There’s a lot of talk about supply chain modelling, but what does it really mean? From an academic point of view it means trying to represent real life activities, as closely as possible, through the use of mathematical analysis and computing power; usually with the aim of drawing conclusions about how supply chains can be improved. That’s fine, but it’s not much use if the model is so complex that no-one but the modeller can understand or use it. The real aim in modelling is to help the end user gain a better understanding of all or part of the supply chain, so they can make informed decisions for the benefit of the business. Modelling is not an end in itself, it’s just one way of helping to improve the decisions made by an organisation. It’s fundamentally concerned with taking data about supply chain history and turning this into information which can be used to help predict what might happen in the future. It can be as simple as applying growth to last year’s sales to estimate next year’s (yes, that’s a model!), or it can be as complex as trying to predict what would happen to costs and service if 30 distribution centres each with 50,000 lines, spread across the whole of western and eastern Europe, were consolidated into far fewer locations (now that is a model!).
The Logistics Business has been developing supply chain models for almost 20 years, and we think we know a thing or two about them. And one of the most important lessons we have learned is not to make the models too complicated. They have to be easily understood by those who will make use of the output, and it has to be possible to apply practical considerations and experience where the validity of the assumptions is uncertain. What we have done is to develop a suite of modelling tools which are linked through a common database and which can be used together or independently. The key elements are geography, network, transport, labour, and cost, with further elements covering distribution centre operations. Together they can build a full picture of the whole supply chain and logistics activities, with more or less emphasis on any one element, depending on data availability and the modelling objectives. Because the elements are separate but linked, it is much easier to see the impact of any assumptions and to consider the practical implications of these. Modelling in this way is far more cost effective than the use of some of the expensive, well known modelling tools and, what is more, it is easier to offer the user access to the models for further analysis as the business changes.
Our success has come from modelling to an appropriate level of complexity, but always balancing the output of the model with practical considerations based on operational experience.