International Journal of Research in Industrial Engineering
Volume:1 Issue: 2, Summer 2012

In recent decade, firms showing an increasing interest in involving customers, suppliers, other partners and even competitors in innovation process. With current interest in collaboration, partnership, outsourcing and growth in communication facilities, open innovation has gained more importance. Today, customers are considering as one of the major partners in innovation process. In this paper we explored involving customers as a CRM activity and engaging them in different phase of innovation process. We review open innovation concept and its three core process archetypes and investigate customer involvement in innovation process base on that. In addition we distinguished between different kinds of customer as B2B or B2C, when they involve in open innovation process.

This paper addresses the permutation of a flexible job shop problem that minimizes the makespan and total idleness as a bi-objective problem. This optimization problem is an NP-hard one because a large solution space allocated to it. We use a duplicate genetic algorithm (DGA) to solve the problem, which is developed a genetic algorithm procedure. Since the proposed DGA is working based on the GA, it often offers a better solution than the standard GA because it includes the rational and appropriate justification. The proposed DGA is used the useful features and concepts of elitism and local search, simultaneously. It provides local search for the best solution in every generation with the neighborhood structure in several stages and stores them in an external list for reuse as a secondary population of the GA. The performance of the proposed GA is evaluated by a number of numerical experiments. By comparing the results of the DGA other algorithms, we realize that our proposed DGA is efficient and appropriate for solving the given problem.

Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. Based on this replacement, DEA is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Evaluating the efficiency of DMUs that have two-stage network structures is so important in management and control. The resulting two stage DEA model not only provides an overall efficiency score for the entire process, but also yields an efficiency score for each of the individual stages. In this Paper we develops Nash bargaining game model to measure the performance of DMUs that have a two-stage structure. Under Nash bargaining theory, the two stages are viewed as players. It is shown that when only one intermediate measure exists between the two stages, our newly developed Nash bargaining game approach yields the same results as applying the standard DEA approach to each stage separately. With a new breakdown point, the new model is obtained which by providing example, the results of these models are investigated. Among these results can be pointed to the changing efficiency by changing the breakdown point.

Distribution centers (DCs) play an important key role in supply chain. Delivering the right items to the right customers at the right time, at the right cost is a critical mission of the DCs. Today, customer satisfaction is an important factor for supplier companies in order to gain more profits. Optimizing the number of fulfilled orders (An order that the required quantity of all items in that order are available from the inventory and can be send to the customer) in a time period may lead to delay some major orders; and consequently lead to dissatisfaction of these customers, ultimately loss them and lead to lower profits. In addition, some inventory may remain in the warehouse in a time-period and over the time become corrupt. It also leads to reduce the benefit of supplier companies in the supply chain. Therefore, in this paper, we will present a dynamic mathematical model to flow process /storage process of goods for order picking planning problem (OPP) in DCs. And we will optimize the number of fulfilled orders in this problem with regard to a) the coefficient of each customer, b) to meet each customer's needs in the least time c) probabilistic demand of customers, and d) taking inventory to send to customers at the earliest opportunity to prevent their decay. After presenting the mathematical model, we use Lingo software to solve small size problems. Complexity of the mathematical model will intensify by increasing the numbers of customers and products in distribution center, Therefore Lingo software will not able to solve these problems in a reasonable time. Therefore, we will develop and use a genetic algorithm (GA) for solving these problems.

Due to recent advancements in information technology and communication, Electronic Customer Relationship Management has drawn the attention of many firms to achieve competitive advantages. Despite the increasing importance given to understanding their customers better, organizations find inconsistencies between information technology and the existing marketing strategies, when they come to decide upon implementing e-CRM, as well as lack of theoretical backgrounds for developing success measures in this domain. Therefore, this study deals with describing a model for successful e-CRM, using variables such as customer information quality, technology system, efficiency, customer satisfaction, and profitability. These constructs cover most of variables and concepts presented yet in theories of successful information systems and views about customer satisfaction. Ample empirical evidence is gained through this research from analyzing the outcomes of 309 questionnaires distributed to employees and customers of some automobile manufacturers having established e-CRM. The results of this study may open up new ways of applying concepts and relationship-oriented marketing models and e-CRM in automobile industry.