the last 20 years (Langabeer and Rose, 2001). The customer now manages the performance of the total supply chain (Kuglin, 1998), through their satisfaction. In other words, the demand chain is about the informed customer, customers dictating what they want, where and why. Whilst a relatively simple concept, the processes to achieve it are a lot more complex (Doherty, 2001), due to the number and size of the consumer markets, sales channels, and the quantity of information that is available. Companies are trying to close the loop between the supply and demand chains, by using their real-time consumer knowledge, collaborating with the trading partners, and investing in web-enabled technology (Doherty, 2001). A demand chain is a complex concept that involves businesses being supplied by, and supplying, a multitude of other businesses, with linkages between them being supported through transportation, warehousing, logistics, manufacturing planning and control systems, and information management. DCM can also be extended to the service sector (Anderson and Morrice, 2000), such as the real estate industry (Selen, 2001). In DCM, the bundles of goods and services being provided are customised for individual customer segments and/or customer partners. In a recent study on operations strategy, the requirement is stated for “business executives to reformulate strategies and reconfigure their organisations, to alter the approach companies relate with customers and link with other entities” (Ghosh, 2001). The contributions to this special issue present methodologies for addressing today’s DCM challenges, using a number of illustrative case studies. In addition, they provide directions for future research in DCM. A brief summary, and positioning of these papers, is outlined in the following section. In “analysis and design of focused demand chains”, Childerhouse et al. state that the theory of focused demand chains is based on the premise that modern day marketplaces have diverse requirements for alternative products and services. No one demand chain strategy can best service all these requirements. Hence, focus is required to ensure demand chains are engineered to match customer requirements. Such focus is enabled via segmentation on the basis of each product’s characteristics. The authors refer to a progression from focused manufacturing (Skinner, 1974), to focused logistics (Fuller et al., 1993), to focused demand chains. A critical part of the integrated framework categorises the types of demand chains. Using the DWV3 classification variables proposed by Christopher and Towill (2000), the products are categorised into clusters with similar characteristics. These DWV3 variables are described as: Duration of life cycle; time Window for delivery; Volume; Variety; and Variability (DWV3). Duration and stage of product life cycle have been noted by many authors as key characteristics that require demand chains to recognise and thereby adopt tailored strategies. Fisher (1997) uses the Duration of product life cycle as a classification variable to differentiate between functional products with long life cycles (>2 years) and innovative products with short life cycles (3–12 months). The time Window for delivery or delivery lead time, reflects the responsiveness requirements placed on the demand chain. This is, therefore, a very important variable in classifying demand chains. Volume is the third of the DWV3 variables. Fuller et al. (1993) once more use this variable in their classification of logistically distinct businesses. They go on to emphasise the attention that should be placed on key products that are both high Volume and high margin because of their critical importance to an organisation. Product Variety is constantly increasing at the marketplace as demand chains attempt to compete on the basis of added value in relation to colour, form and function. There is an overall move towards individualisation and away from aggregation as customisation increases. The final classification variable of the five is demand Variability and is the most significant in the opinion of the authors. A detailed historical account of the change programme in a lighting company used as a test bed herein, was written by Aitken (2000). The resultant four focused strategies greatly enhanced the competitiveness of the company and their demand chain partners. The DWV3 classification variables are generic, and it is the contention of the authors that these hold for many industries and marketplaces. The advantages of focus have been long understood in the context of manufacturing management and can be traced back to the fundamental theoretical work of Skinner (1974). Fuller et al. (1993) extended the approach to logistics, whilst Fisher’s (1997) argument encompassed the whole demand chain. However, regrettably few examples exist in the literature of how to achieve this desired focus in practice. This paper bridges this gap by presenting a structured methodological framework for implementing focused demand chains across organisations. In “demand chain management theory: constraints and development from global aerospace supply webs”, Williams et al. develop an empirical and theoretical approach to where strategic capabilities should lie within