International Journal of Industrial Engineering and Productional Research
Volume:26 Issue: 2, Jun 2015

Today’s competition is promoted from firm against firm to supply chain versus supply chain, globalization and competition is a common phenomenon so each organization should make its supply chain as a weapon against the others, in doing so this paper presented a conceptual, graphical, step by step methodology to construct supply chain strategies by integrating strategic management’s theories, application and analysis and supply chain point of view to help managers to compete in the market.

In many manufacturing processes, the quality of a product is characterized by a non-linear relationship between a dependent variable and one or more independent variables. Using nonlinear regression for monitoring nonlinear profiles have been proposed in the literature of profile monitoring which is faced with two problems; 1) the distribution of regression coefficients in small samples is unknown and 2) with the increasing complexity of process, regression parameters will increase and thereby the efficiency of control charts decreases. In this paper, wavelet transform is used in Phase II for monitoring nonlinear profiles. In wavelets transform, two parameters specify the smoothing level, the first one is threshold and the second one is decomposition level of regression function form. First, using the adjusted coefficient of determination, decomposition level is specified and then process performance is monitored using the mean of wavelet coefficients and profile variance. The efficiency of the proposed control charts based on the average run length (ARL) criterion for real data is compared with the existing control charts for monitoring nonlinear profiles in Phase II

Assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. In many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. When some changes related to market demand occur, it is worthwhile to re-balance an existing line rather than balancing a new one. Hence, in this paper we suggest a re-balancing model of an existing assembly line in which a new demand related cycle time (CT) is embedded to the traditional assembly line balancing problem (ALBP) as a new parameter. It does not focus on balancing a new line; instead it considers a more realistic problem which is re-balancing an existing line. The objective is to re-schedule the tasks in order to reduce the current CT to the new required one such that two criteria are optimized: (i) minimization of the incurred costs and (ii) minimization of non-smoothing of reconfigured line. To solve the considered problem, an effective differential evolution algorithm is developed. Furthermore, to enhance the performance of algorithm, its parameters are optimized by the use of Taguchi method which is a conventional statistical technique for parameter design. The obtained results from computational experiments on benchmark instances show the effectiveness of suggested algorithm against other methods.

There are many real problems in which multiple responses should be optimized simultaneously by setting of process variables. One of the common approaches for optimization of multi-response problems is desirability function. In most real cases, there is a correlation structure between responses so ignoring the correlation may lead to mistake results. Hence, in this paper a robust approach based on desirability function is extended to optimize multiple correlated responses. Main contribution of the current study is the synthesis of ideas considering correlation structure in robust optimization through defining joint confidence interval and desirability function method. A genetic algorithm was employed to solve the introduced problem. Effectiveness of the proposed method is illustrated through some computational examples and some comparisons with previous methods were performed to show applicability of the proposed approach. Also, a sensitivity analysis was provided to show relationship of correlation and robustness in these approaches.

This research aims at presenting a consolidated model of data envelopment analysis (DEA) technique and value engineering to select the best manufacturing methods for gate valve covers and ranking the methods using TOPSIS.To do so, efficiency evaluation indices were selected based on the value engineering approach and different manufacturing methods were evaluated using DEA technique.Finally, effective methods were ranked based on TOPSIS. Accordingly, 48 different methods were identified for manufacturing the part. The DEA results showed that only 12 methods have complete efficiency. Meanwhile manufacturing method No. 32 (A216 WCB casting purchased from Chinese market as the raw material, machining by CNC+NC and drilling by radial drill) was ranked the first.Major limitations of the research include time limitations, place limitation, lack of access to the standards adaptability index in different machining and drilling methods, limitation on evaluating all parts of a product, limitation on a technique evaluating efficiency and ranking, and mere satisfying with superior indices in each factor of value engineering. Most previous studies only evaluated efficiency of manufacturing methods based on a single approach.By applying value engineering, which is in fact a combination of three approaches (including quality approach, functional, and cost approaches), the present research provided a far more comprehensive model to evaluate manufacturing methods in industrial.

Nowadays supply chain management has become one of the powerful business concepts for organizations to gain a competitive advantage in global market. This is the reason that now competition between the firms has been replaced by competitiveness among the supply chains. Moreover, the popular literature dealing with supply chain is replete with discussions of leanness and agility. Agile manufacturing is adopted where demand is volatile while lean manufacturing is used in stable demands. However, in some situations it is advisable to utilize a different paradigm, called leagility, to enable a total supply chain strategy. Although, various generic hybrids have been defined to clarify means of satisfying the conflicting requirements of low cost and fast response, little research is available to provide approaches to enhance supply chain leagility. By linking Leagile Attributes and Leagile Enablers (LAs and LEs), this paper, based upon Quality Function Deployment (QFD), strives to identify viable LEs to achieve a defined set of LAs. Due to its wide applicability, AHP is deployed to prioritize LAs. Also, fuzzy logic is used to deal with linguistics judgments expressing relationships and correlations required in QFD. To illustrate the usefulness and ease of application of the approach, the approach was exemplified with the help of a case study in chemical industry.