Shigeji Miyazaki

A hybrid statistical genetic-based demand forecasting expert system

Demand forecasting is considered a key factor for balancing risk of over-stocking and out-of-stock. It is the main input to supply chain processes affecting their performance. Even with much effort and funds spent to improve supply chain processes, they still lack reliability and efficiency if the demand forecast accuracy is poor. This paper presents a proposal of an integrated model of statistical methods and improved genetic algorithm to generate better demand forecast accuracy. An improved genetic algorithm is used to choose the best weights among the statistical methods and to optimize the forecasted activities combinations that maximize profit. A case study is presented using different product types. And, a comparison is conducted between results obtained from the proposed model and from traditional statistical methods, which demonstrates improved forecast accuracy using the proposed model for all time series types.

Time-based goal chasing method for mixed-model assembly line problem with multiple work stations

In sequencing problems for mixed-model assembly line in JIT production system, the Goal Chasing method (GC) is widely used for parts used leveling goal. The difference in assembly time of each product is not taken into consideration in the Goal Chasing method. Assembly time usually varies with product types. In recent years, the Time-Based Goal Chasing method (TBGC) has been proposed. The advantage of TBGC is to consider the influence of different assembly time of each product and idle time in production period. TBGC, however, has been only applied to single work station problems. In this paper, TBGC is applied to an assembly line problem with multiple work stations. Furthermore, the sequencing method and use of Simulated Annealing (SA) or Local Search (LS) for this problem are proposed.

An analytical comparison of inventory costs between the pull and the parts-oriented production systems

Abstract This paper presents nomographs for evaluating the relative size of parts inventory costs yielded by the pull system and the parts-oriented system for the multi-stage production. The parts-oriented system indicates the less inventory costs under the larger variation of parts demand, the larger production stages and the higher safety stock level. The merit of pull system increases as the variation of parts demand and the safety stock level decreases, respectively, which have been treated as management goals of Toyota Production System (a typical example of the pull system).

The optimal operation planning of a Kanban system with variable lead times

Abstract In the majority of the previous research on Kanban systems delivery lead time is treated as a fixed value. In many practical situations, however, the lead time is variable. In this paper, optimal operation planning of a fixed interval withdrawal Kanban with variable lead times is proposed. A cost is incurred for inventory level less than the safety inventory level, since the risk of shortage of inventory must be considered. Behaviour of the optimal number of Kanbans and the withdrawal interval of Kanbans are investigated in terms of various parameters such as standard deviation and mean lead time.

Batch-scheduling problems to minimize actual flow times of parts through the shop under JIT environment

Abstract We propose a performance measure called actual flow time for scheduling problems under JIT environment requiring that input material should arrive in the shop at the right times in the right quantities and that the completed parts should be delivered at their due dates. We also address batch-scheduling problems, i.e., to determine batch sizes and to schedule the resulting batches so as to minimize the actual flow times of parts through the shop. Optimal algorithms solving the problems on a single machine and heterogeneous machines with a common due date are developed, and an algorithm taking advantage of the optimal algorithms is proposed for solving the problems with multi-due-date. Proof of the optimality is not given for the later algorithm but numerical experience shows that it is very efficient. Numerical experience that shows the characteristics of the problems is also presented.

Sequencing method for products in consideration of assembly time

In mixed-model assembly scheduling with a single workstation, there is a new sequencing method [time-based goal chasing method (TBGC)] in consideration of the different assembly times between jobs. In this paper, we propose a new objective function in expanding the TBGC. Furthermore, we propose some heuristic methods to minimize the objective function. And through numerical experiments, we show the effectiveness of our proposal.

An optimal operation planning for the fixed quantity withdrawal Kanban system with variable leadtimes

Abstract This paper deals with an optimal operation planning for the fixed quantity withdrawal Kanban system with variable leadtimes and different consumption rates of parts for each production term. The behaviour of the optimal operation planning which minimizes the average total operation cost is shown by means of simulation analysis of various values of parameters: the order cost factor, the range of consumption rates of parts and the range of leadtimes for delivery.