global aerospace supply chains. Supply chains, traditionally dominated by original equipment manufacturers (OEMs), are being replaced with radical new organisational structures, as electronic commerce promotes opportunities for improving inter-organisational co-ordination. With higher levels of inter-organisational communication, the efficiency of organisations composed of traditional multi-functional areas is challenged. OEMs can increasingly focus on their core capabilities and devolve their low value intra-organisational operations to sub-contractors. Consequently, new organisational systems, structures and processes are emerging to transform accepted models of business operations and strategy. The authors apply their approach to the aerospace industry, where these changes are particularly relevant. Up to 70% of the final value of a typical aerospace platform is out-sourced, and the prime contractor traditionally played the lead role handling most of the risk associated with innovation, development funding and production. Recent increases in out-sourcing and supply chain rationalisation demonstrate that there may be new drivers that encourage prime contractors to find more efficient ways of managing capabilities and transactions within demand chains. What is not clear in this dynamic aerospace manufacturing environment is where strategic capabilities should be owned within production and operator supply chains. Subsequent questions are concerned with how to identify strategic capabilities and how to co-ordinate capabilities between large numbers of organisations. The theoretical research question of this paper considers how transaction cost economics (TCE) and the resource-based view (RBV) of the firm contribute to explaining where strategic capabilities should be owned within production and operator supply chains. The authors subsequently apply TCE to aerospace supply chains, as the total stock of capabilities and their quality within a demand chain will be important relative to competitor demand chains. Resource-based theory of the firm, on the other hand, can be used to provide some insight into capability identification. Within individual demand chains there must be the right mix and qualities of capabilities relative to competitor chains. The construction of such systems may enable the delivery of the right product, at the right time, at lower cost. TCE helps explain where the development of a demand chain may encounter constraints. The authors use a number of qualitative supply chain case studies to address “where strategic capabilities should be owned within production and operator supply chains”, resulting in the identification of capability-focused governance strategies that are co-ordinated between companies at high and low tiers of the supply chain, and their drivers and capabilities. What is required is a strategic planning approach that helps practitioners choose and prioritise the drivers, and subsequently identify the most appropriate capabilities in response to those drivers. As such, each company must plot its own path in recognition of its unique position in the supply chain. It has long been recognised that fully cross-functional integrative empirical research is required to help support the understanding of the applicability of OM practices within industry. In “demand chain management: an integrative approach in automotive retailing”, Hines et al. postulate that academics, consultants and practitioners have been searching for the ‘holy grail’ theory, method, or solution that will cure all of their supply chain ills. Theories or approaches have come from systems dynamics, time compression, lean thinking, business process re-engineering, agility, mass customisation and the virtual organisation (respectively: Forrester, 1961; Stalk and Hout, 1990; Womack and Jones, 1996; Hammer, 1990; Kidd, 1994; The Economist, 2001; Davidow and Malone, 1992). In many previous approaches, researchers have attempted to develop an appropriate solution to the improvement of the real case supply chain, based on a specific methodological approach, which often leads to predictable solutions. There appears to be a significant danger that solutions are not being tailored to particular supply chain requirements, but more to the prescriptive solutions of particular approaches. Thus, the key determinant of the solution may be the method chosen, and not always the actual supply chain dynamics. What is called for is an integrative approach that seeks to gain a more holistic and contingent decision-making approach. This paper explores such an integration of approaches developed within the process-based lean thinking, strategic cost management, marketing, and policy deployment areas. In order to investigate the approach, a single automotive retailer is used as an instrumental case, deploying the action research driven case study method (Yin, 1989). It is the authors’ contention that an integrated or holistic process-based approach is the most effective way to drive companies towards a competitive advantage. In “demand chain management in manufacturing and services: web-based integration, drivers, and performance”, Frohlich and Westbrook investigate the relationship between Internet-enabled supply chain