Linking aggregate production planning with marketing pricing decisions

PurposeThe 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.Design/methodology/approachThis 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.FindingsThe 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.Research limitations/implicationsThe 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.Originality/valueAn 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.

Purpose – The purpose of this paper is to study the impact of learning effect and demand uncertainty on aggregate production planning (APP), provide practitioners with some important managerial implications for improving production planning and productivity. Design/methodology/approach – Motivated by the background of one labour-intensive manufacturing firm – a mosquito expellant factory – an APP model considering workforce learning effect and demand uncertainty is established. Numerical example and comparison with other two models without considering learning and uncertainty of demand are conducted. Findings – The result shows that taking into account the uncertain demand and learning effect can reduce total production cost and increase flexibility of APP. Practical implications – Managerial implications are provided for practitioners with four propositions on improving workforce learning effect, i.e. emphasizing employee training, combing individual and organizational learning and reduction of forgetting effect. Originality/value – This paper has practice value in improving APP in labor-intensive manufacturing.

Production planning is one of the most important functions in the process of production management. Production planning in the intermediate range of time is termed as “aggregate production planning (APP)”. Aggregate production planning is an important upper level planning activity in a production management system. The present study tries to suggest an aggregate production planning model for products of Hafez tile factory during one year. Due to this fact that the director of the company seeks 3 main objectives to determine the optimal production rate, the linear goal planning method was employed. After solving the problem, in order to examine the efficiency and the distinctiveness of this method in compare to linear programming, the problem was modeled just by considering one objective then was solved by linear programming approach. The findings revealed the goal programming with multi objectives resulted more appropriate solution rather than linear programming with just one objective.

Production planning in practice

Hierarchical model for production planning in the case of uncertain demand

Application of Aggregate Production Planning (APP) models has not received due attention from industries in developing countries (DCs). However, with the development of Information Technology in DCs, the application of such models has become significant for improving productivity. Realising the importance of a systematic management in DCs, an attempt has been made in this paper to discuss the application of APP models in DCs. The main objective of APP is to identify the optimum level of work force and production speed requirements to cushion the effect of fluctuations in demand. Many researchers, in order to estimate the work force size and production rate by minimising the total production cost, have followed different types of approaches. The APP models discussed here are for validating their applications in developing countries like India, Malaysia, Mexico and Brazil. A detailed analysis has been reported in this paper on the applicability of the APP models for industries in DCs. The suggestions for mo...

This study aims to provide Decision Makers (DMs) with a framework for achieving long-term development of Aggregate Production Planning (APP) in a Supply Chain (SC).The contribution of this study is to consider both Proportional Fairness (PF) and robustness in the APP optimization processes, recognizing that overlooking fairness among the multi-objectives in APP may result in inequitable considerations due to different priorities.Neglecting robustness may yield unreliable and non-resilient outcomes in APP, particularly in uncertain situations where uncertain and vague information poses a challenge.To address these concerns, a unified proportional fairness and robustness optimization model is proposed by applying the principles of PF and Robust Chance-Constrained Programming (RCCP) to the conventional weightless objective optimization approach.The effectiveness of this approach is demonstrated through a case study of an APP problem in a SC with the objectives of minimizing total costs, minimizing fluctuations in workforce levels, and maximizing total values of purchasing under uncertain environments.The comparative analysis indicates that the outcome derived from the proposed approach outperforms the results of both traditional weightless and fairness approaches, particularly in enhancing fairness and robustness in the APP.

In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, the aggregate production planning sometimes becomes extremely complex in dealing with all relevant cost criteria. Most of the existing approaches have focused on minimizing only production related costs, consequently ignored other cost factors, for instance, supply chain related costs. However, these types of other cost factors are greatly affected by aggregate production planning and its mismanagement often results in increased overall costs of the business enterprises. Therefore, the proposed model has attempted to incorporate all the relevant cost factors into the optimization model which are directly or indirectly affected by the aggregate production planning. In addition, the considered supply chain related costs have been segregated into two major categories. While the raw material purchasing, ordering, and inventory costs have been grouped into an upstream category, finished goods inventory, and delivery costs in the downstream category. The most notable differences with the other existing models of aggregate production planning are in the consideration of the cost factors and formulation process in the mathematical model. A real-life industrial case problem is formulated and solved by using a genetic algorithm to demonstrate the applicability and feasibility of the proposed model. The results indicate that the proposed model is capable of solving any type of aggregate production planning efficiently and effectively.

