Aggregate planning today

Work Study 04/1995; 44(3):4-7. DOI: 10.1108/00438029510085339

ABSTRACT 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.

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    ABSTRACT: Purpose ‐ The purpose of this paper is to explore different aggregate production planning (APP) strategies (inventory levelling, validation of the workforce and flexible production alternatives: overtime and/or outsourcing) by using a system dynamics model in a two-level, multi-product, multi-period manpower intensive supply chain (SC). Therefore, the appropriateness of using systems dynamics as a research method, by focusing on managerial applications, to analyse APP policies is proven. From the combination of systems dynamics and APP, recommendations and action strategies are considered for each scenario to understand how the system performs and to improve decision making on APP in the SC context. Design/methodology/approach ‐ The research design analyses a typical factory setting with representative parameter settings for five different conventional APP policies ‐ inventory levelling, workforce variation, overtime, outsourcing and a combination of overtime and outsourcing ‐ through deterministic systems dynamics-based simulation. In order to validate the simulation model, the results from published APP models were replicated. Then, optimisation is conducted for this deterministic setting to determine the performance of all these typical policies with optimal parameter settings. Next, a Monte Carlo stochastic simulation is used to assess the robustness of such performances in a variety of demand settings. Different aggregate plans are tested and the effect that events like demand variability and production times have on the SC performance results is analysed. Findings ‐ The results support the assertion that the greater the demand variability, the higher the flexibility costs (overtime, outsourcing, inventory levelling, and contracts and firings). As greater inter-month oscillations appear, which must be covered with additional alternatives, the optimum number of employees must be determined by analysing the interchanges and marginal costs between capacity oversizing costs (wages, idle time, storage) and the costs to undersize it (penalties for lowering safety stocks, delayed demand, greater use of overtime and outsourcing). Accordingly, controlling the times to avoid increased costs and penalties incurred by delayed demand becomes an essential important task, but one that also depends on the characteristics of this variability. Practical implications ‐ This paper has developed a modelling approach for APP in a manpower intensive SC by applying system dynamics. It includes a simulation model, the analysis of several scenarios, the impact on performance caused by variability events in the parameters, and some recommendations and action strategies to be subsequently applied. The modelling methodology proposed can be employed to design-specific models for each SC. Originality/value ‐ This paper proposes an APP system dynamics approach in a two-level, multi-product, multi-period manpower intensive SC for the first time. This model bridges the gap in the literature relating to simulation, specifically system dynamics and its application for APP. The paper also provides a qualitative description of the various pros and cons of each analysed policy and how they can be combined.
    International Journal of Operations & Production Management 07/2014; 34(8). DOI:10.1108/IJOPM-06-2012-0238 · 1.13 Impact Factor
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    ABSTRACT: It is necessary for the management of any industry to workout an intermediate range plan also known as aggregate production plan, consistently with the long range policies and resources allocated by long range decisions. It is a procedure of translating the expected demand and production capacity of the available facilities into future manufacturing plans for a family of products. It includes decisions on production quantity, work force and inventory to workout a low cost product and timely delivery. Ant colony optimization algorithm finds its extensive application in solving job shop scheduling, assignment problems, transportation problems, etc. Genetic algorithms are proposed to solve the problem, already by the authors. In this paper, an attempt is made to solve an aggregate production-planning problem for obtaining an effective solution using ant colony algorithm. Also a hybrid algorithm that combines genetic algorithm and ant colony algorithm is proposed and its effectiveness over the models developed using genetic algorithms and ant colony algorithm is also analyzed.
    Journal of Advanced Manufacturing Systems 11/2011; 04(01). DOI:10.1142/S021968670500059X
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    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.
    IOP Conference Series Materials Science and Engineering 06/2013; 46(1):2015-. DOI:10.1088/1757-899X/46/1/012015


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