integration strategies and performance in manufacturing and services. Together, greater online co-ordination with associated reduced lead-times helps defeat the bullwhip effect and contributes to higher performance (Lee et al., 1997). The authors’ investigation was especially motivated by the possibility that manufacturing and services are sufficiently different enough that it affects the need for DCM. They summarise the literature on demand and supply integration, and subsequently describe four web-based strategies, captured in four models (A–D). At one extreme is a strategy of little or no web-based integration (model A). At the other end of the continuum is a strategy with high levels of web-based integration, co-ordinating the whole demand chain from customers backwards to suppliers, popularly called DCM (model D). In between these polar extremes are companies whose strategies involve web-based integration with either their suppliers (model B) or customers (model C). The authors then postulate their first set of hypotheses. H1a 1.Manufacturers and services adopting a web-based DCM integration strategy (model D) will have the highest levels of operational performance. H1b 1.Manufacturers and services adopting either a web-based supply (model B) or demand (model C) integration strategy will have medium levels of operational performance. H1c 1.Manufacturers and services adopting a low web-based integration strategy (model A) will have the lowest levels of operational performance. greater access to new markets; the anticipated internal performance improvement; the so-called “bandwagon” effect. This results in a second set of hypotheses to be tested. H2a 1.Access to new markets drives the adoption of web-based demand chain integration. H2b 1.Anticipated benefits drive the adoption of web-based demand chain integration. H2c 1.External pressure drives the adoption of web-based demand chain integration. A stratified random sample was collected from UK manufacturers and services, and there was strong evidence that DCM led to the highest performance in manufacturing, but few signs of DCM in services. Manufacturers and services relying on only web-based demand or supply integration outperformed their low integration counterparts, but lagged DCM in manufacturing. The paper also extends our knowledge about the implementation of supply chain improvements and adds evidence to this emerging stream of literature on the adoption drivers behind the use of demand-driven chains. In particular, DCM adoption drivers such as rational-efficiency and bandwagon effects seemed to drive change. The findings have some important implications for theory, as well as for manufacturing and service companies interested in improving their performance. Companies in the fast growing industries need to be constantly developing their supply chain efficiency. At the same time, they are all the time facing a variety of new customers, with new situations and needs. Understanding the customer’s situation and need, together with the right offering, contributes to good co-operation in improving the joint demand chain, which further leads to superior demand chain efficiency and high customer satisfaction. However, going too far in customisation would ruin efficiency. On the other hand, too rigid an approach to supply chain management would risk customer satisfaction. In “from supply to demand chain management: efficiency and customer satisfaction”, Heikkila addresses an important question in DCM: “how to find a good balance between good customer satisfaction and supply chain efficiency?” An inductive case study of six customer cases of Nokia Networks, one of the leading providers of mobile telecommunication technology, leads to postulate propositions exploring that question. The aim was through case study research to find new perspectives for the demand chain structure and for the industrial customer–supplier relationships, and how they influence the demand chain performance in a young, fast-growing industry. The main research question was further divided into questions of information and material flows, customer–supplier relationships, and demand chain performance. The unit of analysis was a demand chain for building cellular networks, consisting of a customer (telecommunications operator), the technology supplier’s organisational units responsible for serving the customer in network building, and the factory assembling and delivering base stations to the customer’s network. Theory building from inductive case research was chosen as an appropriate research approach for this study. The research is directed toward development of testable hypotheses that are generalisable in various application environments. This research approach is a suitable method to describe and explore new phenomena (Handfield and Melnyk, 1998; Eisenhardt, 1989), or to build new operations management theories (Meredith, 1998). This type of theory building relies on direct observations of the objects or participants in the theory and its development (Glaser and Strauss, 1967; Yin, 1989). The research approach is inductive, and utilises both qualitative and quantitative data. The case study allows the investigation to retain the holistic and meaningful characteristic of complex