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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.

Aggregate planning approaches as presented in the literature are rarely used in practice. The objective of this research is to survey the aggregate planning practice of manufacturing companies in the UK, with the aim of designing more realistic aggregate planning systems. The results gathered provide useful insights into current practice, including the ranking of management decision alternatives, changing functional involvement with key aggregate planning objectives, and views concerning the use of a proposed system of aggregate planning.

This article details how survey evidence of aggregate planning practice can be modelled to develop a functional objective search (FOS) system which incorporates the strategies and multiple objectives of the aggregate planning problem. The system is structured to be interactive in use and capable of providing near optimal, low cost planning decisions which reflect different functional interests. The FOS system is validated using data concerning a paint company taken from the literature. A wider range of aggregate planning variables and issues are also explored during 125 test runs in which the system is used to consider varying functional emphasis, variable worker productivity and profit.

Reports the development and successful implementation of a computer‐integrated production and operations management system (POM), encompassing schedule activities such as aggregate production plan, master production schedule and material requirements plan, and capacity activities such as financial plan, resource requirements plan, rough‐cut capacity plan and capacity requirements plan, at the planning level. POM’s icon‐menu driven system which associates icons with decision model spreadsheets makes it very user‐friendly, and facilitates the integration of decisions encountered by industrial/ manufacturing engineers and operations managers.

The management in non‐profit humanitarian organizations is often preoccupied with its welfare objectives and ignores the operations efficiency and operating cost controls. Proposes an operations‐planning and control framework for small non‐profit humanitarian organizations. The proposed framework integrates several operations management‐planning tools such as time series forecasting, aggregate production planning, ABC analysis, and material requirements planning (MRP) to facilitate better demand and resource management. The purpose of this framework is to provide management with better resource planning and a base of performance evaluation. Using real data, this framework was applied to a non‐profit organization taking into consideration its unique welfare objective. Results indicate that substantial improvements in operations efficiency and cost reduction are possible for small non‐profit organizations through modified operations‐planning and control activities.

Some hierarchical approaches for production planning in a batch type manufacturing environment are described. In a single stage production system, three aggregation levels exist: types, families and items. The performance of a hierarchical system model is largely dependent on the methods of disaggregation at different levels. This paper reports on a study of hierarchical methods at the family disaggregation level and incorporates a simple modification to improve upon a heuristic proposed by Winters. Results indicate that even a very simple hierarchical planning approach can give a significant reduction in backorders in a production shop having severe capacity restriction.

The study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.

This research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.

The findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.

The proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.

An integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.

This article is concerned with four major propositions: 1) The next major thrust in long‐range planning will come in the public sector, and is a thrust long overdue. 2) Over the past two decades we have significantly improved corporate planning in the private sector. We still have much to learn, but compared with the early efforts we are now high on the learning curve. 3) The fundamental lessons we have learned in the private sector in doing comprehensive long‐range planning are applicable to the introduction, development, and success of aggregate integrated long‐range planning in the public sector. 4) It is imperative that a dialogue begin immediately between practitioners of long‐range planning in the private sector and those in the public sector who are directly involved in the development of this process. Both groups have much to learn from each other, but it is particularly important that those in the public sector be exposed to the first‐hand experience of those in the private sector.

Co‐ordination of the activities of production units is necessary to realise the required delivery performance in the market. These should not conflict with reaching the production economics objectives of each of the units. Production structure is needed to reduce the complexity and should minimise the loss of potential flexibility. Any structure will have some elements in common — the definition of basic elements (e.g. capacities) as a first step in production control structure design; the introduction of product units and the decomposition of the total production control to Goods Flow Control and Production Unit Control; the relationship of sales and manufacturing and the interference of products and capacities as two main determining factors of the Goods Flow Control structure. The generality of these elements means it is possible to develop a small but relatively complete set of reference structures. A reference structure for Goods Flow Control in a repetitive manufacturing situation is discussed. Its main elements are master planning, material co‐ordination, workload control and work order release.

Five hundred financial executives from North American companies were surveyed by means of a mailed questionnaire to gain a view from outside the operations functions of the basis on which aggregate inventory decisions are taken. The response indicated that more functions than might have been expected were involved in the process of determining inventory levels and, partly because of this, policy tended to be of a shorter rather than longer term nature.