Period Batch Control - A Production Planning System Applied to Virtual Manufacturing Cells

Period Batch Control (PBC) system has been known for its implementation with the classical group technology (GT) cells, and it has been known for its simplicity. The main production planning decisions concern the choice of the period length and the stage number and contents. Also, in order to better integrate the production planning with the application of GT cells at the shop floor, the concept of virtual manufacturing cells has been applied. Since virtual cells configurations are changing periodically, a model for implementing the PBC system into virtual manufacturing cells environment is developed. The model enables alignment of the PBC principles and rules with virtual cell design goals. Model is tested on the case study of furniture production. With the use of scheduling software, different scheduling rules were simulated for four production weeks. The experimental results from these for production weeks show how the choice of PBC parameters impacts the virtual cells configurations, machine sharing and utilization

A Machine Learning Based Method for Customer Behavior Prediction

Under the data-driven environment, market competition is increasingly fierce. Enterprises begin to pay attention to precise marketing to make costs down, improve marketing efficiency and competitiveness. E-mail marketing is widely used in enterprises due to its advantages of low cost and wide audience. This paper uses machine-learning techniques such as decision tree, cluster analysis and Naive Bayes algorithm to analyze customer characteristics and attributes with historical purchase records, and further analyzes the key factors that affect potential customers\u27 purchase behavior by selecting models with high promotion degree through promotion graph, to realize accurate marketing. The results show that the prediction effect of decision tree is better than clustering analysis and Naive Bayesian algorithm, and has a higher promotion degree. The customers who are 45-55 years old and commute 1-2 kilometers away are more likely to make purchases if they do not have a car or have a car at home

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies

Periodic Virtual Cell Manufacturing (P-VCM) - Concept, Design and Operation

This paper presents and discusses the concept of Periodic Virtual Cell Manufacturing (P-VCM). After giving an illustrative example of the operation and design complexity of a P-VCM system, we present an industrial case to study the applicability of the concept. The illustrative example and the industrial case indicate the context for which P-VCM is appropriate