論文

Measuring project success is a challenging issue among researchers and practitioners, because of its multidimensional properties. During the last three decades, numerous researchers have tried to define and illustrate its dimensions. But still there is no general agreement about it. To measure and predict success of a project, we need to take into account all project stages including utilization stage of the product of a project in an integrated approach. In this paper we try to introduce a novel model to measure project success considering selection, execution and utilization stages. The model is based on the deviations from the goal of each stages. And the amount of success in this model which we call the "Circles Model", is the magnitude of proximity to the final goal. We apply it to an example and measure its success quantitatively. Lastly we discuss four extensible directions for future studies to apply the model on high performance projects, multi stakeholders space, program and portfolio domain and finally to explore the inŽuence of the performance of each stage on the performance of the next stages.

Project success is a vague concept among researchers and practitioners. Many researchers have tried to explain when a project could be called successful, but there is no agreement. The reason is existence of different stakeholders and viewpoints in a project. In this article we aim to make a comparative framework to compare different viewpoints and discover intersections and variances in the concept of project success to decrease these diversities. We review 173 selected papers and make an integrated framework containing nine different viewpoints that could be used as a raw dataset to explore trends in the literature and hidden aspects of project success. Analyzing statistics of this framework we found that: 1) Although the focus on stakeholders satisfaction are increasing but still cost, time and quality is dominating success criteria for evaluating project success. And also we categorized nine success criteria to three levels of priority i.e. high, medium and low priority, based on the frequency of appearance in the papers. Then we found some corelationships between nine groups of criteria using Association Rules method. 2) During 80s most papers deal with cost,time and quality (project management success), and in 90s it expanded to customer satisfaction (product success), then currently, it expanded to multiple project evaluation (program success). We also found that there are still some forgotten parts of project success and we argue that one of the most important hidden aspects is "Project Selection Success". It should be considered because it defines project success at its origin. Furthermore its inŽuence on the project management and total project success is not clearly defined in the literature.

Freshness of agricultural products is very important for health of customers and profit of farmers. In this paper,we deal with vegetable supply chain including production activities such as planting, mowing, fertilization, harvesting and distribution activities such as transportation from farm to distribution center, distribution center to retailer, restaurants,and consumers. There are hundreds of tons waste throughout the supply chain including consumption every year, and it becomes big social problem in Japan. In this paper, aiming to reduce waste of vegetables and other fresh agricultural products, we develop a visualization system of vegetables where those production activities are recorded using sensing technology, and demonstrated to buyers ahead of bidding in the distribution center. Since those production activities were carried out according to GAP (good agricultural practices) standards, buyers may correctly determine quality of the agricultural product ahead of harvest and put there bidding price before harvest. This is an assumption in this paper, which means that agricultural product could be harvested after demand is determined, or the harvesting activities performed for the fixed order, i.e., harvest to order (HTO). Through POC (proof of concept) of the system, we found that the visualization system can contribute to reduction of waste more than 15% same time it can generate GAP report automatically whenever it necessary according to the recorded data.

Purpose: This paper considers an option contract in a two-stage supplier-retailer supply chain (SC) when market demand is stochastic. The problem is a Stackelberg game with the supplier as a leader. This research assumes demand information sharing. The purpose of this study is to determine the optimal pricing strategy of the supplier along with the optimal order strategy of the retailer in three option contract cases. Design/methodology/approach: The paper model the option contract pricing problem as a bilevel problem. The problem is then solved using bilevel programing methods. After computing, the generated outcomes are compared to a benchmark (wholesale price contract) to evaluate the contract. Findings: The results reveal that only one of the contract cases can arbitrarily allocate the SC profit. In both other cases, the Stackelberg supplier manages to earn the total SC profit. Further analysis of the first contract, show that from the supplier’s perspective, the first stage forecast inaccuracy is beneficial, whereas the demand uncertainty in the second stage is detrimental. This contracting strategy guarantees both players better outcomes compared to the wholesale price contract. Originality/value: To the best of the authors’ knowledge, this research is the first that links the option contract literature to the bilevel programing literature. It also the first to solve the pricing problem of the commitment option contract with demand update where the retailer exercises the option before knowing the exact demand.

This paper considers an option contract in a two-stage two suppliers - one buyer supply chain (SC) when market demand is stochastic. The suppliers compete by offering a commitment option contract to the buyer. The contract is composed of three prices. It allows the buyer to purchase a firm quantity and reserve options before knowing the exact market demand. After realizing the exact demand, the buyer can choose the number of options to exercise. The aim of this paper is to determine an optimal ordering and selection strategy for the buyer. It also determines an optimal bidding strategy for the suppliers. Furthermore, this contract will be compared to a pure option contract. These contracts are justified through numerical examination, and it was found that buyers prefer the lower-cost products as as to satisfy the demand with a higher realization probability. Furthermore, it was found that in equilibrium, local suppliers earn profit from the exercised options, whereas overseas suppliers earn profit from the reserved options. The paper also shows that the commitment option price provides suppliers with a greater profit in comparison to a pure option contract under the same conditions.

Platform-based product development has been regarded as a cost-efficient approach to handle wide product variety. The key to successful platform-based product development is finding the best combination of the platform configuration and modular design. Most research papers have focused on product platform configuration with a given module set without considering operation costs including the design cost, procurement costs and inventory costs. A new model is proposed in order to determine the optimal product platform configuration for a given external product family considering the vertical and horizontal selection of a module with multiple options and operation costs. The demand for the external product family is unknown, and normal distribution has been assumed. The module acquisition lead time varies along with the module acquisition amount and the transportation time. A hybrid methodology combining a simulated annealing and VNS (variable neighborhood search) is proposed and applied to solve the proposed model. Numerical examination was carried out for the purpose of evaluation. The results show that the proposed model can provide an optimal solution. This research makes two main contributions: the vertical and horizontal selection process of module options and the consideration of operation cost of module acquisition in product platform configuration.

Production line design is one of the important research topics for constructing smart factories. Many research papers have been published concerning smart factories; however, most of them are focus on subjects such as automation. IoT (Internet of Things), AI (Artificial Intelligence) and other new technologies that are introduced to increase the efficiency of existing manufacturing lines based on the so-called philosophy of " status-quo improvement " . In this research, we consider a design problem for a new production line taking into consideration investment cost, operation cost, synchronization penalty and flexibility penalty. We emphasize synchronization and flexibility as new key performance indices to evaluate the level of intelligenced to the smart factory, in which a multiple number of equipment in two production processes can provide the proper number of combinations to satisfy the external demand remove. Based on a real case involving a major player in the dairy product processing industry in Japan, we focus on two core production processes in a processed cheese manufacturing line; melting process and filling process. We design the process to include investing in a multiple number of machines with different capacities, while minimizing the total cost giving consideration to investment, operations, synchronization and flexibility. Two types of decision variables are introduced representing investment and operation. Through numerical experiments and the case study, we show that the proper number of equipment and capacity can be obtained applying the proposed model. The proposed model can also be applied to those companies considering further investment in factory or production line.

Safety stock allocation problems including uncertainty in the supply-side have forced to assume a speci?c distribution of the lead time yet this is usually not reliable. From a guaranteed-service approach, a new model based on robust optimization in which no distribution is assumed is proposed. The robust model is a mixed-integer linear program. Then, the model is validated and applied to several complex real-case supply chains. Preliminary results are promising and show how the model could be a powerful tool for practitioners to manage risks from the supply-side in a robust yet efficient way.