Experimental earliness/tardiness production planning with the due window system

In order to use the philosophy of JIT to improve the production planning method of MRP-II, we propose the experimental software system of the earliness/tardiness produc tion planning problem with due window. By means of the approaches and model reported in this paper, the optimal production planning can be achieved. The recommended model extends the problem of due window from the shop scheduling level into the aggregated planning level of mass manufacturing systems. Simulation results have demonstrated that the experimental software is a useful tool for the production management of repetitive manufacturing enterprises.     

A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

Computational complexity of algorithms for MRP and JIT production planning problems in enterprise resource planning systems

An enterprise resource planning system (ERP) is the information backbone of many manufacturing companies. At the core of ERP is a conventional material requirements planning (MRP) production planning system or a variation of MRP when just-in-time (JIT) principles are used in manufacturing. MRP and JIT both organize production planning into a hierarchy of long-, medium- and short-range problems. In all there are eight different problems. Some are common to MRP and JIT, others are specialized for a particular system. This paper analyses the computational requirements of these problems. This is important for ERP because it plans for large numbers of products (e.g. 50 000 products at 3M Company and 44 000 products at States Industries in Oregon) at large numbers of locations (e.g. 82 locations in 21 countries for Visteon Automotive Systems of Michigan and 19 locations at Boeing). We show that adequate algorithms exist for some problems, but better algorithms are needed for other problems if ERP is to provide useful production plans.     

A finite capacity material requirements planning system

In a discrete parts manufacturing environment, material requirement planning (MRP) is carried out without considering the manufacturing resource capacity. As a result, during implementation, adjustments in planned orders may become necessary. This paper presents a finite capacity material requirements planning algorithm (FCMRP) to obtain capacity-based production plans. These plans need no costly adjustments to satisfy the capacity constraints. Performance of the FCMRP system, when studied through a set of test examples, has been found to be superior to the existing MRP system.     

The relative importance of planning and control systems in achieving good delivery performance

Amongst factors such as quick changeovers and workforce flexibility, managers in 533 UK manufacturing plants ranked a responsive planning and control system as the most important facilitator of good delivery performance on products made-to-order or assembled-to-order. The rankings indicate greater importance to companies in the Household and Engineering sectors than in Process and Electronics, where other factors are dominant. These results are combined with data such as customer lead times and item variety, to characterize and explain differences between the plants in these four sectors. Collectively the results indicate considerable differences in the production planning and control tasks. This implies that general statements on the importance of planning and control systems are inadequate. Practitioners need contextual information in order to ascertain when research is applicable to their circumstances.     

Aggregate production planning with workforce transferring plan for multiple product types

This paper presents the aggregate production planning for multiple product types where the worker resource can be transferred among the production lines. A mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A real situation of a manufacturing company was selected as a case study. The actual data was used to test and validate the proposed model. The optimal aggregate production plan provides the information on managing the available production capacity together with the useful workforce transferring plan. The obtained solutions were compared to those of another approach where the workers cannot be transferred among the production lines. The total cost is significantly reduced when the workers are allowed to transfer among the production lines.     

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.     

Period batch control: Classic,not outdated

Period Batch Control was developed and first applied during the Second World War. The historic roots are discussed and the principles of this classic production planning approach explained, PBC compared to other production control concepts, and it is demonstrated that it may still be fruitfully applied, especially in combination with cellular manufacturing. It is therefore considered as a classic, but not outdated production planning concept.     

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.     

AN ANALYSIS OF CAPACITY PLANNING PROCEDURES FOR A MATERIAL REQUIREMENTS PLANNING SYSTEM*

This paper is concerned with planning work-center capacity levels in manufacturing firms that employ a material requirements planning (MRP) system. It presents four procedures for developing work-center capacity plans designed to insure the production of components and assemblies as specified by the MRP plan and the master production schedule (MPS). These procedures (capacity planning using overall factors, capacity bills, resource profiles, and capacity requirements planning) are compared using simulation analysis. The results indicate that the performance of a procedure when measured against the MPS depends on the operating conditions of the manufacturing system. The results also indicate that the choice of a particular procedure often represents a compromise among the benefits of improved MPS performance, the costs of preparing and processing data, and the premium expenses required for more frequent adjustments in work-center capacity levels.     

A multistage approach for production and workforce planning in long-cycle product environments

Aggregate production planning (APP) has been studied extensively for the past two decades. The APP problem, also called production and workforce scheduling, is to determine the optimal workforce and production level in each period of the planning horizon in order to satisfy demand forecasts for these periods. The advantages of the APP are low cost of data collection and computational cost of the running model; the accuracy of data; and, effective managerial understanding of the results. If the product of concern takes longer than one period, it is called a long-cycle product. Examples of long-cycle products are aircraft, ships, buildings and special machines. A detailed model incorporating dynamic productivity and long-cycle products considerations is presented to solve the problem of production and workforce planning. Using a multistage production system approach, a search technique is developed to solve this class of problems where the objective function is linear and some of the constraint coefficients are dynamically nonlinear. The model provides a better solution than an aggregate production planning model, often used to solve these problems.     

