Pongsak Holimchayachotikul

Value creation through collaborative supply chain: holistic performance enhancement road map

This paper proposes an integrated novel framework between B2B-SCM using data mining techniques such as K-Means based on particle swarm intelligence (particle swarm optimisation) and association rule. It constructs relationship rules of holistic performance enhancement road map. The data-set of relationships between enterprise and its direct customers of the case study organisations in France was used for demonstration. The experiment results show how domain managers powerfully utilise the graphical analysis results to provide the holistic performance improvement and weakness resolution relationship rules. In the long run, organisations are able to use this framework to design and adjust their units to conform the exact customer needs. This paper introduces and explains a new idea of measuring value added along the supply chain from a collaborative perspective. The extended model is adapted from our previous model and from balanced scorecard model. It provides a tool to measure tangible and intangible value between partners.

Forecast of off-season longan supply using fuzzy support vector regression and fuzzy artificial neural network

NN and SVR were discussed and proposed for forecasting of off-season longan supply.Reducing NN and SVR proposed for improving computational runtime.NN and SVR forecasting models were extended by fuzzy algorithms.Totally, six proposed models were compared in term of their efficiency.Multi regression was compared and over fitting was verified. An over-supply crisis in longans in northern Thailand adversely affected farmer income. Cultivating longans off-season was adapted as an alternative solution to this over-supply problem. However, lacking information management and analysis, over supply occurred even during the off-season, leading to a slump in the sale price. Supply forecasting plays an important role in solving this problem. To solve this problem, we proposed a systematic approach for off-season longan forecasting using neural network, fuzzy neural network, support vector regression and Fuzzy Support Vector Regression (FSVR). In addition, grid search was applied to each support vector model to find its optimum architecture. Real data sets were used to evaluate and compare the effectiveness and efficiency of the algorithms. The experimental results showed that FSVR was the most effective forecasting technique.

Predictive performance model in collaborative supply chain using decision tree and clustering technique

This paper proposes an integrated framework between B2B supply chains (B2B-SC) and performance evaluation systems. This framework is based on data mining techniques, enabling the development of a predictive collaborative performance evolution model and decision making which has forward-looking collaborative capabilities. The results are deployment for collaborative performance guidelines, which were validated by the domain experts in terms of its real practical usage efficiency. This framework enables managers to develop systematic manners to predict future collaborative performance and recognize latent problems in their relationship. Its usages and difficulties were also discussed. Furthermore, the final predictive results and rules contain vital information relating to SC improvement in the long term.

Predictive performance measurement system for retail industry using neuro-fuzzy system based on swarm intelligence

Between 2011 and 2013, convenience store retail business grew dramatically in Thailand. As a result, most companies have increasingly been choosing the application of performance measurement systems. This significantly results in poor performance measurement regarding future business lagging measure. To solve this problem, this research presents a hybrid predictive performance measurement system (PPMS) using the neuro-fuzzy approach based on particle swarm optimization (ANFIS-PSO). It is constructed from many leading aspects of convenience store performance measures and projects the competitive level of future business lagging measure. To do so, monthly store performance measures were first congregated from the case study value chains. Second, data cleaning and preparations by headquarter accounting verification were carried out before the proposed model construction. Third, these results were used as the learning dataset to derive a predictive performance measurement system based on ANFIS-PSO. The fuzzy value of each leading input was optimized by parallel processing PSO, before feeding to the neuro-fuzzy system. Finally, the model provides a future performance for the next month’s sales and expense to managers who focused on managing a store using desirability function (

A Framework for Modeling Efficient Demand Forecasting Using Data Mining in Supply Chain of Food Products Export Industry

D_{i})

Data Mining for CNC Machine Adjustment Decision in Hard Disk Drive Arm Manufacturing: Empirical Study

Di). It boosted the sales growth in 2012 by ten percentages using single PPMS. Additionally, the composite PPMS was also boosted by the same growth rate for the store in the blind test (July 2013–February 2014). From the experimental results, it can be concluded that ANFIS-PSO delivers high-accuracy modeling, delivering much smaller error and computational time compared to artificial neural network model and supports vector regression but its component searching time differs significantly because of the complexity of each model.

B2B supply chain performance enhancement road map using data mining techniques

Due to the complexity of food supply chain management, it is difficult to determine precise areas requiring improvement. This paper aims to develop a key problem identification and extraction framework, based on value stream mapping and theory of constraint techniques for food supply chain improvement direction. A case study in Thailands cooked chicken industry was used to illustrate to this. It is essential to identify and understand the main problems of this chain by focusing on the effects from downstream to upstream and finding opportunities to solve defects. The results indicate that a non-value added step more than 90 % at the processing factory and caused a bottleneck in capacity resource analysis which revealed main problem.

Performance Improvement of Freight Logistics Hub Selection in Thailand by Coordinated Simulation and AHP

According to the Hamburger effect, food products export industry sector, especially cooked chicken products export to Japan of Thai industry, effort has been spent in the supply chain management (SCM) of internal efficiency, solely aiming at competitiveness survival in terms of cost reduction, better quality. To meet the customer satisfaction, the company must work towards a right time and volume of demand delivery. Therefore, forecasting technique is the crucial element of SCM. The more understanding how their company use the right forecasting based on information sharing in their SCM context; the more reducing inventory and capacity planning cost increase their company competitiveness. Presently, most of companies, in this sector, do not have a right knowledge to implement the suitable forecasting system to sustain their business; furthermore, they only use top management judgment and some of economical data for forecasting decision making to production. Because the complex, stochastic, dynamic nature and multi-criteria of the logistics operations along the food products exporting to Japan of Thai industry supply chain, the existing time series forecasting approaches cannot provide the information to operate demand from upstream to downstream effectively. The objective of the paper is how to develop a conceptual framework for an innovative and simplified forecasting system implementation for this industry based on data mining including time series factors and causal factors. Then we discuss a methodology to determine appropriated implementation guideline.