价值流分类-英文

Value stream classification Martin Christopher Cranfield School of Management, Cranfield University, Cranfield, UK Denis R. Towill Cardiff Business School, Cardiff University, Cardiff, UK James Aitken Cranfield University, Cranfield, UK, and Paul Childerhouse Waikato Management School, The University of Waikato, Hamilton, New Zealand Abstract Purpose – In the twenty-first century business scenario, most organisations supply a range of products to multiple markets, so participate in several often quite different supply chains. Just as the linear chain is a simplification of a supply network, the single channel is a simplification of the true complexity many organisations face. When all products are pushed down a single channel, they are paced by the slowest and customers are charged an average price resulting in many being underserved. Manifestly, for most businesses “one size fits all” is not a viable option in delivery pipeline design and operation. This paper aims to address these issues. Design/methodology/approach – Traditionally, the requisite number of delivery pipelines operated by a business is determined by “hunch”, as is the range of products flowing down each channel to the marketplace. The information technology (IT) revolution, which in turn has spawned the “analytic corporation” enables pipeline selection and product matching to be placed on a more formal footing. In order to enable the tailoring of value stream pipelines to markets five classification variables are proposed. These are duration of product life cycle (D), delivery window (W), annual volume (or value) (V1), product variety (V2) and demand variability (V3). Findings – Through the use of case studies drawn from real-world situations, the authors are able to highlight the practical value of using appropriate taxonomies to identify appropriate supply chain design strategies. A framework for the implementation of a scheme for value stream classification is proposed. Practical implications – The proposed analysis, design, and implementation methodology is summarised in flow diagram format. Emphasis is placed on the formation of a “natural group” task force to execute this programme. Production, sales, logistics, and marketing are all essential “players” therein. Originality/value – It is established that the DWV 3 classification system is suitable for framing pipeline performance improvement programmes. By exploiting the “analytic corporation” IT capability, the system has a dynamic role in determination of channel switching as products progress through their individual life cycle phases. Keywords Supply chain management, Classification schemes, Delivery, Lean production, Agile production Paper type Research paper 1. Introduction Shewchuck (1998) summarised the drawbacks resulting from rigid adoption of the “one size fits all” philosophy in attempting to satisfy customer demands in the present day marketplace. The substance of his review shown in Table I is a useful starting point in The current issue and full text archive of this journal is available at www.emeraldinsight.com/1741-038X.htm JMTM 20,4 460 Received November 2007 Revised August 2008 Accepted October 2008 Journal of Manufacturing Technology Management Vol. 20 No. 4, 2009 pp. 460-474 q Emerald Group Publishing Limited 1741-038X DOI 10.1108/17410380910953720 understanding the complexities facing suppliers in an era of short life cycles, high variety, and high-initial demand (especially of electronic “fashion” accessories). Shewchuck (1998) thereby identifies four distinct manufacturing environments. Also highlighted are the potential tensions between marketing and manufacturing. As we have indicated previously, it is essential to properly exploit both the “lean” and “agile” delivery paradigms to arrive at a satisfactory total solution (Christopher and Towill, 2000). This includes exploitation of both information and material flow de-coupling points (Mason-Jones and Towill, 1997), and the leagility concept (Naylor et al., 1999). We suggest these are important facets in the move to implement market-driven supply chains, such as outlined by Sharifi et al. (2006). The delivery pipeline design procedure adopted should be transparent to analysts, designers, managers and users alike. Using too many classification variables (taxas) means the analysis may become very messy and thereby lose valuable insight. However, using too few taxa may not generate enough feasible solutions to eventually arrive at a “best” design. The taxa need gradation on an agreed simple scale (preferably binary). Finally, the pipelines output by the taxonomy should be capable of relating to established delivery processes so that their actual engineering exploits “best practice”. Finally, the total pipeline system must cater for real-time switching of products between value streams according to pre-set triggering rules. In other words, as noted by Fassoula (2006), adaptive operation of supply chains is highly desirable. Posited manufacturing environment Typical products Associated marketing characteristics Associated manufacturing characteristics Compressed life cycle Home computers Audio systems Cameras Medium product variety High demand during maturity phase Short product life cycle High volumes require mass-production for short periods Danger of product obsolescence Compressed time-to-market Mobile phones Palmtops Fashion goods Medium product variety High demand during maturity phase Short product life cycle and very short growth phase Competition so fierce that mass-production needed during start-up Danger of failure to penetrate market Mass customisation via assembly Personal computers Made-to-order bicycles Three-day car High-product variety Single unit product demand Relatively short lead time Deep bill of material (BOM) Product variety enabled via limitless combination of components Mass customisation via processing Spectacle lens Made-to-order shoes Made-to-order clothes High-product variety Single unit product demand Very short lead time Shallow BOM Product variety enabled via on-the-spot processing Source: Authors summarising Shewchuck (1998) Table I. Why “one size does not fit all” in twenty-first century delivery pipelines Value stream classification 461 2. Contribution of present paper Christopher and Towill (2000) suggested on an a priori basis that there were five underlying considerations when enterprise supply chain strategies were being investigated. These were the likely duration of the lifecycle (D); the time window for delivery (W); the anticipated volume of demand (V1); the potential variability of that demand (V2) and the level of variety (V3). Whilst this framework has a strong logical underpinning, it can be argued that it leads to the potential for a large number of theoretical supply chain strategies. It also assumes that all five dimensions would be of equal importance in all contexts. Hence, this approach should be seen as a preferred starting point which in practice may result in amalgamation and consolidation of product ranges and further system simplification as justified by that particular enterprise. However, it is far better to start the analysis with too many variables rather than too few, since the risk of wrongly selecting a particular “best” set of delivery pipelines is thereby reduced. This paper describes the reasoning behind the original DWV 3 framework and links the resulting taxonomy to a number of prescriptions for organisational change and supply chain development. It is suggested that “organisational agility” (Nagel and Dove, 1991) is a key pre-requisite for the adoption of a more flexible, differentiated supply chain strategy. There are also significant issues relating to supplier choice criteria, distribution channel design and governance. These concepts are evaluated and discussed in the context of particular examples covering a range of industries and enterprises. The context of our research can be seen from Table II. From the range of variables considered by the various authors cited therein, it is evident that value stream classification is an extensive field of interest. It obviously relates to many other concepts, including customisation, postponement, plus the lean and agile paradigms (Childerhouse et al., 2002). As observed by Turner and Williams (2005), despite such a knowledge base being available current supply chains may still fall way below customer expectations. The uptake of each of the foregoing five variables is documented for six real-world case studies. In no single case are all these taxa utilised. However, each variable is used in at least one of the cases. Furthermore, the additional variables used in the cases beyond the starting point of DWV 3 are also discussed. The empirical cases cover a range of organisations both in size and industrial sector. Hence, they provide a reasonably broad investigation of our value stream classification approach. We demonstrate that DWV 3 is a robust framework for developing the necessary pipelines for enhancing enterprise competitiveness. In practice, this system results in a limited (and therefore handleable) number of aggregate pipelines tailored to individual business needs. Thus, a company with many thousands of SKU’s will be best served with a few (usually between 4 and 6) carefully selected, designed, and implemented pipelines matched to requisite customer service levels and other key business metrics. The paper concludes with a description of the underpinning principles that should enable the effective design and implementation of responsive supply chains. 3. Searching for the key classification variables As stated earlier in the paper, Christopher and Towill (2000) argued that there are five key characteristics that influence decision making on the design of value stream delivery strategies. “These attributes are duration of life cycle; time window for JMTM 20,4 462 delivery; volume; variety; and variability (DWV3).” Table III summarises the key reasons why each of these five classification variables was included as discrete taxa within our scheme. Note that cross-reference is made to important citations in the literature as appropriate. Duration and stage of product life cycle have been noted by many authors as key characteristics influencing a demand chain to adopt tailored strategies. For example, Fisher (1997) uses the duration of product life cycle as a classification variable to differentiate between functional products with long life cycles (then set at , two years) and innovative products with short life cycles (three to 12 months). He particularly emphasises the need for specific responsive delivery strategies for the latter to best exploit the short windows of opportunity. Authors Classification variables Taxa Hoekstra and Romme (1992) Position of de-coupling point Make and ship to stock Process constraints Make-to-stock Product-market constraints Assemble to order Delivery service requirements Make-to-order Inventory cost considerations Purchase and make-to-order Fuller et al. (1993) Annual sales revenue Systems orders Annual unit volume Customer inventory replenishment Co-ordination requirements Rapid response Destination volume Nuts and bolts Handling characteristics Slow movers Customer order fulfilment interval Bulk cable Lampel and Mintzberg (1996) Level of aggregation Pure standardisation Level of individualisation Segmented standardisation Customised standardisation Tailored customisation Pure customisation Fisher (1997) Product innovation Functional products Demand volume stability Innovative products Product life cycle duration Made-to-order lead time Product variety End-of-sale mark down Pagh and Cooper (1998) Product life cycle Full speculation Product customisation Logistics postponement Product variety Manufacturing postponement Product value Full postponement Relative delivery time Delivery frequency Uncertainty of demand Naylor et al. (1999) Cost and quality Lean Lead time and service level Agile Stability of demand Leagile Lamming et al. (2000) Product innovation Innovative-unique and complex Product uniqueness Innovative-unique and noncomplex Product complexity Functional and noncomplex Source: Authors Table II. Classification of value streams within the literature Value stream classification 463 The time window for delivery (or delivery lead time) reflects the speed requirements placed on the demand chain. In combination with manufacturing and logistics lead times, it identifies the most feasible position of the de-coupling point (Hoekstra and Romme, 1992). Fuller et al. (1993) use delivery lead time as a classification variable, whilst Christopher (1992) emphasises the competitive advantage of offering increased customer service via shorter order cycle times. Volume is the third of the DWV 3 variables. Fuller et al. (1993) use this as a dominant variable in their classification of logistically distinct businesses which resulted in the pipeline set summarised in Table IV. However, they are not generic, which is the goal of this paper. In similar vain, Parnaby (1994) explains the “Lucas Way” by which the manifold manufacturing processes are segmented depending on volume. Three such categories are often used, called “runners”, “repeaters” and “strangers”. They are typically based on a Pareto analysis of present/forecast demand. Product variety, the fourth DWV 3 variable is constantly increasing in the marketplace as demand chains attempt to compete on the basis of added value in relation to such attributes as colour, form and function (Gottfredson and Aspinal, 2005; Scala et al., 2006). Variety has a major effect on a demand chain in relation to the point of differentiation as increased changeovers, larger inventory pools and scheduling issues often result. The fifth and final variable is demand variability. Fisher (1997) emphasises the impact that unpredictable demand can have on the chain either in the Classification variables Key reasons for use within value stream classification system Duration of life cycle (D) Short life cycles require rapid time to market and short end-to-end replenishment pipelines The value stream is required to “fast track” product development, manufacturing and logistics Replenishment lead times vary according to stage within the product’s life cycle Time window for delivery (W) Rapid response is required to replenish fashion goods that are selling well at a particular point in time Competitive pressures are continually reducing acceptable response times Many value streams compete on the basis of very short windows for delivery of customised products Volume (Vol) High-volume mass markets allow for lean-type production and make-to-forecast strategies Lower volume markets benefit from flexibility throughout the entire demand chain Variety (Var) Greater variety results in a larger number of SKU’s Continuous appraisal of the split between variants required during the product’s life cycle Variants popular at the introductory stage may be less popular in the decline stage Variability (Vly) Variability relates to both demand and supply predictability Spikes drastically affect capacity utilisation and resultant production techniques Unpredictability increases the risk of obsolescence and lost sales Source: Authors Table III. Key reasons for the choice of DWV 3 variables to classify demand chain types JMTM 20,4 464 form of stock-outs and resultant lost sales, or alternatively excessive obsolescence costs. Subsequently, Naylor et al. (1999) use stability of demand to appropriately match either lean or agile paradigms to best satisfy marketplace demands. This is an important concept for location of the material flow de-coupling point so as to generate appropriate value stream delivery pipelines. Whilst we have tended to focus on demand variability it should also be recognised that supply or process variability can be equally important in the choice of supply chain strategy. Indeed, in these days of increased global sourcing and off-shore manufacturing, this source of variability needs to be factored into the equation. We would propose therefore that our original DWV 3 model be amended to define “variability” as relating to the uncertainty in the wider supply chain, not just in demand. 4. Applying the value stream classification system Selecting a value stream classification is, of course, a key element, but still just one element in any business process improvement (BPI) programme to match pipelines to market-place for that particular enterprise. Considerable data collection and processing is needed to profile the product range prior to detailed design. The complete BPI procedure is shown in Figure 1. It covers agreement on the classification variables to be used and codification levels (H/L; H/M/L) as appropriate. If too many levels are selected, then subsequent analysis becomes more complicated. Indeed, the benefits from using a formal classification system may be lost and the exercise de-generate into chaos as vested functional interests within the enterprise argue strongly in favour of “their” special cases. Such judgements need to be made at the design stage when the full facts have emerged. Undertaking the codified profiling of an extensive product range is obviously a computer-based task. Nowadays, this would rightly be regarded as an integral activity of the “analytic corporation” as described by Davenport (2006). Such profiling is followed by cluster analysis to determine how much traffic might use a particular route. For example, the IMATEL (Fuller et al., 1993) industrial business process-reengineering programme generated 384 theoretical routes. However, with their then current business only 300 of these were actually populated, but many of them likely to be quite sparsely. Pipeline description Associated product attributes System orders Co-ordinating and marshalling orders for installers Dedicated support services Customer inventory replenishment Shipping regular “Top-up” orders Stocking points located near largest customer Rapid response Order-specific shipping Low-cycle time order entry and handling Nuts and bolts Ordered and stored in bulk Typically available in “barrels” Slow movers Dedicated product with unique pricing Stocks held by customers Bulk products Independent transportation Dedicated handling facilities Source: Fuller et al. (1993) Table IV. Delivery pipeline implemented at IMATEL Inc. Value stream classification 465 Figure 1. Flow diagram summarising BPI programme for implementation of value stream classification schema R es ou rc in g Ph as e A na ly sis a nd Id ea s T es tin g Ph as e D es ig n Ph as e Ex ec u tio n Ph as e Logistics Production Sales Set Up “Natural Group” Task Force Marketing Agree Target Service Levels Select Classification System Product Profiling Cluster Analysis Select Contender Pipelines Modifications Aggregation and Check Pilot Run Full-Scale Implementation Source: Authors JMTM 20,4 466 This is the BPI stage where computer-based experiments with adjusting codification are appropriate. Thus, the initial assumption on the volume breakpoint may have been (greater than/less than) 500 units per week. Given the right software to repeat the profiling for this variable changed to 1,000 (or any other sensible value) units per week is straightforward. However, realistic initial selection is preferred as the need for excessive experimentation can cloud the issue, rather than provide the required transparency. Further aggregation to eventually produce a realistic number of value stream pipelines requires informed judgement. As we shall see in the next section, IMATEL reduced the 300 “paper” routes into just six very clearly definable pipelines. Furthermore, it is