Scheduling in an automated flow shop to minimize cost: Backward-meta scheduling method

Abstract Recently, computer integrated manufacturing (CIM) has become the most practical production system. Nevertheless, some problems appear in the stage of scheduling that are effected by the complexity of the system. Especially, CIM is classified to be an on-line system that has to decide the production schedule within a very short period. Nowadays, among the applied scheduling rules in CIMs, meta scheduling methods such as GA and SA have been widely used. In this paper, we have applied some meta scheduling methods to a model of CIM that is referred to as an automated flow shop, where backward scheduling should be used to realize a JITs theory. For the objective function, we have intended to minimize the total cost calculated through the production schedule of orders. Scheduling methods have been constructed and tested in the scheduling model by conducting the simulation test. Finally, results of the simulation test have been compared to find the performance of the proposed method.

Design of Demand Forecasting Expert System for Dynamic Supply Chains

When distributors and wholesalers seek help with issues relating to inventory management, they are usually concerned about an increasing level of out-of-stocks or over stocking. Out of stocks are leading to sales loss and customer service complaints. Over-stocks are resulting in slow inventory turnover and a buildup of dead inventory. In fact, out-of-stocks and overstocks are actually a flip side of the same inventory management coin. Any effective initiative to resolve these issues must address core structural causes of these inventory management problems. Superior inventory management begins with timely, accurate, detailed demand forecasts. Over last decade demand forecasting has played a prominent role in the corporations worldwide. Corporate executives have spent millions of dollars and invested thousands of man-hours trying to improve methods used & complicate it more. In each case little attention was paid to the integration between drivers, inputs and demand forecast (Harrison & Qizhong, 1993). In the face of all these advancements in hardware and software forecast error still remain high. The inaccuracy in the forecast is due to previous researchers focused on statistical methods and their improvements only. There was no effort on the modeling of the problem and how to build an expert system to interact properly with the dynamic changes of the supply chain (Ajoy & Dobrivoje, 2005). The forecasting model is not treated as enterprise system has its specifications and constraints which are modeled and simulated. In this research we propose a design of expert demand forecast system which is designed after deep understanding of demand cycle within dynamic supply chain and interaction between different parameters within the supply chain. It is utilizing Bayesian vector auto regression, restricted vector auto regression, and kernel fisher discriminant analysis (Scholkopf & Smola, 1998), (Scholkopf et al., 1999) with improved genetic algorithm to filter, analyze inputs and factors affecting demand along with demand history and then generate baseline and operational forecasts. This model proposes new mathematical and expert modeling methodology to generate forecasts. We used a practical case study from international FMCG (Fast Moving Consumer Goods) industry using over 1000 product types and results show that a significant forecast accuracy and other supply chain key performance indicators improvements over one year months rolling. The proposed model is composed of the integration between statistical and intelligent methods with expert input to generate more accurate demand forecasts. The inputs to the Source: Expert Systems, Book edited by: Petrică Vizureanu, ISBN 978-953-307-032-2, pp. 238, January 2010, INTECH, Croatia, downloaded from SCIYO.COM

Multiple Objectives Scheduling with JIT Theory in a Flow Shop FMS.

In an FMS, a most complicated problem to be solved may be the scheduling problem which is flexible due to the complexity of the system. FMS needs very high cost for installation, therefore in order to get high efficiency, the most appropriate schedule (in general means the schedule with the minimum cost) should be obtained in the stage of scheduling. In this article an FMS model which is a flow shop-type production system has been engaged. Scheduling method is based on the multiple objectives and the total objective function is to minimize the value of all concerned objectives. Consequently, scheduling algorithm has been composed by applying the backward scheduling with Simulated Annealing (SA) method to realize JIT theory. Finally, numerical example has been conducted to show how the scheduling algorithm works and especially to find the efficiency of the proposed algorithm. According to the numerical example, it is shown that the algorithm works well when it was applied with the concerned FMS system.