A framework for collaborative planning, forecasting and replenishment (CPFR): state of the art

Purpose– Although many papers purport the significant value attributable to supply chain performance from the use of Collaborative Planning, Forecasting and Replenishment (CPFR), the question of ‘what are the main constructs and efficient framework for successful implementation of CPFR?’ remains largely unanswered. This question will be addressed by identifying and analysing the main constructs for successful implementation of CPFR. This paper attempts first to seek answers to this question. Second, to review the scope and value of CPFR using a devised state-of-the-art taxonomy for the classification of selected bibliographical references and third, to develop a conceptual framework by identifying areas which need more research. Design/methodology/approach– The method underlying this paper followed the steps of a systematic literature review process outlined by Soni and Kodali (2011). The review is based on a total of 93 papers published from 1998 to 2013 on CPFR. Findings– Four main constructs for successful implementation of CPFR have been identified: CPFR enablers, CPFR barriers, trading partner selection and incentive alignment. The findings indicate that there is a need for better understanding of the amount and level of information sharing as an important function of CPFR implementation. The paper also illustrates a number of shortcomings in the current literature and provides suggestions to guide future research on implementing CPFR in different industries. Practical implications– This paper is of interest to both academicians and practitioners as it helps to better understand the concept and role of CPFR in supply chain integration and its implementation results, enablers and inhibitors. The proposed framework in this paper can be used to give insight for future research and practice. Originality/value– The paper offers a framework for the review of previous research on CPFR and identifies the most important shortcomings that need to be addressed in future research. In addition, this review is both greater in scope than previous reviews and is broader in its subject focus

Designing a Customer Relationship Management System in Online Business

With the advancement of online shopping technology, it has become the first choice for most consumers. The activity of online stores in this competitive business space should be in line with the expectations of their customers. Understanding, collecting, maintaining and organize data in online stores makes it easier for managers to decide. So, in this research, we examine the textual and non-textual of user opinions and reviews. We use rapid miner software and text mining. In this research, the processes are aimed at finding active users, analyze the user type and their suggestions, analyzing the strengths and weaknesses of the products, and categorizing them with the K-NN and Naïve Bayes algorithms.  Finally, suggestions were made to increase loyalty and improve business using the results obtained from the processes

GREEN SUPPLIER SELECTION BASED ON THE INFORMATION SYSTEM PERFORMANCE EVALUATION USING THE INTEGRATED BEST-WORST METHOD

Information Systems (IS) have become crucial for all the organizations to survive in contemporary technology-oriented environment. Consequently, the number of companies and organizations which have invested widely in their IS infrastructures to present better services and to produce higher value products is increasing. On the other hand, nowadays, because of the increase of governmental rules and serious requirements of more people in the case of environmental protection, it seems necessary for all the enterprises to follow these regulations if they want to survive in the global markets. However, what is at issue here is not just the companies’ agreement with the environmental laws; in addition, they should apply some strategies to decrease the negative environmental impacts of their products in some countries. Thus, the aforementioned arguments are the reasons for the compulsory use of the green supplier selection (GSS) in all firms. Considering the mentioned contents, the purpose of this study is representation of the relation between ISs and GSS as two vital components of firms in a novel way which has not been done before. Actually, it shows the ISs' performance or effectiveness to select the green suppliers taking into account the different levels of importance of GSS measures (including eight criteria and 31 sub-criteria), using a multi-criteria decision-making method called Best Worst Method (BWM) to identify the weights (importance) of GSS measures and compute the GSS performance of 10 ISs in a company using the data gathered in a survey from ISs' experts

A Multidimensional Decision Making for Supplier Selection in the presence of Information Systems

Information Systems (IS) have turned into vital means for companies to survive in the contemporary technology-oriented environment. Subsequently, over the last decades, this has brought about the heavy investment of companies in ISs to guarantee high-quality products and services. Similarly, supplier selection (SS) plays an inescapable role in today’s business. In addition, there are several studies published showing the importance of the ISs in the SS problem. However, there has not been any work evaluating the effectiveness of ISs on the SS problem, including a comprehensive and up-to date SS model. Therefore, this study proposed a complete model including six criteria that are almost most important and shared in the literature: sustainability, reliability, resiliency, greenness, risk and cost, and 31 sub-criteria. Then the effectiveness of 10 ISs on the SS problem has been shown through using BWM in two consecutive stages, and then the model conducted in Emdadkhodro automotive company to show its practicality and accuracy

