Recent market conditions require flexible or higher speed for products to reach markets. This speed is limited by a bottleneck under market/production capacity, and this problem is known as the theory of constraints (TOC) in the supply chain management (SCM). The model here proposed originates in a paper by Matsui (1983, 1988), and has quite a distinctive origin in comparison to the bottleneck theory in TOC. This paper presents a new management model of enterprises (MGM), gives a design or decision-making method for management, and discusses the similarities of the model in comparison with the theory of constraints (TOC). First, the MGM model is presented as the pair-type cooperative vs. non-cooperative game model of maximizing the throughput, i.e., the marginal profit between the sales price and the operating cost per unit time, under a bottleneck dependent to demand (market)/supply (production). Next, the 2-stage design method proposed determines the economic traffic of market/production in the first stage (economics), and sets the economic leadtime/pricing and constraints under the economic traffic in the second stage (reliability). Finally, a special example of MGM is given and considered under a M/M/1 enterprise model, a variety of management solutions/designs is discussed on a pair-matrix table, and a comparison to bottleneck solutions is noted.

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Inventory management is ultimately concerned with when and how much to order in response to some uncertain information on demand and product. For example, Cheung and Zhang categorized customer order cancellation as uncertain information and studied the impact of customer order cancellation on a periodic review (s,S) inventory model. On the other hand, the presence of defects in products is also one of uncertain information having an influence on the inventory policy. Defective products in inventories brings not only unnecessary inventory cost, but also the defective products being passed on to the customer results in a loss. Therefore, defective products should be removed through inspection in order to avoid unnecessary cost and loss. At the same time, a shortage of inventory may be caused by removing defective products. Then, the purpose of this article is to consider the (s,S) inventory policy under uncertain information as the presence of defective products in addition to the customer order cancellation studied by Cheung and Zhang. We define the total cost function for the (s,S) inventory model and consider the optimal (s,S) inventory policy under the defined total cost function through some numerical examples.

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Accelerated life tests are frequency used to obtain information on life distribution of products quickly. The products can be submitted to higher level of stress in various ways such as constant, step, and progress stress. In a constant-stress test, products are run at some constant level of stress until all products fail or a time limit is reached. A step-stress test allows the stress setting to be changed at a prescribed time or upon the occurrence of a fixed number of failures. In a progress-stress test, the stress is continuously increased over time. Under the assumption of the functional relationship between the parameters of failure distribution and applied stress, there are many studies about parameter estimation using data obtained from an accelerated life test. In this paper, instead of the parameter estimate problem, we consider a new design procedure of simple-step-stress accelerated life tests for assuring the mean time to failure at usual stress with specified producer and consumer risks under the condition that the parameters of the failure distributions at two stress levels are provided. The practical use of the proposed design procedure is illustrated through some numerical examples.

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This paper focuses on the importance of decision making in Research and Development Investment, which is one of the major concerns in Technology Management. The study intends to construct a model relative to the decision support of the total amount of Research and Development Investment and distributions for different research activities. For this objective, the paper attempts to build a Techno-Dynamics Model which explains the process by which various research efforts bring forth knowledge and techniques. It is likewise interested in exploring the research spiral of the study outcomes that would result. The Techno-Dynamics Model that is essential for this paper is based on a Stock of Technological Knowledge. The paper tries to describe the research spiral model that is inextricable from the degree of knowledge, technology, product development, production, and finally volume of sales. In this paper, these layers are defined as "stock of basic research, ""stock of applied research, ""stock of development research, ""operating, " and "sales." They reflect an accumulation of technological knowledge, technological transfer, obsolescence, technological development, and a time lag of production. This paper takes these factors into account and hopes to structure a multi-layered model. The final result will hopefully have a utility value and may be put into operation in a real marketing model for a private enterprise.

