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In this study, two intelligent demand forecasting approaches are used to forecast the spare parts demand in the energy industry. In the first approach, a stacked generalization-based demand forecasting technique that combines traditional time-series forecasting and machine-learning methods is developed. In the second approach, external information (EI) is incorporated into the first one. Thus, the stacked generalization-based demand forecasting technique is used as a base model, and the EI is used to focus on predicting the peak demand; consequently, significant forecast errors can be minimized. A case study of a natural gas liquefaction company is then considered to test the performance of these methodologies. Our results show that the proposed techniques perform significantly better than the previous methods. Compared with the mean absolute scaled error and relative geometric root mean squared error of the company's forecasts, our intelligent demand forecasting approaches yield a 40.07% (36.81%) and 2.07% (3.40%) increase in butterfly-valve demand forecasting (spiral wound gasket) when no EI is used, and a 57.78% (60.41%) and 5.73% (7.36%) improvement with EI, respectively. [ABSTRACT FROM AUTHOR]

The analytical model compares the mean and variance of order interval and order quantity in supply chain channel using two methods, namely Silver Meal and the least unit cost methods. The results show Silver Meal method produced stable intervals between orders but with varying quantities while least unit cost method produced orders in varying intervals with stable quantities.

Byline: Kris M.Y. Law, Nyoman Pujawan Schedule instability has been a major problem in the manufacturing industries. This study is to investigate how the various influencing factors affect the schedule instability experienced by manufacturing companies. A research framework is developed by hypothesising the effect of the key factors (relationships with the supplier and customers, and internal operation) on schedule instability and the correlation between collective efficacy on performance and schedule instability. The results obtained indicate that the internal operations of manufacturing companies and relationships with customers have significant impacts on the degree of collective efficacy perceived, while internal operations are significantly correlated to customer relationships.