categorisation of decisions simply indicates their main purpose. Moreover, such decisions are not always obvious and depend on ‘technical’ assumptions. Note how the case example ‘Adi- das shuts its robotic factories’ demonstrates how one company, although being innovative in tackling changes in market requirements, still had to modify its operations decisions.
Figure 1.13 A classification model of operations and processes management decisions Inputs of
resources
Outputs of products
and services
Processes Chapter 7 Supply chain management
Chapter 8 Capacity management Chapter 9 Inventory management Chapter 10 Resource planning and control Chapter 11 Lean synchronisation
Direct Steering operations
processes
Develop Improving the operation’s
capabilities Design
Shaping operations and processes
Chapter 12 Improvement Chapter 13 Quality management Chapter 14 Risk and resilience Chapter 15 Project management
Deliver Planning and controlling
ongoing operations Chapter 5 Process design 1 – Positioning
Chapter 6 Process design 2 – Analysis
Chapter 1 Operations and processes Chapter 2 Operations and strategic impact Chapter 3 Product and service innovation Chapter 4 Operations structure and scope
Case example
Quantitative and qualitative decision-making in operations
In operations and process management (and throughout this text) we frequently use ‘models’, the better to understand a decision. By a model we mean an explicit statement of our image of reality. It is a representation of the aspects of a decision with which we are concerned. It struc- tures and formalises the information we possess about the decision, and in doing so presents reality in a simplified and organised form. A model therefore provides an abstraction of a more complex reality. They can be partial in that they exclude some factors, and they can aggregate or compress several factors into one, but models are at the core of operations decision- making.
Some of the models used in operations are qualitative. They categorise or describe the rela- tionships between aspects of decisions, but they do not necessarily ascribe precise associations between variables. For example, Figure 1.9, which describes the relationships between pro- cesses and operations for the Studio Division, is a qualitative model. It does not provide an
‘answer’, but it does enhance understanding and stimulate discussion around other possible ways to organise the operations.
Quantitative models are also important in operations and process management, but present different challenges. Quantitative models try to represent the underlying behaviours involved in a decision by using mathematical and/or statistical descriptions of relationships. They allocate numerical values to variables to produce a mathematical representation of reality. For example, the economic order quantity (EOQ) model that we explain in Chapter 9 is a good illustration of a quantitative model. It gives a precise relationship between the costs involved in making one particular inventory decision and therefore can be used to make the decision of how much stock to order. Well, at least it is supposed to. In fact, this model illustrated one of the problems with using a quantitative approach in operations and process management: in order to model the decision mathematically, reality has to be simplified to an extent that may severely limit its usefulness. Not that this is a condemnation of quantitative modelling. Practical operations management depends on the quantification of decision-making where possible. But for most operations decisions some combination of quantitative and qualitative modelling is required.
‘Behavioural’ operations
Academics who write about, research or teach operations management are sometimes accused of ignoring the ‘practical reality’ of how operations management decisions are made. Their models, frameworks and guidance, it is claimed, do not reflect how people really behave when making operations management decisions in practice. This has led to the development of a products. It was hoped that the Speedfactories could pro-
duce shoes in days and replenish the fastest-selling prod- ucts during the same season.
Yet within four years of the Speedfactories opening, Adidas announced it would cease production at the facil- ities. The company said it made more sense for the com- pany to concentrate its Speedfactory production in Asia where the know-how and the vast majority of its suppli- ers were located, and where Adidas already makes more than 90 per cent of its products. Adidas said it would use its Speedfactory technology at two Asian supplier fac- tories, and would concentrate on modernising its other
suppliers. One reason for the relative failure of the Speed- factories was the restricted range of models they could make. Adidas had set up Speedfactories to make train- ers with a knit upper and Adidas’s unique bouncy ‘Boost’
midsole, but it could not make leather shoes with a rub- ber sole because that used a different kind of joining pro- cess. So, as was pointed out by commentators, the effort was a failure not because its objective was flawed, but rather because it paid insufficient attention to the manu- facturing process itself. Moreover, as Adidas pointed out, the learning that it gained from the Speedfactories would be used in its Asian supply base.
Critical commentary
■
33 (relatively) new branch of operations management. It is called ‘behavioural operations man- agement’ (BOM) or simply ‘behavioural operations’, and explores the interaction of human behaviours and operational systems and processes. More specifically, it challenges the idea that managers are rational when making decisions that impact operations performance. One research team has summarised what they see as common behavioural assumptions to opera- tions models:10
• People are not a major factor in operations decisions.
• People are deterministic and predictable.
• People make decisions independently of each other.
• People do not learn from experience.
• People are not part of the product or service.
• People are emotionless.
• Work is perfectly observable and can be understood.
Clearly these assumptions are extremely unrealistic, and while no experienced operations manager would ever subscribe to them, they do act as a warning as to how the models, frame- works and techniques in this text should not be interpreted. Very few of the models that we use are rigidly prescriptive. Generally, they do not attempt to dictate a single ‘optimum’ solution to any problem. Rather they try to structure and clarify operations decisions, the better to under- stand, debate and, hopefully, make better decisions.
Critical commentary
All chapters will contain a short critical commentary on the main ideas covered in the chapter. Its purpose is not to undermine the issues discussed in the chapter, but to emphasise that, although we present a rela- tively orthodox view of operations, there are other perspectives.
•
The central idea in this introductory chapter is that all organisations have operations (and other func- tions) that have processes that produce products and services, and that all these processes are essen- tially similar. However, some believe that by even trying to characterise organisations in this way (perhaps by calling them ‘processes’) one loses or distorts their nature and depersonalises or takes the‘humanity’ out of the way in which we think of the organisation. This point is often raised by ‘pro- fessional’ staff. For example, the head of one European ‘Medical Association’ (a doctors’ trade union) criticised hospital authorities for expecting a ‘sausage factory service based on productivity targets’. No matter how similar they appear on paper, it is argued, a hospital can never be viewed in the same way as a factory.