Definition of Terms
Some of the terms we use may not be familiar to everyone, or may have a slightly different meaning to that which you are used to.
Each link below will take you to a definition of the term
- Manufacturing Strategy
- Resource and Competence-based Strategy
- Management Tools
- Costs of Complexity
- Measuring Complex Systems
- Supply Chain Management
- Business Performance Measurement
- Make-Buy Decisions
- The Learning Curve for Industry
Strategy has been defined as:
" ..the determination of the basic long-term goals and the objectives of an enterprise, and the adoption of courses of action and the allocation of resources necessary for carrying out these goals." (Chandler, 1962, Strategy and Structures: Chapters in the History of the Industrial Enterprise, MIT Press, Cambridge, Mass)
Such a broad definition of strategy covers a multitude of decisions from "What business should we be in?" to "How can manufacturing contribute to the competitive advantage of this business?"
Recognising this has led to the idea of a hierarchy of strategy with three major levels (Hofer and Schendel, 1978) Strategy Formulation: Analytical Concepts, West, St. Paul):
- Corporate Strategy – what set of businesses should we be in?
- Business Strategy – how should we compete in a given business?
- Functional Strategy – how can this function contribute to the competitive advantage of the business?
Manufacturing strategy is one such functional strategy. The concept of manufacturing strategy is often traced to Skinner's 1969 Harvard Business Review article, "Manufacturing- Missing Link in Corporate Strategy". Skinner suggested a top-down approach to manufacturing. Manufacturing objectives should be derived from business objectives, and then manufacturing policies developed to address these objectives.
Manufacturing objectives cover such things as cost, quality, delivery and flexibility and usually there are trade-offs between them. Trade-off decisions are also required in a number of key areas in order to support the manufacturing objectives. Skinner identified five decision areas: 1) plant and equipment; 2) production planning and control; 3) labour and staffing; 4) product design / engineering; and 5) organisation and management. These basic ideas (trade-offs and consistency of objectives/policies) have formed the foundation from which the current understanding of manufacturing strategy has developed. We formally define it as follows:
A manufacturing strategy is defined by a pattern of decisions, both structural and infrastructural, which determine the capability of a manufacturing system and specify how it will operate to meet a set of manufacturing objectives which are consistent with overall business objectives.
Useful introductory books to manufacturing strategy concepts are:
- Hayes, R.H. & Wheelwright S.C.(1984) Restoring Our Competitive Edge (Competing through Manufacturing) ,Wiley, New York.
- Skinner, W. (1985) "Manufacturing – the formidable competitive weapon", Wiley, New York
Practically all firms base their business objectives on satisfying their customers’ needs. This is a valuable approach for aligning products, services and objectives with existing markets. It satisfies the Opportunities and Threats half of a SWOT (Strenghts, Weaknesses, Opportunities, and Threats) analysis. Resource and competence-based strategy making provides real insight into your firm’s Strengths and Weaknesses, an aspect many firms neglect. Why? Partly because it is much easier to analyse markets that are, so to speak, "out there" than to talk about strengths and weaknesses which are "in here, in you, round this table and just outside that door". And partly because there are few pragmatic methods to help managers, see reference 3.
However the achievement of any of your business objectives is dependent on your strengths and weaknesses. For example, all firms in a market may wish to reduce their new product lead-times but one will do so more quickly and reliably than others. Why is that? Is it because of the market? No - it is to do with the resources each company has or can access (cash, knowledge, equipment, values, reward systems etc.) and the effectiveness of the management of those resources towards reducing lead-time.
Resource-based strategy is the one approach that concentrates on the individuality of each firm, the important differences between each firm and its competitors. According to this approach every firm, including yours, is unique. And it is on the peculiarities that make your firm unique that sustainable competitive advantages can be based. Being aware of and then improving and protecting these unique resources and managing them more effectively will reinforce your strengths and ameliorate your weaknesses and thereby improve your competitive position.
Resource and competence-based approaches are particularly valuable when:
You are considering changing the boundaries of your business, for example:
- By acquisition or divestment
- Entering joint ventures or other partnership arrangements
- Considering Make versus Buy alternatives
- Entering new markets
- Taking on new technologies
- Disaster is at hand.
- You are trying to build a more sustainable competitive advantage.
- You need fresh perspectives on how to improve your business.
- You wish to take account of your resources in plans to achieve your objectives.
1. Barney, J.B. (1996). Gaining and Sustaining Competitive Advantage , Addison-Wesley, Reading, MA.
Especially Chapter 5 "Evaluating Firm Strengths and Weaknesses: Resources and Capabilities".