A framework for production control in health care organizations

The paper presents a hierarchical framework for production control of hospitals which deals with the balance between service and efficiency, at all levels of planning and control. The framework is based on an analysis of the design requirements for hospital production control systems. These design requirements are translated into the control functions at different levels of planning required for hospital production control. The framework consists of five levels of planning and control: patient planning and control, patient group planning and control, resources planning and control, patient volumes planning and control and strategic planning, though this last level does not make part of production control as such. Each of the levels of the framework is further elaborated in terms of the decisions made regarding patient flows and resources, and the co-ordination of the different planning levels. Implications of the framework are discussed by describing some points where current practice deviates from assumptions made in our approach. Recommendations for future research and development of the planning framework are formulated.     

An aggregate production planning framework for the evaluation of volume flexibility

Flexibility in manufacturing has been identified as one of the key factors to success in the marketplace. Many types of flexibility have been identified in the literature among which volume flexibility is one of the most important. Volume flexibility of a manufacturing system is defined as its ability to be operated profitably at different overall output levels. Volume flexibility permits a manufacturing system to adjust production upwards or downwards within wide limits. In this paper, we develop an aggregate production planning model for volumeflexible production systems. The model can be used with a Monte Carlo simulation to evaluate the optimal level of investment in volume flexibility for a firm operating under a given set of market conditions. In addition, the model can be used to develop some conclusions about the relationship between the value of volume flexibility and the cost of holding inventory, the cost of shortage, forecast accuracy, and the length of the planning horizon.     

Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

Alternate Routing Strategies in Batch Manufacturing: An Evaluation

Batch manufacturing firms are experiencing significant changes because of technological developments in work center design, such as flexible manufacturing systems (FMS) and planning/control tools like computer-aided process planning (CAPP). These new developments provide production managers with some solutions to a number of complex problems. For example, numerical-controlled (NC) machine center installations are effective in providing quality parts because of tight tolerance specifications built into the equipment. However, these highly efficient centers create bottlenecks that constrain shop throughput, since production planners tend to rely too much on them. To help improve manufacturing planning, we introduce an important element to the batch production scheduling component of CAPP's mission—evaluating possible alternate routes. Production scheduling encompasses job route selection as well as machine center assignment (loading), job releasing, and setting due dates. In this paper, three routing strategies requiring different levels of shop floor information are tested and evaluated using computer simulation. Shop performance is measured by total cost and traditional measures of job flow time, lateness, and tardiness.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.     

Production planning and control tools

There are numerous tools available to be used for production planning and control purposes. The number of tools is ever increasing, and so are the levels of sophistication as well as complexity. For the specific manufacturing firm, the task of selecting the most appropriate set of tools is not trivial. However, in recent years, the understanding of the relationship between tools and manufacturing environments for which they are suitable has increased. The purpose of this paper is to provide an overview of production planning and control tools available today, as well as new trends, issues and ideas.     

A taxonomy of international manufacturing networks

Abstract

Manufacturing firms with multiple product groups do not need to involve all factories in the production of all product groups. Some factories may specialize on a small set of products, while others participate in the manufacturing of a broader set of products. However, current theories on international manufacturing networks do not explain in detail how organizations design international manufacturing networks for different products or product groups involving different sets of factories. This research investigates 20 product group networks at five global manufacturing firms. We distinguish between three types of factories: component manufacturing factories, assembly factories, and integrated factories (having both component manufacturing and assembly). Furthermore, we identify four network types: linear, divergent, convergent, and mixed structures. These four types exhibit distinctly different characteristics in terms of key characteristics, factory roles, product types, process types, market types, sourcing, and key managerial challenges. Most networks are relatively small – on an average consisting of four factories and some contain a number of subnetworks that are self-sufficient in terms of material flow and serve separate market regions. We identify two new types of factory roles related to component manufacturing competences, which we call ‘strategic feeder’ and ‘full lead’.     

Distributed Computer System Capacity Planning and Capacity Loading

The availability of micro-, mini-, and super computers has complicated the laws governing the economies of scale in computers and has increased the tendency to decentralize and distribute computing power. The optimal design of such a system requires integration of computers of varying power and a strategy for capacity loading. This paper considers the problem of capacity planning and capacity loading of a distributed computer system as a hierarchy of decisions. A linear programming model is developed for the initial capacity planning problem. A loading model that accounts for variations in arrival and processing rates of the jobs in a dynamic environment is developed to support the operations.     

Hierarchical production planning for multi-product lines in the beverage industry¶

Multi-commodity production and distribution scheduling is one of the most complex and crucial problems facing many manufacturing companies. For a major European manufacturer specialising in bottling juices and drinks, we have designed and developed a hierarchical decomposition approach to the solution of the multi-commodity production planning problem. In this paper we focus our attention on the coarsest decomposition level, called multi-commodity aggregate production planning (MCAP). It concerns the choice of the best feasible production plan for a set of products (commodities) over an extended time horizon so as to meet forecast aggregate demands throughout the horizon. At this level, the problem constraints include hard constraints (such as production lines having a maximum capacity and products having short life-times), and soft constraints (budgetary concerns.) The objective is to determine the production plan that covers each period's demands as best as possible, while minimizing all relevant costs. Our method for solving MCAP produces optimal plans in negligible times in commodity PC workstations.