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This paper proposes a heuristic method to minimize makespan in a machining-assembly flowshop where there are two parallel flow lines with two machines per line at a machining stage and one robot at an assembly stage. Each job has two major component-parts to be processed in parallel at the machining stage, and a flow line to be used for machining each part is specified individually in advance. No assembly operation of these parts for any job involving various other parts supplied independently by some contracting companies can start until all the related component-parts have been completed at the machining stage. This scheduling problem is NP-complete and an optimal schedule for this problem is not always a permutation schedule. In the proposed method, an NEH-based heuristic algorithm first provides a set of promising permutation schedules, and then a Johnson-rule-based heuristic algorithm generates a non-permutation schedule for each of the five best permutation schedules. To avoid falling into a local optimal, the Johnson-rule-based heuristic algorithm allows that the performance of schedule becomes worse than that of the previous one in its searching process. The performance of the proposed method is compared with a branch and bound algorithm with one hour limitation in computation time. Numerical experiments to solve 100 instances generated randomly for each problem with at most 50 jobs show that the proposed heuristic algorithm can provide a near-optimal non-permutation schedule efficiently with high accuracy such as mean relative error being less than 0.3% and the maximum relative error being less than 3%.

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The practicality in recent process synthesis and process optimization of highly reliable systems leads to redundancy allocation problems and reliability assignment/redundant allocation problems. These optimization problems will be formulated as nonlinear integer programming (nIP) and nonlinear mixed integer programming (nMIP) models. In such reliability optimization, numerous exact methods and approximate methods have been proposed for the past two decades. However, they involve more computation efforts and usually require larger computer memory so that many researchers have placed more emphasis on the heuristic methods. From this viewpoint, genetic algorithm (GA) as one of the meta-heuristic methods, has been receiving much attention in this field. Unfortunately, GA cannot guarantee the optimal solution because of the fundamental requirements of not using a priori knowledge and not exploiting local search information. In order to overcome this kind of problem, in this paper, we propose a hybrid genetic algorithm combined with a fuzzy logic controller (FLC) and local search method. When applying the proposed method for the nMIP model, we also combine a neural network (NN) technique for devising of initial values for the GA. The efficacy and efficiency of the proposed algorithm is demonstrated by applying it to reliability optimization design problems of large-scale systems and by comparing its results with those of other traditional methods.

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Team management on flat organization has been studied and has increasingly reconfirmed the importance of teamwork recently. A quantification of teamwork has already been devised and a relationship between teamwork scores and productivity has been shown. Those surveys were based on Group Dynamics and introduced activity in the manufacturing plants to bottom line workers and staff members. This paper analyzes the relationship between teamwork scores and team achievement in a management simulation before real activity will be tested in the industrial world. Moreover, achievement of all team was decided and arranged in order for every term according to various financial indices (Return on Investments : ROI, Turnover Rate of Assets : TOA, Ratio of Profit to Net Sales : ROS). Cumulative scores decided the final order of each team in the total management activity throughout approximately ten years. This research used and considered five years' data for management simulation from 1994 to 1998. The total number of teams was 100, and the number of student answers was 493. To confirm a clearer difference, this paper extracted and evaluated some teams from all with Good-Poor Analysis. They are the four most significant groups and the four lowest ranking groups each year. The high achievement groups numbered 20 teams and student answers, 102. Moreover, there were 20 teams and 96 answers in the low achievement groups are over the five years. In conclusion, the following hypotheses were adopted and became the indicator of real management activity. Hypothesis : High achievement group's awareness of teamwork is higher than that of the low achievement group (average value). Hypothesis : High achievement groups have divergence-stratified type and low achievement groups have factional type of organization among team members (cluster analysis).

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The significance of service industries in the global economy has increased in recent years and hence there is substantial interest in service marketing. Furthermore, the importance of customer loyalty is noted, particularly in mature markets. This paper presents the construction of structural models to investigate the conceptualization of service quality and the relationship between service quality, customer satisfaction and loyalty in the fast-food industry. Questionnaire surveys are performed for two fast-food chains : McDonald's and Mos Burger. First, factor analysis is performed to construct models for the evaluation of perceived service quality. Five- and six-factor structures are selected for Mos Burger and McDonald's, respectively. It is shown that the "Reliability in Commodity" factor has the greatest effect on perceived service quality in McDonald's, and the "Original Taste" factor has the greatest effect in Mos Burger. Structural models are estimated and the significance of relationships between service quality, customer satisfaction, loyalty and other factors (i.e., switching cost and reputation) is examined. The results suggest that (1) customer satisfaction has a significant effect on loyalty and service quality has a significant effect on customer satisfaction in both chains, (2) service quality and switching cost have a significant effect on loyalty in the case of McDonald's, (3) reputation has a significant effect on loyalty in the case of Mos Burger. The implications of the results are discussed and topics for future research are suggested.

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