2. Grant, R.M. (1991). "The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation", California Management Review, Spring,114-135.
For one of the best overviews of the area.
3. Mills, J.F., Platts, K.W., Bourne, M.C.S., & Richards, A.H. (2002). "Competing through Competences", Cambridge University Press, Cambridge, UK.
A unique (we believe) source of practical tools for using these ideas and written for practising managers.
4. Prahalad, C.K. & Hamel, G. (1990). "The core competence of the corporation"' Harvard Business Review, May-June, pp. 79-91 (Reprint # 90311).
For why managers got excited about the internal analysis of firms and the most popular reprint from HBR ever (at least up to 2001).
5. Teece, D.J., G. Pisano and A. Shuen (1997). "Dynamic Capabilities and Strategic Management", Strategic Management Journal., Vol. 18, No. 7, 509-533.
A classic paper which, prior to its publication in 1997, might well have been the most photocopied working paper in the history of strategy research.
We define tools as ways to carry out a particular (improvement) task. It has a clear role and narrow focus. Thus, it often could be used on its own. Examples of management tools are Force Field Diagram, Pareto Analysis, Process Diagram etc. Whereas,techniques are programmes or procedures which comprise a set of tools. Thus, techniques have wider application than tools and may need more skill and knowledge to apply them effectively. For example, SMED is a tool for reducing setup time on machine, and it is also part of the tools used in TQM or JIT techniques. Other management techniques are Value Analysis, Hoshin Kanri, Statistical Process Control etc.
In CSP, efforts have been put in to develop effective, research-based tools and techniques for strategy-making and performance measurement system design. Chief among them is TAPS (Tool for Action Plan Selection) and Developing Decision Support Tools projects. We also have produced a catalogue which provides brief introduction to the management tools and techniques.
The results of many of these projects are now in the public domain and being exploited commercially.
Following are some of the useful resources to learn more about management tools and techniques information and research:
- www.bain.com (for information on latest survey on management tools and techniques)
- Euske, K.J. & Player, R.S. (1996) ‘Leveraging Management Improvement Techniques’, Sloan Management Review, pp.69-79.
- Dale, B. and Mcquater, R. (1998) ‘Managing Business Improvement and Quality: Implementing Key Tools and Techniques’, Oxford: Blackwell Publisher.
- Superfactory (www.superfactory.com)
The complexity index measures how complex a manufacturing system is. It takes into account the variety of products made, the resources used, and the uncertainty in running the system. A high complexity index is not always a bad thing. It could mean a high flexibility or agility of the system responding to changing markets. Analysing the costs of complexity makes complexity tangible enabling players within a supply chain to understand where the major and minor opportunities for cost reduction lie.
Costs of complexity are the costs, involved in running a system. Like the measure of complexity itself, the cost of complexity can be divided into two categories: costs of structural complexity and costs of operational complexity.
- Costs of structural complexity include all costs generated by production in predictable circumstances. The budgeted costs of production, raw materials and labour are associated with structural complexity. These costs are determined by the variety of products, raw materials needed, type of process necessary to manufacture the products, equipment to perform the processes and handle the materials, labour, and overheads. In general, the higher the complexity of the products and the production system, the greater the cost.
- Costs of operational complexity are the extra costs caused by actual variation from the predictable state due to uncertainty. They include all unexpected costs due to customer changes, breakdowns, unnecessary stock holding, and loss of business as a consequence of shortfalls. All costs of operational complexity are non-value-added.
- Frizelle G. and Woodcock E., "Measuring complexity as an aid to developing operational complexity", International Journal of Operations and Production Management, 15(5), 26-39, 1995.
- Frizelle, G., "The management of complexity in manufacturing", Business Intelligence, London, 1998.
- Frizelle G. and Suhov Y. M., "An entropic measurement of queueing behaviour in a class of manufacturing operations", Proceedings of Royal Society Series A, 457, 1579-1601, 2001
- Sivadasan, S., J. Efstathiou, G. Frizelle, R. Shirazi and A. Calinescu, An information-theoretic methodology for measuring the operational complexity of supply-customer systems, International Journal of Operations & Production Management, 22(1), 80-102, 2002.
Systems encountered in the real world are complex. Complex, in this context, means systems that exhibit variety, connectedness and uncertainty. The uncertainty arises because of the presence of variety and connectedness. In the area of manufacturing and supply chains, the presence of complexity is a barrier to their effective management. Understanding how complexity affects such systems would allow for a focused approach to improving their performance.
