Aggregate planning today

ArticleinWork Study 44(3):4-7 · May 1995with60 Reads
DOI: 10.1108/00438029510085339
A firm must plan its manufacturing activities at a variety of levels and operate these as a system. Aggregate planning is medium-range capacity planning which typically covers a time horizon of anywhere from three to 18 months. The goal of aggregate planning is to achieve a production plan which will effectively utilize the organization's resources to satisfy expected demand. Planners must make decisions on output rates, employment levels and changes, inventory levels and changes, back orders, and subcontracting. Aggregate planning determines not only the output levels planned but also the appropriate resource input mix to be used.
    • "In this planning step, aggregate data (time buckets, product families, resources, etc.) is necessary in order limit the complexity to a reasonable level. It typically covers the manufacturing activities for a horizon between 3 and 18 months with the goal to achieve a production plan which will effectively utilize the organization's resources and to satisfy expected demand (Pan and Kleiner, 1995). Aggregate plans are supposed to be updated periodically and determine the requirements for master production scheduling (MPS). "
    [Show abstract] [Hide abstract] ABSTRACT: In hierarchical production planning (HPP) systems, aggregate production planning (APP) is meant to balance capacity requirements and production quantities for medium term planning horizons. Aggregate plans provide the basic input for further planning steps. In recent years researchers came up with comprehensive models and sophisticated solution methods for this kind of high level planning. However, some practitioners claim that the aggregate planning concept is rarely applied in industry. We present a comprehensive HPP framework, which we use to investigate the impact of aggregate planning in a make-to-order (MTO) environment. The basic inputs for aggregate plans are market forecasts. Thus, we conduct experiments assuming different forecasting techniques. The planning problem is formulated as a linear mathematical model and solved to optimality by a standard optimization engine. A discrete-event simulation model is used to perform lower level planning steps and to mimic the shop floor where stochastic and nonlinear dependencies are considered. The performance of the system is evaluated based on service- and inventory levels. Real world data coming from the automotive supplier industry is used to define four demand scenarios. For each of them we compare the performance of the system with and without the inclusion of aggregate plans. If aggregate plans are used we assess the impact of different forecasting techniques. We analyze the results and give managerial recommendations. Our experiments show, for example, that in a setting of low capacity utilization, aggregate plans are rather unprofitable. Moreover, we observe that APP using time series forecasting techniques seems to be a good strategy if demand is highly volatile or resources are scarce.
    Article · Jun 2015
    • "In 1992 Nam and Ogendar [6] conducted a survey of APP techniques and categorized them into these classes: trial and error method, graphical technique, mathematical technique, linear decision rule, search decision rule, management coefficients method, parametric production planning, production switching heuristic, linear programming, goal programming, mixed integer programming, transportation method and simulation model. Three years later, Pan and Kleiner [7], categorized available models into seven classes including informal approach, mathematical model, linear programming model, linear decision rule [8], [9], heuristic technique, the management coefficients model and search procedure using computer simulation. However, another classification of recently used techniques was given in 2004 by Reay-Chen and Tien-Fu [10]. "
    [Show abstract] [Hide abstract] ABSTRACT: Aggregate Production Planning (APP) is a medium-term planning which is concerned with the lowest-cost method of production planning to meet customers' requirements and to satisfy fluctuating demand over a planning time horizon. APP problem has been studied widely since it was introduced and formulated in 1950s. However, in several conducted studies in the APP area, most of the researchers have concentrated on some common objectives such as minimization of cost, fluctuation in the number of workers, and inventory level. Specifically, maintaining quality at the desirable level as an objective while minimizing cost has not been considered in previous studies. In this study, an attempt has been made to develop a multi-objective mixed integer linear programming model that serves those companies aiming to incur the minimum level of operational cost while maintaining quality at an acceptable level. In order to obtain the solution to the multi-objective model, the Fuzzy Goal Programming approach and max-min operator of Bellman-Zadeh were applied to the model. At the final step, IBM ILOG CPLEX Optimization Studio software was used to obtain the experimental results based on the data collected from an automotive parts manufacturing company. The results show that incorporating quality in the model imposes some costs, however a trade-off should be done between the cost resulting from producing products with higher quality and the cost that the firm may incur due to customer dissatisfaction and sale losses.
    Full-text · Article · Jun 2013
    • "Pega et al. [15] developed an integrated approach to address the aggregate planning problem and applied it to a firm, which yielded significant savings in the operational costs of the firm. In addition, excellent surveys of APP models can be found in Nam and Logendran [16], and Pan and Kleiner [17]. The approach presented in this study has to do with multiple criteria mathematical programming, which so far appears to have received less than sufficient research attention. "
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, we present an aggregate production planning (APP) model applied to a Portuguese firm that produces construction materials. A multiple criteria mixed integer linear programming (MCMILP) model is developed with the following performance criteria: (1) maximize profit, (2) minimize late orders, and (3) minimize work force level changes. It includes certain operational features such as partial inflexibility of the work force, legal restrictions on workload, work force size (workers to be hired and downsized), workers in training, and production and inventory capacity. The purpose is to determine the number of workers for each worker type, the number of overtime hours, the inventory level for each product category, and the level of subcontracting in order to meet the forecasted demand for a planning period of 12 months. Additionally, a decision support system (DSS) based on the MCMILP model is proposed. It will help practitioners find the “best” solution for an APP problem without having to familiarize themselves with the mathematical complexities associated with the model. An example to illustrate the use of the DSS is also included.
    Full-text · Article · Apr 2006
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