Organizational readiness for implementation of Supply Chain Analytics

Supply chains today are amassed with data. To remain competitive in a global economy, supply chain organizations need to constantly derive meaningful information from this plethora of data and make critical business decisions. This process is also referred to as Supply Chain Analytics (SCA). This paper attempts to measure the readiness of organizations to implement Business Analytics – a more generic form of SCA. The results were derived from the survey analysis of 112 respondents in 7 countries from various industries and professional backgrounds. This survey analyzed organizations in four broad categories – standardized and integrated data, well-established infrastructure, sound technical and non-technical expertise and the organizational culture and strategy – and attempted to determine their readiness for implementing Analytics in the organization

A collaborative demand forecasting process with event-based fuzzy judgements

Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy

A demand forecasting methodology for fuzzy environments

Ülengin, Füsun (Dogus Author)Several supply chain and production planning models in the literature assume the demands are fuzzy but most of them do not offer a specific technique to derive the fuzzy demands. In this study, we propose a methodology to obtain a fuzzy demand forecast that is represented by a possibilistic distribution. The fuzzy-demand forecast is found by aggregating forecasts based on different sources; namely statistical forecasting methods and experts’ judgments. In the methodology, initially, the forecast derived from the statistical forecasting techniques and experts’ judgments are represented by triangular possibilistic distributions. Subsequently, those results are combined by using weights assigned to each of them. A new objective weighting approach is used to find the weights. The proposed methodology is illustrated by an example and a sensitivity analysis is provided