Freezing the master production schedule in multilevel material requirements planning systems under deterministic demand

This paper extends the studies by Sridharan, Berry, and Udayabhanu from single-level MPS systems to multilevel material requirements planning (MRP) systems, and examines the impact of product structure, lot-sizing rules and cost parameters upon the selection of MPS freezing parameters under deterministic demand. A model is built to simulate the master production scheduling and material requirements planning operations in a make-to-order environment. The results show that all the MPS freezing parameters studied have a significant impact upon total inventory costs and schedule instability in multilevel MRP systems. First, the order-based freezing method is preferable to the period-based method. Secondly, the study finds that increasing the freezing proportion reduces both total inventory costs and schedule instability. This finding contradicts the finding by Sridharan et al. in single-level systems. Thirdly, the study finds that a higher replanning periodicity results in both lower total inventory cost and lower schedule instability. The study also indicates that the product structure and lot-sizing rules do not significantly influence the selection of MPS freezing parameters in a practical sense under most situations. However, the cost parameter seems to significantly influence the selection of replanning periodicity.     

A note on the deterministic EPQ with partial backordering

In a recent paper published in Omega [Pentico DW, Drake MJ, Toews C. The deterministic EPQ with partial backordering: a new approach. Omega 2009; 37(3):624–36], the authors proposed an EPQ model with partial backordering. In the EPQ model, a critical value of backordering rate was developed, below which the optimal production policy is either to lose all sales or meet all demand, above which the optimal policy is to allow stockouts with partial backordering and meet fractional demand. However, there exists a flaw in the critical value of backordering rate, which will be amended in this note.     

DEA-based production planning

Production in large organizations with a centralized decision-making environment like supermarket chains or factories with many workshops, usually involves the participation of more than one individual unit, each contributing a part of the total production. This study is motivated by a production-planning problem regularly faced by the central decision-making unit to arrange new input and output plans for all individual units in the next production season when demand changes can be forecasted. Two planning ideas have been proposed in this paper. One is optimizing the average or overall production performance of the entire organization, measured by the CCR efficiency of the average input and output levels of all units. The other is simultaneously maximizing total outputs produced and minimizing total inputs consumed by all units. According to these two ideas, we develop two DEA-based production planning approaches to find the most preferred production plans. All these individual units, considered as decision-making units (DMUs), are supposed to be able to modify their input usages and output productions. A simple numerical example and a real world data set are used to illustrate these approaches.     

Marginal analysis production planning

A forward looking with backward recourse production planning heuristic is developed using marginal analysis. In the search for a minimum cost solution, a set of rules derived by Kunreuther and Morton for determining planning horizons is employed. It is shown that the logic of the heuristic is similar to several dynamic lot-sizing models. The marginal analysis production planning (MAPP) heuristic is computationally more efficient than optimizing approaches and gives results that are generally less than 5% more expensive than the optimal solution. In addition, through the notion of level periods, the heuristic permits the user to easily investigate strategies where the work force size and daily production rate remain constant for a specified number of periods.     

Development and teaching a computer-assisted course in production planning

Abstract. Course development and student activities are described for a successful computer-assisted senior level course in production planning. Development was guided by the view that students must (1) understand the elements being integrated, (2) understand the problems that are being resolved through integration, and (3) participate in the integration activity. Production planning is treated as a hierarchy of individual decision situations which are to be integrated. Each decision is assisted by a software-based technique. A menu program and a common database were developed to allow a student to become the 'integrator' by directing the information flow among the individual decision situations. The paper describes the development tasks, the course elements, and student activities which provide guidelines for others developing similar courses.     

Hierarchical production planning at a multiproduct multiprocessor fibreglass manufacturing facility

Abstract

Abstract. A hierarchical production planning model is developed for a forming facility of a major fibreglass manufacturer. The model establishes aggregate quarterly quantities for families of products, monthly disaggregate production quantities for end-items within these families, and a weekly sequencing schedule of end-items. A key feature of the planning system is its ability to decompose total set-up cost into primary and secondary components and to account appropriately for each component at a distinct level of the planning hierarchy. The mathematical programming formulations, the accompanying solution algorithms, and the results of their application to nine months of actual company demand and cost data are presented.     

