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 structured procedure for materials planning during production transfer

This paper explores materials planning procedures to ensure the materials’ availability during production transfers. The paper defines a production transfer as the preparation, physical transfer and start-up of relocated production. A structured procedure of materials planning during production transfer is developed based on theory, and then validated and refined based on the analysis of four case studies. The paper shows that there is a need for a structured procedure of materials planning during production transfers. It also explains the importance of activities that create prerequisites for the materials’ availability during production transfer, such as updating and adapting documentation, planning and control systems, and describes the activities that ensure the materials’ availability, such as preventive and corrective actions. A valid estimation of the time needed to reach a steady state and a combination of several preventive actions improves the ability to ensure that materials are available. The cases showed differences across company size, because large companies took more and farther-reaching preventive actions.     

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.     

A review of hierarchical production planning and its applicability for modern manufacturing

The hierarchical production planning (HPP) paradigm has become an accepted planning and control strategy for many medium-to-large manufacturing situations. While the paradigm appears intuitively obvious and appropriate for many factories, there are a number of modern manufacturing situations where the application of the HPP approach may not be appropriate. By understanding the fundamental principles and concepts inherent in the HPP approach, it is possible to identify situations suitable for HPP with little or no adaptation, and situations where HPP must be extensively modified before use. A poor understanding of HPP     

A simulation testing and analysis of aggregate production planning strategies

In this study, a hybrid discrete event simulation (DES) and system dynamics (SD) methodology is applied to model and simulate aggregate production planning (APP) problem for the first time. DES is used to simulate operational-level and shop-floor activities incorporated into APP and estimate critical time-based control parameters used in SD model of APP and SD is used to simulate APP as a collection of aggregate-level strategic decisions. The main objective of this study is to determine and analyse the effectiveness of APP strategies regarding the Total Profit criterion by developing a hybrid DES–SD simulation model for APP in a real-world manufacturing company. The simulation results demonstrated that the priority of APP strategies with regards to Total Profit criterion is: (1) the pure chase strategy, (2) the modified chase strategy, (3) the pure level strategy, (4) the modified level strategy, (5) the mixed strategy and (6) the demand management strategy, respectively. The APP system is first simulated under mixed strategy (basic scenario) conditions to include all APP capacity and demand options in constructed SD simulation model to show a comprehensive view of APP components and their interdependent interactions. Then, the obtained results will be used as Total Profit measure to compare with system's performance under some experimental scenarios applying different APP strategies.     

A possibilistic aggregate production planning model for brass casting industry

This article discusses a possibilistic aggregate production planning (APP) model for blending problem in a brass factory; the problem computing optimal amounts of raw materials for the total production of several types of brass in a planning period. The model basically has a multi-blend model formulation in which demand quantities, percentages of the ingredient in some raw materials, cost coefficients, minimum and maximum procurement amounts are all imprecise and have triangular possibility distributions. A mathematical model and a solution algorithm are proposed for solving this model. In the proposed model, the Lai and Hwang's fuzzy ranking concept is relaxed by using ‘Either-or’ constraints. An application of the brass casting APP model to a brass factory demonstrates that the proposed model successfully solves the multi-blend problem for brass casting and determines the optimal raw material purchasing policies.     

Linking aggregate production planning with marketing pricing decisions

In this paper, we link production planning decisions to marketing decisions that involve the price of product groups. The focus of this paper is the development of a closed-loop procedure for aggregate production planning and pricing. We seek to satisfy uncertain demand while minimizing total costs that include material, labour, and inventory holding costs. The procedure is useful for variable demand to update short-term aggregate plans.     

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.     

Experimental push/pull production planning and control system

We introduce an experimental push/ pull production planning and control software system which is designed as an alternative to a MRP-II system for mass manufacturing enterprises in China. It has the following distinguishing features: (1) putting the philosophy of JIT into the master production scheduling of MRP-II via the earliness/ tardiness production planning method; (2) controlling material input by push and processing/ assembly by pull; and (3) adjusting the parameters of the production line by the‘ suggestion for improvement of production line’ module. Simulation results have shown that the proposed system can achieve better planning and control performance than existing systems.     

