Research On Individual Demand Forecasting Method In Instant Customerisation

In recent years, much attention has been focused by practitioners as well as academics on a new manufacturing paradigm called 'instant customerisation'(IC). IC means that once customer's individual demand is put forward, a manufacturer can deliver the right product immediately. IC can realize the synergies among customisation, zero customer lead time, and low cost. One of critical operation tactics is finalise-to-individual demand forecasting to realize IC. IC attaches more importance to individual demand forecasting, especially short-term forecasts. However, there is scarcely research on universal quantificational individual demand forecasting method until now. Therefore the content in the dissertation belongs to a new field, and its characterstic is originality. An in-depth study for forecasting individual demand quantificationally results in innovative production as follows.Firstly, the concept, characteristic and qualitative classified methods are proposed. There is scarcely research about the concept of individual demand. We propose the concept of individual demand with broad sense and narrow sense respectively. In addition, the concept of product customerisation attributes is proposed. Then the main character of individual demand is studied and individual demand is classified according to various criteria. These criteria include basic purchasing motivation of consumer, the relation between individual demand and customisation product design or manufacturing, consumer psychology and marketing.Secondly, quantificational classified and clustering methods of individual demand are proposed. There is hardly any such research. According to different criterion, individual demands are classified quantificationally. These criteria include demands' correlation, well-proportioned extent of different customers' demand quantity, demands' autocorrelation and delivering frequency. Detailed steps and methods of quantificational classification are described for every criterion. Then, the method of individual demand clustering based on principal component analysis is proposed.Thirdly, we propose a case-based reasoning method for individual demand fore- casting. There is scarcely research on universal individual demand forecasting method. An important feature and difficulty of individual demand forecasting is that the value of different types of attributes must be forecasted at the same time. It is especially difficult to forecast nonnumeric attributes. Then an oriented-attribute forecasting approach is proposed in order that individual demand forecasting can be transformed into numeric forecasting. We describe how the data is preprocessed in detail, therefore individual demand forecasting can be translated into numeric time series forecasting. Then we propose a case-based reasoning (CBR) system for universal individual demand forecasting. The CBR system is divided into three stages: case representation, similarity search, and case forecasting and adjustment. In particular, we propose a distance-based formula that broadens our view by regarding the slope and length of a segment as changing characteristics of a time series. Basing on the compactness and flexibility of the formula form, its application can be either complicated or simple in various environments. Since many relevant methods can be unified into a framework by the use of our formula, it enriches and expands the measure method of time series. To some extent, pattern distance of time series proposed by Wang Da et al. is a special situation of our formula. As a universal method, it is able to be applied in many other fields. The performance of our system is evaluated using real data from a restaurant. The empirical results show that our method is able to produce a forecast with a high degree of accuracy.At last, other individual demand forecasting method in the forecasting support system is studied. The character of individual demand forecasting is analyzed, and the concept of individual demand forecasting is expanded. The request of individual demand forecasting support system is proposed. Then the application of markov chains for individual demand forecasting is discussed. The corresponding combination forecasts methods are proposed according to the character of individual demand forecasting. Some basic approach and methods about correlation sub-aggregate are put forward.