Building a short-term production planning system for flexible manufacturing systems: an integration viewpoint

From the viewpoint of integration, the production objective of flexible manufacturing systems (FMS) should follow results of MRP/CRP. Thus, the planning goal of short-term production planning (STPP) is to satisfy quantity and due date requirements determined by MRP/CRP. The STPP of FMS should cover part type selection, production ratio, process selection, process ratio, resource allocation, machine grouping, routing mix and loading problems.

A STPP system is built to solve the STPP problem of an FMS having multiple machine types, each with multiple m.'ichinc units. This system adopts a variable-period flexible planning approach, using part mix and process selection flexibilities to solve STPP problems. It also provides flexibilities in its operations sequence and in the machine routing to the dynamic operation planning system.     

Aggregate production planning using spreadsheet software

Firms can benefit by developing and using aggregate production plans. However, reports of ongoing production planning applications are rare. The complexity of production planning models has been cited as one of the reasons that firms do not develop formal production plans. This study was conducted to determine if a simple model, trial-and-error on a spreadsheet, could be used to produce cost-effective production planning solutions. Results indicate that good solutions can be obtained using this method. Furthermore, solutions tend to improve as the knowledge of the production planner increases and as time spent developing solutions increases. In addition, experience at producing spreadsheet-based solutions can translate into lower cost solutions for more complex problems while using less time for analysis. These findings indicate that spreadsheets can be effective decision aids for developing production planning solutions. By coupling the power and simplicity of a spreadsheet modelling package with the trial-and-error approach to production planning, model complexity should no longer be cited as a reason for not developing and using formal aggregate production plans.     

A note on an economic production quantity model and just-in-time production

In this paper an economic production quantity (EPQ) model in which the production rate is variable is studied. An analysis is presented of the impact of a variable production rate on the optimal production quantity and the total relevant cost. It is observed that this EPQ production and inventory system, in which the production rate is close to the demand rate, possesses many characteristics that are similar to a just-in-time (JIT) production system. It is shown that the normal prerequisites and benefits of JIT production can be identified from an analysis of such an EPQ system.     

Implications of selected production planning functions: Impact analysis through simulation

When capacity differences are minimized through an efficient algorithm, and integration of capacity planning with any production planning system is performed, it affects some elements of production planning functions. In the reverse way, some elements of production planning and management techniques also affect the effectiveness of capacity planning. These happen because capacity planning processes, production planning processes and production management techniques are not standalone sub-systems, rather these are totally dependent on each other. This paper aims at determining and formulating the effects of some of the selected elements of capacity and production planning functions on each other. This study is conducted using simulation in object-oriented SIMPLE+ + system.     

Concurrent consideration of product design,process planning and production planning activities

In manufacturing engineering, product design, process planning and production planning activities are often considered independently. However, in order to effectively respond to changes in business situations, such as changes in demand forecast, product mix and technology, it is desirable to consider them concurrently. For this purpose, a large-scale linear programming model has been developed. The model considers minimization of the sum of processing cost, late shipment cost and inventory holding cost as the objective, and concurrently selects product designs, and generates process plans and production plans. The number of columns in the formulation can be large and, hence, an efficient column generation scheme is developed to solve the model. The model and solution procedure are illustrated with examples.     

An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches

We study a real-world production warehousing case, where the company always faces the challenge to find available space for its products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported.     

Integration of process planning and production planning and control in cellular manufacturing

Organisations willing to succeed in global competition have to integrate their internal and external processes. This especially includes planning and production control (PPC) processes. Optimised allocation of the production resources and quick response to demand changes result in lower cost and improvement of production performance. Practitioners and researchers have been trying to achieve these goals using production planning techniques. Although the results are significant, it seems necessary to integrate production operations in order to improve the production performance. The goals, information and decisions taken in production planning and control and process planning are often very different and difficult to integrate in Cellular Manufacturing (CM) environments. Designing an efficient PPC system and integrating it with process planning in a cellular environment is of the same importance. The following paper proposes first a comprehensive framework of integrated process planning and production planning and control in CM. Then, with respect to this framework and utilising the domain knowledge in the area of CM systems, an integrated model based on Integrated Definition Modeling Language is developed. The application of the models has been considered as a case study for a production system in electronics and telecommunication sector in a plant in Iran. The validity and completeness of the proposed model is tested by a panel of experts in the areas of production planning and control in CM environments.     

