For any industry or company, to be a competent in market needs increase in performance in all possible ways. Quick response to market and sufficient production as per requirement is the only way to increase the returns, which require critical planning and production systems. For this co-ordination between business units or workstations is essential. Many of the executive managers in industries has to instruct only the task flows to its subordinates, that is a single straightforward production planning process is followed as executed from top level of organization; their capacity as per their education level is not utilized more than 10%. The use of multi-agent system allows physical distribution of the decisional system and procures a hierarchical organization structure with decentralized control that guarantees the autonomy of each entity and flexibility of network. Our study focuses on managers/partners that adapt together their local planning process to face different requirements of supply chain environment using different planning strategies, when decisions are supported by distributed planning systems. The agent based system has the advantage of making collaborative management of disturbances in supply chain as the agents has the advantage of making autonomous decisions in a distributed network. Because each partner can choose different behavior and all behavior has an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Thus, in this paper study of Multi-behavior planning agent model is done using different planning strategies when decisions are supported by distributed planning system. Keywords: Multi-agent, agent-based planning systems, collaborative supply chain planning. I. INTRODUCTIONThe bearing industry is highly distributed, with many production units interacting in all activity levels. Traditionally, companies used to view themselves as separate entities and did not devote efforts to collaborate with other echelons of the extended enterprise. The main problems of this industry lies in the large amount of stochastic disturbances in many aspects of the supply chain, mainly due to the highly heterogeneous aspect of the resource, uncertain process output, production of co-products and by-products, price variation in the spot market, and demand variation in commodity markets. Collaboration among entities in the supply chain can have a great impact on the system performance. The various OR techniques were applied to improve planning, coordinating, transporting between the manufacturing units and workstations within units. But all are centralized controlled and this may result in much of the system being shut down by a single point of failure, as well as plan fragility and increased response overheads. Agent technology provides a natural way to address such problems, and to design and implement efficient distributed intelligent manufacturing systems. Agent technology has been recognized as a promising paradigm for next g...

A forecast is an estimate of the level of demand to be expected for the particular product or several products for some period of time in the future. It can be said that forecast is an educated guess; certainly is should contain as little error as is humanly possible. To make a forecast more meaningful, it should be in turns of units to be planned or scheduled, and it should cover a time period at least as long as the period of time required to make a decision and to put that decision into effect .The nature of product and its demand pattern affect the type of forecast to be made and the time period which must be covered or spanned; like demand may be constant, may have cyclic variations, long term upward trend etc.. Generally we find that forecast can be done by subjective opinion, market research, using certain index or can be computed using averages or some combination methods. Most of the forecaster assumes that use of statistical methods applied to the past data is a realistic way of forecasting future demands. New researchers use the bass model and other life cycle assessment models of the product to forecast the demand but these require a similar product life cycle to compute certain constants which again weaken the forecast. We think that the possibility of getting the almost exact forecast depends on the process (may be statistical or any) that are used to forecast, and these processes are selected by the forecaster; this means that these all forecasts basically dependant on the experiences of the forecaster and the knowledge regarding the trend of the market, and also the ability of the forecaster to predict the remaining life cycle of the product. This is the basic region that we get the values which, many times, are far from actual one. Considering the requirements of forecasts i.e. a huge knowledge about the product whose demand is to be forecasted and certainty of irrelevance or missing data, the author experiments on a new tradition of forecasting and uses an artificial neural network technology to forecast the future demand and represent some of them in this paper. This technology uses mathematical formulations to model nervous system operations and is used to learn pattern and relationships in data. There is verity of software available to model the neural network but among those the author fined more convenient software which is a add-ins of MS EXEL known as Neuro XL Predictor which is very simple to use as it is a part of Microsoft Excel spreadsheets.