Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency-Frequency-Monetary (RFM) model has been commonly recognised as an effective approach to analyse customer behaviour. However, the traditional RFM approach is a coarse method for quantifying customer loyalty and contribution that can only provide a single lump-sum value of the recency (R), frequency (F), and monetary value (M); hence, it discards information regarding customers' product preferences. Typically, different customers make different purchases. Subsequently, purchases are likely to be different across customers. This creates data sparsity, which affects the performance of conventional clustering methods. In this study, we integrated the group RFM analysis and probabilistic latent semantic analysis models to perform customer segmentation and customer analysis. The results indicated that the developed approach takes into account the product preference and provides insight into and captures a wide ABOUT THE AUTHORS Arthit Apichottanakul is currently working as lecturer in the Faculty of Technology, Khon Kaen University, Thailand. He completed his PhD in Industrial Engineering from Khon Kaen University, Thailand. His current research interests include intelligent applications, optimization and data science in logistics and supply chain management.

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In this paper, the artificial neural networks (ANN) is used to estimate the market share of Thai rice in the global market. Two models are formulated under two assumptions. First, the market share depending on exporting prices of rice of Thailand, Vietnam, India, USA, Pakistan, China. Second, only the export prices of rice from Thailand, Vietnam, USA, and China are considered. The export prices are used as input parameters, while the market share of Thai's rice in the global market is the only output parameter of the models. Annual data from 1980 to 2005 are gathered from United States Department of Agriculture (USDA) and Food and Agriculture Organization of the United Nations (FAO). The study showed that the second model provide more promising results with the minimum mean absolute percent error (MAPE) of 4.69% and the average MAPE of 10.92%.

The pork processing industry resembles a case of disassembly scheduling because a planner needs to decide the pig size and quantity to be supplied to the slaughtering house, as well as the amount of meat and meat size needed to process an order. The meat processing resembles disassembly scheduling for multiple products with parts in commonality. We extend the general disassembly model further to cover product perishability of the meat while allowing demand to occur in other levels of product hierarchy rather than the leaf product (products that are no longer being disassembled). We also allow the model to obtain outsource products to be processed if it achieves a more economical solution. In this study, we developed a mathematical model to determine the quantity and size of pig supplies (root items) and meat cuts (parent/child items) to be processed for an order to minimize the total cost. Computation time and cost of generated test problems are obtained. The application of pork processing plan is demonstrated.