Output planning for material requirements under uncertainty

This paper considers a production planning and control system requiring that, for each work station, a time-phased series of input and output values be estimated. It is assumed that the projected input levels can vary significantly across time periods and that anticipated output levels can be determined by management, subject to limits specified by available manpower and subcontracting. The actual input and output levels for each period depart from the planned levels according to a probability distribution determined empirically. An approach is presented that allows management to evaluate a chosen set of output levels to determine the probability that sufficient amounts of work will be available at a work station each period.     

Managing key interfaces in production planning and control

Detailed observational research, undertaken in two large and complex manufacturing businesses, identified ways in which work groups interface to employ knowledge to perform production planning and control activities. Eleven interfaces were observed and methods were established to classify them into types based on how knowledge is communicated and shared. Characteristics of the decision-making environment on either side of each interface were identified to explore their impact on interface type. Key differences in decision-makers’ predominant modes of decision-making, their agendas, and the form and scope of their knowledge were found to influence communication and decision-making processes. These findings have implications for production planning and control organisation and process design. In particular, it is seen that the effectiveness of interfaces may be improved through the alignment of decision-making approaches, agendas and knowledge across interfaces and/or through the effective use of interface tools that enable decision-makers to understand the consequences of decisions made.     

Scenario aggregation in single-resource production planning models with uncertain demand

In this paper we present an application of the scenario aggregation approach proposed by Rockafellar and Wets to a simple standard multi-product multi-period production planning problem with uncertain demand and setup cost modelled by logical zero-one variables. The uncertainty in demand is expressed by a number of demand scenarios. As compared with more traditional approaches that require distributional assumptions and/or estimates of parameters from historical demand data, the scenario approach offers greater flexibility and makes it possible to take subjective information into account. The scenario aggregation principle and the corresponding progressive hedging algorithm offer a theoretically sound basis for generating consistent solutions for production planning models with uncertain demand. Since the production planning problem studied in this paper is of mixed-integer type the original scenario aggregation approach cannot be applied directly. However, since the integer variables in the production planning model are indirectly coupled to the continuous production decisions an alternative method in which only the production quantities are used to couple the different realizations can be used. This paper is a first attempt to perform this form of coupling. We illustrate the ideas on a small example and use this example to demonstrate how the solution can be evaluated in terms of flexibility measures.     

IT security planning under uncertainty for high-impact events

While many IT security incidents result in relatively minor operational disruptions or minimal recovery costs, occasionally high-impact security breaches can have catastrophic effects on the firm. Unfortunately, measuring security risk and planning for countermeasures or mitigation is a difficult task. Past research has suggested risk metrics which may be beneficial in understanding and planning for security incidents, but most of these metrics are aimed at identifying expected overall loss and do not directly address the identification of, or planning for, sparse events which might result in high-impact loss. The use of an upper percentile value or some other worst-case measure has been widely discussed in the literature as a means of stochastic optimization, but has not been applied to this decision domain. A key requirement in security planning for any threat scenario, expected or otherwise, is the ability to choose countermeasures optimally with regard to tradeoffs between countermeasure cost and remaining risk. Most of the planning models in the literature are qualitative, and none that we are aware of allow for the optimal determination of these tradeoffs. Therefore, we develop a model for optimally choosing countermeasures to block or mitigate security attacks in the presence of a given threat level profile. We utilize this model to examine scenarios under both expected threat levels and worst-case levels, and develop budget-dependent risk curves. These curves demonstrate the tradeoffs which occur if decision makers divert budgets away from planning for ordinary risk in an effort to mitigate the effects of potential high-impact outcomes.     

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.     