ABSTRACT: This paper presents a multi-stage stochastic model to address an aggregate production planning problem. The proposed stochastic model includes two random variables, namely, random demand and random quality, where the production system generates a random amount of defectives. The main contribution of this paper is to extend the traditional deterministic production planning model to take into account the impact of uncertainty, which yields to the development of an integrated stochastic model that jointly optimizes production management decisions considering quality concerns under a certain service level. The performance of the proposed stochastic model is analyzed to draw relevant managerial insights regarding the effects of such a service level target. Also, a sensitivity analysis is conducted to study the effect of varying several costs and system uncertainty parameters to validate the cost-effectiveness of integrated stochastic modelling. The obtained results demonstrate that uncertainty has a significant influence on the decision variables and the total incurred cost, and it is not advisable to ignore the presence of the stochastic variables since they significantly modify the optimal decisions. Keywords: aggregate production planning, quality, random demand, stochastic programming, optimization

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

In this paper, a production plan stability control technique called flexible requirement profile (FRP) is combined with fuzzy aggregate production planning (APP) to not only handle the planning system uncertainties, but also to maintain the stability of the production plans when re-planning. More specifically, this paper proposes two new fuzzy aggregate planning models namely the fuzzy Max-Min FRP-APP and fuzzy ranking FRP-APP. The proposed models are tested on five industry-based cases and the results are compared to the crisp FRP-APP, and traditional fuzzy APP models with no stability considerations. The results indicate that the proposed fuzzy FRP-APP model is able to yield comparable stability and cost performance as the crisp FRP-APP model but produce noticeably more stable production plans compared to the non-FRP-APP models without significantly sacrificing the cost. [Received: 30 June 2019; Accepted: 12 August 2020]

It is widely felt that the performance evaluation in aggregate production planning provides a theoretical and practical overview. The present study aimed to evaluate the performance in the aggregate production planning. In this regard, the optimal values were determined by the multi-objective grey aggregate production planning model and the weights of the input and output indicators of the performance evaluation were characterized by the step-wise weight assessment ratio analysis (SWARA) technique. Further, the efficiency of the decision-making units was determined by the ratio efficiency dominance (RED) model. Then, the ranking of decision-making units was conducted. In the case study of automobile parts manufacturing industry in Iran, the sensitivity analysis was performed on the model and its effects were evaluated, in addition to evaluating the proposed model. The results indicated that the proposed model had a high degree of accuracy in evaluating performance compared to previous models and helps managers to make better decisions to increase the efficiency and reduce the waste of resources.

The higher online customers initiate promising advantage and grasping prospective opportunity for companies in Thai ecommerce market and receive more attention to the demand of cosmetic products. It is therefore useful or even necessary to give more attention to the management of production planning. For capturing customer preferences and sales prediction purposes in large domain considerable effort has directed to construct an effective model of aggregate production planning. This research examines growth of internet Thai users in general via various forecasting techniques of demand. The available data from service customer interaction are useful to predict the monthly purchase behavior of individual users. The proposed model balancing supply with demand to minimize the total production cost or maximize profitability gives high accuracy in predictions of the aggregate number of orders placed by all users each month. However, all parts of the E-Business organization such as operations, marketing including finance departments must join planning processes to ensure that they are moving in harmony with one another. Accurate forecasting and aggregate production planning are such techniques that can move all parts of the organizations in same harmony.

This chapter extends the flow control approach to the problems with multiple facilities and either single or multiple products characterized by simple structures. It, thus, deals with an intermediate hierarchical level that bridges aggregate production planning and detailed production scheduling characterized by complex product structures. Specifically, this chapter focuses on a typical problem arising in aggregate production planning and master scheduling — balancing materials and capacity requirements under given production and market conditions. The balancing is accomplished by dividing an aggregate material flow into subflows of specific items and allocating them among multiple manufacturing facilities. The goal of both the production plan and master schedule (in some companies it may be difficult to distinguish between them) is to optimally load the facilities when either initial inventory volumes or input inventory flows are predetermined at higher levels of top-down production planning hierarchy. This goal is achieved by tracking demands for products along a planning horizon in a make-to-order environment, or by filling the stocks with required volumes of products by the end of the planning horizon in a make-to-stock environment.

According to the main characteristics of production line of iron and steel enterprise, how to make aggregate production planning of iron and steel enterprise are analyzed. An aggregate production plan model of product line of iron and steel enterprise is proposed, which is calculated by using standard library function of the Matlab7.0 genetic algorithm toolbox to program. Penalty function was adopted in the course of getting solution. These parameters of scale of father population, crossover probability, mutation probability and penalty factor were combined and optimized. The result obtained is in accordance with the actual conditions of this enterprise basically that the optimization solution to production plans of real iron and steel enterprise by using the algorithm.