Neural networks in production planning and control

The purpose of this paper is to present a selection of the most significant ANN (artificial neural network) applications used to solve PPC (production planning and control) problems. Therefore, the paper itself is a reasoned classification rather than a simple survey. The PPC domain is divided into various areas, and for each area one or more examples of ANNs are discussed, together with the benefits and drawbacks of their application to that particular area. Finally, two case studies developed by the authors are reported and discussed to analyse the main implementation problems to be considered when using ANNs.     

Decision support for multi-family aggregate production planning

Aggregate production planning decisions are inter mediate range decisions that can have a significant impact on both productivity and profitability. In this paper, we examine an interactive computer-based method that provides decision support for the aggregate planner. The proposed approach combines the judgement of the planner with the optimization of subproblems to arrive at an effective solution for multi-family aggregate production planning problems. In the interactive approach, the planner exercises direct control over sensitive workforce levels and production capacities. A network flow sub-problem solver is used to generate optimal production plans and inventory levels given the user-specified production capacities. Decision aids are provided to help the planner achieve a cost-effective solution that is consistent with judgement concerning workforce levels. Computational testing on five test problems indicates that very cost-effective solutions can be obtained. The results of applying the interactive method to a real-world problem are also reported.     

Material planning for production kits under uncertainty

A kit is a specific collection of components and/or tools needed for completing a procedure or producing a product. A multiperiod material planning system for production kits under the demand and procurement lead-time uncertainty is considered when component sharing among kits is plausible. Simulation experiments show that component sharing improves the system's service measure of average kit availability and average backorder quantity per period. Also, carrying component safety stock only enhances the benefits of component sharing by reducing the average backorder quantity at the expense of increased inventories, but does not improve the average kit availability.     

Synchronization of material flow to aid production planning in a job shop

This paper discusses how synchronizing finish times for correspondingly numbered process operations of mating component parts (batches) can help a job shop take advantage of some aspects of the just-in-time philosophy.This paper proposes that the synchronization of process operations of mating parts (batches) can be an effective production planning tool that may actually decrease total throughput time where manufacturers are attempting to decrease in-process inventory. An example problem is included to demonstrate the situation.A production planner must keep in perspective all production orders when trying to decrease in-process inventory. The throughput times of all orders may be increased, while too much effort is concentrated on reducing in-process inventory costs of individual orders.     

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failures, repairs and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviors of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production/subcontracting control policies (general, urgent, regular) are compared to one another. The proposed joint production/regular subcontracting control policy involves a cost decrease of up to 20%, as compared to results obtained by Dror et al. [1], who used a simplified control policy in addition to a heuristic solution approach for a real case study. This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.     

A case study of production expansion planning in a soft-drink manufacturing company

This paper discusses the problem of production expansion planning in a soft-drink manufacturing company. We first formulate the problem as a large-scale transportation problem by making some realistic simplifications. We then program the transportation algorithm on a spreadsheet software to help solve the problem on a microcomputer. Through this case study we demonstrate how some standard OR technique can be combined with low-cost microcomputers to become an effective management tool to handle complex management decision problems.     

Multi-site aggregate production planning with multiple objectives: A goal programming approach

This paper addresses the problem of aggregate production planning (APP) for a multinational lingerie company in Hong Kong. The multi-site production planning problem considers the production loading plans among manufacturing factories subject to certain restrictions, such as production import/export quotas imposed by regulatory requirements of different nations, the use of manufacturing factories/locations with regard to customers' preferences, as well as production capacity, workforce level, storage space and resource conditions of the factories. In this paper, a multi-objective model is developed to solve the production planning problems, in which the profit is maximized but production penalties resulting from going over/under quotas and the change in workforce level are minimized. To enhance the practical implications of the proposed model, different managerial production loading plans are evaluated according to changes in future policy and situation. Numerical results demonstrate the robustness and effectiveness of the developed model.