real life events (Yin, 1989). information and material flows together forming the structure in a demand chain, the relationship between an industrial customer and the supplier, and the performance of a demand chain. The paper subsequently proposes a model for DCM. The case research of the six customer relationships in cellular network building indicates that there are a variety of customer relationships that the supplier needs to adapt to. Therefore, the crucial question for a supplier is how to design the demand chain architecture according to the needs and characteristics of distinct customer needs and situations. This leads to managerial implications for designing alternative modular demand chain processes. The paper proposes a priority order of six decision-making criteria to answer this question. In “robust planning: a new paradigm for demand chain planning”, a new paradigm for tactical demand chain planning, called robust planning, is put forward by Van Landeghem and Vanmaele. Robust planning aims at recognising and exploring the uncertainty that is inherent in demand chains, and distilling from it planning decisions that will yield more predictable and stable results. In particular, the bullwhip effect (Forrester, 1961; Metters, 1997; Wilding, 1998; Chen et al., 1998), or demand amplification that arises as a result of small disturbances in demand at the consumer level, amplifies as companies start to co-operate more closely (Van der Vorst et al., 1998; Holmström, 1997). Furthermore, corrective actions, such as decreasing stock levels, reducing lead times, etc. may have conflicting results on overall performance (Wilding, 1998). One of the root causes of the bullwhip effect is the use of inadequate forecasting methods, which do not correctly quantify the degree of uncertainty in the market demand (Chen et al., 1998). Planning of supply chains occurs at three hierarchical levels (Shapiro, 1998): strategic, tactical, and operational. Up to now, sophisticated MILP optimisation models, based on aggregate data (both in time and in detail), are primarily used on a strategic level. At the operational level, predominantly heuristic algorithms are used. Both modelling approaches must agree in basic assumptions and optimisation methodology. If not, one risks setting up a supply chain (network) based on an optimal strategy, only to find tactical implementation unable to reach the targets (of inventory and customer service levels), regardless of how optimal the operational level is being scheduled. The authors claim that in practice, the inherently uncertain market demand is either avoided; by requiring the user to specify target levels for each stock point (including sufficient safety stock to cope with uncertainty); or it is addressed in a myopic way, by scheduling each and every customer order in great detail. Whenever market conditions deviate from the design conditions of the supply chain, long lead times and deteriorating due date adherence will occur. It is therefore clear that the current approach of “scheduling of incoming orders, in ever greater detail, with increasing speed and over multiple nodes in the supply chain”, cannot adequately yield robust plans. The authors propose instead a robust planning-approach, which involves Monte Carlo simulation as an inherent part of the tactical planning calculation. This will allow more accurate assessment of uncertainty, enabling decision-makers to determine the logistic set-points (e.g. target stock levels and their location) in such a way that unforeseen conditions will less likely invalidate the base plan, or miss the target performance factors. In this way, one can hope to achieve more effective demand chains, with less nervous re-planning. Alternatively, one can achieve the same target performances with less safety stock. Finally, as close collaboration through electronic exchange of (planning) information spreads within DCM, the robust planning method may reduce the number of re-planning messages, making intensive collaboration schemes practical. The authors report results obtained with robust planning, as applied in a global chemical concern for the past 3 years. Willem Selen, Macquarie Graduate School of Management, Australia. Fawzy Soliman, University of Technology at Sydney, Australia. Henk Akkermans, Eindhoven University of Technology, The Netherlands. Jalal Ashayeri, Tilburg University, The Netherlands. Harry Boer, Aalborg University, Denmark. John Christiansen, Copenhagen Business School, Denmark. Florent Frederix, European Commission. Robert Mellor, University of Western Sydney, Australia. Jan Mouritsen, Copenhagen Business School, Denmark. Jan Olhager, Linkoping Institute of Technology, Sweden. Jaume Ribera, IESE, Spain. Ian Sadler, Victoria University, Australia. Carlo Smith, University of San Diego, USA. Linda Sprague, China Europe International Business School (CEIBS), China. Ronald Tuninga, Nyenrode University, The Netherlands. Jack van der Veen, Nyenrode University, The Netherlands. Roland Van Dierdonck, Vlerick Leuven Gent Management School, Belgium. Rik Van Landeghem, Ghent University, Belgium. Gyula Vastag, Indiana University, USA. Hans Voordijk, University of Twente, The Netherlands. Mohamed Youssef, Norfolk State University, USA.