A Neural Network Decision Support System for Analysing Markets

Today, markets are equipped with IT-based systems to facilitate the flow of information within markets and to provide useful information for producers and costumers. Therefore, real time decision making is a significant issue of IT environment for obtaining maximum profit in markets. A valuable tool for real time decision making are Decision Support Systems (DSSs). Here, we propose a DSS to identify a set of optimal markets for a producer. The producer aims to determine the markets that provide more profit for him via information systems of markets that analyze all transactions and prepare reports. Due to these reports the producer would decide about markets that provide the maximum profit. The effectiveness of the proposed integrated model is illustrated through numerical example

Modelling and Optimization of a Non-Constrained Multi-objective Problem having Multiple Utility Functions using Bayesian Theory

One of the multi-objective optimization methods makes use of the utility function for the objective functions. Utility function creating the most satisfaction answers for decision makers (DMs) by considering the priorities of the DMs; in an available studies; there are only one utility function for each objective function. But due to practical situation in different decision making environments in an industry or trade lead each goal has multiple utility functions. This paper presents a model of multi- objective problem in which each of the objective function has multiple utility function applying Bayesian theory. This model allows DMs to calculate the probability of these utilities using conditional probability in conditions of uncertainty. In addition, examples are given to illustrate the usefulness of this model

Scheduling of Multiple Autonomous Guided Vehicles for an Assembly Line Using Minimum Cost Network Flow

This paper proposed a parallel automated assembly line system to produce multiple products having multiple autonomous guided vehicles (AGVs). Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs. Scheduling of AGVs to service the assembly lines and the corresponding stations are purposed. In the proposed problem the assignment of multiple AGVs to different assembly lines and the stations are performed using minimum-cost network flow (MCF). It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as MCF problem during each short-term scheduling window. <br /

ROUGH MULTI- PERIOD NETWORK DATA ENVELOPMENT ANALYSIS FOR EVALUATION OF SUPPLY CHAIN: A CASE STUDY OF SKILL TRAINING IN IRAN

The existence of a comprehensive and complete model, along with accurate and reliable data, helps to evaluate the performance of the supply chain. Given the different layers and various performances in designing the supply chain, a method that can analyze and evaluate such network structure is required. Moreover, data and conditions’ uncertainty highlight the need for a method that can also include uncertainty in evaluation. In this paper, designing a multi-period network is carried out with rough data to embed in various layers and levels of supply chain. The supply chain performance evaluation is performed using rough network data envelopment analysis. Rough Network Data Envelopment Analysis (RNDEA) is a proper method since it analyzes all the current factors in evaluation; besides, it provides efficiency scores for inefficient decision-making units and boundary forecasting for these units on an efficient border. The study’s outcomes reveal the efficiency of different factors in the designed network. On the other hand, unlike common data envelopment analysis that indicates the maximum of a factor efficiency, the efficiency priority is calculated in the proposed rough network model, and divisional efficiency also is determined in each step

AN INTEGRATED OPTIMIZATION OF PRODUCTION AND PREVENTIVE MAINTENANCE SCHEDULING IN INDUSTRY 4.0

Preventive Maintenance (PM) plays an important role in maximizing machine reliability, improving production efficiency and reducing repair costs. Due to the importance of PM in manufacturing environments, it is necessary to develop an integrated model for scheduling production jobs and maintenance interventions on the machines. On the other side, with the advent of Industry 4.0 and the transformation of factories into smart factories, the production and maintenance processes generate huge volume of data on real-time basis. Despite the importance of the issue and the competitiveness of manufacturing companies in the world, past studies show that the integration of production scheduling and maintenance in Industry 4.0 platform has not been paid much attention. Therefore, in this paper, we propose an optimal parallel machine-scheduling problem with PM activities. A mathematical model is formulated to optimize the scheduling of production and maintenance operations. Industry 4.0 conceptual model is also presented in this paper, in which the smart sensors are considered as the real enablers for industrial digitalization. The optimization problem is solved using GAMS software and branch and bound algorithm. The results of the model provide a suitable schedule for scheduling production and maintenance, and the comparison of solution methods shows that the branch-and-bound algorithm achieves a suitable output in a shorter time