A hierarchical production plan for a make-to-order steel fabrication plant

A three-tiered hierarchical production plan (HPP) for a strictly make-to-order steel fabrication plant with the objective of developing a production plan and master schedule for a set of product archetypes is implemented. Data are collected from an actual steel fabrication plant located in the Midwestern section of the US. An aggregate linear programming model, a non-linear disaggregate model and a master production schedule comprise the respective tiers. Appropriate models provide the forecasts needed in the first two tiers. A production plan and master schedule based on data collected at the plant, benefits expected for its implementation and practical limitations are reported.     

Load-oriented manufacturing control just-in-time production for job shops

The paper describes the principles of load-oriented manufacturing control as a new solution for job shops and its successful implementation in a pump-manufacturing factory. The idea of load-oriented manufacturing control is to limit and balance work-in-process inventory on a level as low as possible in order to accomplish a high work-centre utilization as well as a rapid and in-time flow of orders. As the new system keeps actual lead times on a planned level in a self-regulating way, it allows reliable due-date scheduling. Furthermore it points out the bottlenecks and performs applicable mid-term and short-term capacity planning.     

Tactical production and distribution planning with dependency issues on the production process

Tactical production-distribution planning models have attracted a great deal of attention in the past decades. In these models, production and distribution decisions are considered simultaneously such that the combined plans are more advantageous than the plans resolved in a hierarchical planning process. We consider a two-stage production process, where in the first stage raw materials are transformed into continuous resources that feed the discrete production of end products in the second stage. Moreover, the setup times and costs of resources depend on the sequence in which they are processed in the first stage. The minimum scheduling unit is the product family which consists of products sharing common resources and manufacturing processes. Based on different mathematical modelling approaches to the production in the first stage, we develop a sequence-oriented formulation and a product-oriented formulation, and propose decomposition-based heuristics to solve this problem efficiently. By considering these dependencies arising in practical production processes, our model can be applied to various industrial cases, such as the beverage industry or the steel industry. Computation tests on instances from an industrial application are provided at the end of the paper.     

多品种集约生产计划问题的模糊方法

提出具有模糊需求量和模糊能力约束以及资本水平约束的多品种类集约生产计划问题 的模糊优化模型及模糊解方法. 通过对模糊需求量和模糊等式的描述,提出了模糊需求环境下 生产- 库存平衡方程的两种等价的描述方法, 并给出了模糊等式的实用解释. 建立了具有模 糊需求量和模糊能力约束集约生产计划问题的优化模型FMAPP ,并给出了求解模型的参数规 划方法.     

Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty

We present a stochastic version of a three-layer supply network planning problem that includes the selection of vendors that must be equipped with company-specific tools. The configuration of a supply network must be determined by using demand forecasts for a long planning horizon to meet a given service level. The risk induced by the uncertain demand is explicitly considered by incorporating the conditional value at risk. The objective is to maximize the weighted sum of the expected net present value of discounted cash flows and the conditional value at risk. This would lead to a non-linear model formulation that is approximated by a mixed-integer linear model. This approximation is realized by a piecewise linearization of the expected backlogs and physical inventory as non-linear functions of cumulative production quantities. A two-stage stochastic programming approach is proposed. Our numerical analysis of generic test instances indicates that solving the linearized model formulation yields a robust and stable supply network configuration when demand is uncertain.     

Engineer-to-order (ETO) production planning and control: an empirical framework for machinery-building companies

This paper illustrates the results of an empirical study involving 21 engineer-to-order (ETO) companies, operating in the machinery-building industry. The study investigates the needs and requirements of such companies in terms of software support for governing the businesses, with particular emphasis on production planning and control (PPC) processes. An empirical analysis investigated two main aspects: (i) the set of business activities performed by the companies in the analysed industry and (ii) the relevant, high-level software functionalities required for the execution of such activities. As an answer to the observed compelling need for reviewing the general approaches to PPC in machinery-building companies, we develop an empirical, high-level production planning and scheduling reference framework, encompassing all the activities involved in the order fulfilment process.