INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2009 | Volume:20 | Issue:1|Papers Count:9

One of the efforts in a pull system to meet the final goal of Just-In- Time is reducing Work-In-Process (WIP) without reducing throughput rate an service level. This paper proposes a new hybrid control policy using extended Kanban, Generalized Kanban and CONWIP to form the final construction of production plan. Simulation of a 3-stage case study with probabilistic parameters showed the reduction of inventory level with acceptable service quality. Data Envelopment Analysis (DEA) is implemented for the evaluation and verification of the proposed model. The result obtained indicates that the proposed model is quite efficient and applicable to solve real world problems.

Sharing common resources is amongst critical factors creating competitive advantages in business and manufacturing. In today’s competitive and dynamic environment, application of the resource sharing approach has become the focal point of attention for business managers. By resource sharing and through common platform guidelines, the possibility of producing an extended variety of products using the least variable production elements is provided. Meanwhile, today’s manufacturing industries are trying hard to plan and manage an effective foundation for creating a value stream from the point of supplying resources to the stage of delivering the final product to the customer. Standardizing and sharing product components and common platforms is of great assistance to this effort. This paper presents a mathematical model to contribute to making the decision of choosing the best combination of common components and analyze the time effects of commonality approach and its possible consequential savings as one of the key performance indicators of a supply chain based on common platform.

Product support and after sales services are among the important areas which have attracted the attention of managers and decision makers, especially in the field of supply chain and logistics management. Supplying the spare parts of products to guarantee the desired operation of product during its life time is in the focus of attention of logistics and supply chain managers. What makes the demand forecasting possible and real is the correct identification of demand affecting factors and their relation. So, the best model of spare parts' demand forecasting is one which incorporates all factors influencing the failure rate of the parts. This article presents a model which incorporates the environmental covariates influencing the failure rate as well as the reliability characteristics of parts. In addition, a portion of spare parts demand is due to the interaction of different parts in a system which is known as failure interaction. This factor is regarded in the model as another factor affecting the failure rate of the parts and, the demand estimation as a result. This model uses the reliability models and renewal process to forecast spare parts demand, based on reliability, operational environment and failure interaction.

Working in noisy job shops and doing hard works reduce the efficiency of workers and the resulted injuries impose cost on such an industries. Since, there are different jobs and working environment within a factory, it is possible to reduce job related injuries through a suitable job rotation program. In this paper, we propose a model for determining the job rotation program; that is a bi-objective model applied to minimize daily noise exposure index as well as minimizing job severity index due to lost working days. The Global criterion. Lp- Metric. Method is proposed as the solution methodology. Finally, the model is applied in different cases and the results indicate better utilization compared to single objective models.

Statistical process control charts are generally designed assuming that when the process is in control the observations are independent and identically distributed (i.i.d.) over time. However, the assumption of independence is easily violated when a process inherently generates auto correlated observations. When traditional control charts are applied to such processes then the false alarm rate experienced would be higher than what is expected.Machining process, due to the tool wear out, usually generates auto correlated observations. If such phenomenon is not incorporated in the chart design then one should expect a pattern in the plotted observations that will eventually lead to false alarms from time to time. This paper discusses the application of logistic regression to model and eliminate patterns that appear on fraction nonconforming items chart because of tool wear.Numerical results indicate significant improvements.