There is one type of complexity, called Operational Complexity, that lends itself to being measured. Theory suggests that an appropriate metric is the average rate at which the supply chain or manufacturing process generates information i.e. the entropy of the process. Theory also predicts that operational complexity shows itself through the formation of queues – these can be of either products or information. Thus the presence of complexity turns the operation into a sort of obstacle course. However the presence of queues means that the system is capable of being directly measured, by observing their dynamic behaviour and its causes.
Complexity can be attacked either through simplification or by trying to control it. The former is usually associated with capital expenditure, the latter is a revenue cost. However complexity is neither good nor bad. For example mass customisation involves deliberately expanding the complexity of the product range to offer customers greater variety. The key is to be able to do this without raising prices. Therefore one can differentiate between ‘good’ complexity – complexity the market will pay for and ‘bad’ complexity that merely involves additional cost.
Over the last seven years, measurement exercises have taken place in factories and supply chains. In all cases it has lead to the identification of areas where operational improvements can be made.
Frizelle, G. 1998 The Management of Complexity in Manufacturing. Business Intelligence
Casti, J. 1994 Complexification. Harper Collins New York
Calinescu, A., Efstathiou, J., Scrin, J., and Bermijo, J. 1998 Applying and assessing two methods for measuring complexity in manufacturing. J Operat. Research Soc. 49 723-733
- On the operational and tactical level, Supply Chain Management is the effort towards the integration of business processes across firm boundaries that goes beyond logistics and includes areas such as quality management or new product development. Issues here are the use and integration of modern information technology in the supply chain, process-reengineering, supplier selection and development, relationship management, joint product development, new product introduction and marketing etc. The emphasis is on operational efficiency in the supply chain.
- On a more strategic level, the term Supply Chain Management is connected to notions such as "value chain", "supply network" and "extended enterprise". The Supply Chain Management paradigm leads managers to re-think their current business models, to look upstream and downstream their supply chain, and then take a further step back and look at the supply network as a whole to:
- analyse how their company, products and business processes fit in the overall supply network,
- compare their supply network with competing networks,
- analyse future threats and opportunities such as stemming from changing routes to markets, dynamics of power and dependence in the supply network, loss and development of competences within the supply network etc
The Supply-Chain Council was formed in 1996 by individuals representing companies including AMR Research , Bayer, Compaq Computer, Pittiglio Rabin Todd & McGrath (PRTM), Procter & Gamble, etc. It developed a well accepted supply chain operations reference model: SCOR.
- Bechtel, C. & Jayaram, J. (1997) "Supply Chain Management – A Strategic Perspective", International Journal of Logistics Management, Vol.8, No.1, pp.15-34.
- Hines, P., Lamming, R., Jones, D., Cousins, P., Rich, N. (2000) "Value Stream Management – Strategy and Excellence in the Supply Chain", FT Prentice Hall, London.
- Mills, J. & Schmitz, J. (2002) "A Strategic Review of Supply Networks", Working Paper, Centre for Strategy and Performance, University of Cambridge.
Business Performance Measurement is concerned with:
- measuring the efficiency and effectiveness of actions;
- aggregating and standardising information;
- setting appropriate targets.
The development of performance measures plays an important role in formulating and clarifying plans and strategies and setting targets for employees, project teams and business units. Performance measures should be part of a consistent performance measurement system which connects measures for top management, different business units, middle and lower management and sometimes even individual projects and employees. A performance measurement system should also ensure that a limited and manageable number of measures is chosen and that the measures are balanced in terms of:
- financial and non-financial measures;
- leading and lagging indicators (input, process and output measures);
- trade-offs between measures.
The most popular performance measurement framework is Kaplan and Norton’s "Balance Scorecard". A well balanced performance measurement system will help to:
- develop, discuss and formulate the company’s strategy;
- communicate the strategy throughout the organisation;
- define objectives and specify targets for business units, project teams and employees;
- motivate and monitor employees and managers and guide their actions;
- inform employees, managers and shareholders on the efficiency and effectiveness of past actions and strategies and the likelihood of success for future actions.
Centre for Business Performance at Cranfield University School of Management
The Performance Measurement Association (PMA) is a global network for those interested in the theory and practice of performance measurement and management. It was launched at the 2nd international PM Conference, PM 2000, Cambridge, UK
- Neely, A, Mills, J., Gregory, M., Richards, H., Platts, K., Bourne, M. (1996) "Getting the Measure of Your Business" Workbook, Manufacturing Engineering Group, Cambridge.
- Neely, A. (ed.) (2002) "Business Performance Measurement – Theory and Practice", Cambridge University Press, Cambridge.
- Simons, R. (1999) "Performance Measurement and Control Systems for Implementing Strategy", Prentice Hall, Upper Saddle River, NJ.
Almost any product or service available in the marketplace today involves inputs of one kind or another: raw materials are turned into subcomponents, subcomponents are then assembled to form finished products or are used as an input to some kind of service, etc. However, few companies have all the resources and expertise required to fulfill all of these functions. Sooner or later, a firm generally has to rely on other firms to supply at least some of these inputs. But what tasks and functions should be performed in-house—that is, within the firm—and which ones should be relegated to outside suppliers? And for those tasks that it chooses to outsource to other firms, what type of relationship should be developed with the supplying companies? These questions point to a recurring set of issues that are collectively referred to as "make-buy decisions."
A single make-buy decision in and of itself may not seem to be of great consequence to a company. Among the many components and services that a firm uses to generate revenues, relatively few are singularly of critical importance to the firm’s immediate survival or success. But the aggregate of these decisions effectively defines the scope of what a company is and is not doing. In other words, the resolution of make-buy questions defines the boundary of the firm.
To be sure, quite a bit of research has already been done in the field of make-buy decisions. Dating as far back as 1937, dozens of outsourcing frameworks have been developed for a wide range of industries. Among the broad array of different approaches adopted over the years are valuable ideas like transaction cost analysis and core competencies. Listed below are a few of the more prominent papers on this topic.
(1) Prahalad, C. K., & Hamel, G. (1990). The Core Competence of the Corporation. Harvard Business Review (May-June), 79-91.
(2) Venkatesan, R. (1992). Strategic Sourcing: To Make or Not To Make. Harvard Business Review (November-December), 98-107.
(3) Quinn, J. B., & Hilmer, F. G. (1994). Strategic Outsourcing. Sloan Management Review (Summer), 43-55.
(4) Fine, C. H., & Whitney, D. E. (1996). Is the Make-Buy Decision Process a Core Competence? (Working Paper # 3875-96). Cambridge, Massachusetts, USA: MIT Center for Technology, Policy, and Industrial Development. This paper is also available from
The learning curve concept for industry states that the input cost, or time, per unit produced decreases by a set percentage every time the cumulative production output doubles. The roots of the learning curve concept go back more than a century to studies of how an individual’s performance at a task improves with experience (e.g., Thorndike, 1898; Thurstone, 1919). Wright (1936) introduced the concept to an industrial environment by showing that the decrease in direct labour cost fell by 20% every time the cumulative production doubled for airframe manufacture. Since Wright’s study, a similar effect has been shown to exist in the case of a small group, an organisation and an industry (see Argote et al., 1990, for references). In the literature the phenomenon is variously referred to as "learning curve", "experience curve", "learning by doing" or "learning by use".
The most common form of the relationship between input per product is a log-linear model in the form of the function:
- y = input cost for the xth unit
- x = cumulative number of units produced
- a = input cost for the first unit
- b = progress rate
Say you have a production process with an input cost of £1,000 for the first unit and a 20% reduction in per unit cost for every doubling in the cumulative production output. The values for the parameters are then:
a = 1,000
b = -log(1 - 0.2) / log(2) = 0.322
The resulting curve (a straight line) for the unit cost plotted against cumulative production using Equation 1 is shown on a log-linear graph in Figure 1.
Although a 20% reduction in costs per doubling in cumulative production has been used as a rule of thumb in many industries, it should be cautioned that this can differ even for similar industries, within companies and for subsequent runs of the same product in the same plant.
- Dutton, J.M. and Thomas, A. (1984) "Treating Progress Functions as a Managerial Opportunity", Academy of Management Review Vol. 9 No. 2, pp. 235-247.
- Yelle, L.E. (1979) "The Learning Curve: Historical Review and Comprehensive Survey", Decision Sciences Vol. 10, pp. 302 – 328.
- Thorndike, E.L. (1898) "Animal Intelligence: An Experimental Study of the Associative Processes in Animals", The Psychological Review: Monograph Supplements Vol. 2, pp. 1-109.
- Thurstone, L.L. (1919) "The Learning Curve Equation", Psychological Monographs Vol. 26, pp. 1-51.
- Wright, T. (1936) "Factors Affecting the Cost of Airplanes", Journal of Aeronautical Science Vol. 4 No. 4, pp. 122-128.
- Argote, L., Beckman, S.L. and Epple, D. (1990) "The Persistence and Transfer of Learning in Industrial Settings", Management Science Vol. 36 No. 2, pp. 140-154.
- Learning Curve Calculator: