Aggregate versus disaggregate production planning: a simulated experiment using LDR and MRP

Within the past several years, considerable research has been devoted to the aggregate production planning problem starting with the pioneering work of Holt el al, (1955) and the resulting Linear Decision Rule (LDR). However, researchers have also recognized that developing optimal aggregate production plans, per se, is not sufficient for solving real problems; these plans have to be disaggregated into specific schedules for specific products. Consequently, the thrust of current research is on the ‘disaggregation’ problem. Simultaneously a number of companies have been installing MRP systems with reports of significant improvements in inventory control, production planning, work force scheduling and production costs. This paper reports on an experiment which was designed and conducted to compare the effectiveness of the ‘aggregate-disaggregate’ and MRP approaches to production planning in a simulation environment. LDR was used as the optimal aggregate technique in the aggregate-disaggregate approach. The results appeared to favour the MRP approach     

Building an active material requirements planning system

A material requirements planning (MRP) system is usually implemented with several constraints, such as replenishment of inventories on a period-by-period bases, which obstruct its dynamic performance. This research proposes an active, bucketless, and real-time MRP system. The active MRP system utilizes hybrid architecture that includes an object-oriented database, fuzzy logic controllers, and neural networks. The object-oriented database, which maintains static data relationships, provides superior capabilities in reusability, complex structure operations, and potential integration. The complementary combination of fuzzy logic controllers and neural networks provides a model-free, human-like decision system. Adding triggers and assertions forms an active MRP model.     

Lot-sizing rules and freezing the master production schedule in material requirements planning systems under demand uncertainty

Freezing the master production schedule (MPS) is one of the frequently used methods for reducing schedule instability in multilevel material requirements planning (MRP) systems. The selection of the lot-sizing rule is an important decision in MRP systems and has been shown to significantly influence MRP performance. This study examines the interaction between the selection of lot-sizing rules and the selection of MPS freezing parameters under rolling time horizons under different demand uncertainty conditions. The findings of this study enhance the understanding of multilevel MRP performance in a more realistic environment and help MRP practitioners select the proper lot-sizing rule and MPS freezing parameters.     

An integer linear programming model to support customer-driven material planning in synchronised,multi-tier supply chains

This paper describes an integer linear programming model conceived as an alternative to a traditional material requirements planning (MRP) system for extending the concept of supply chain synchronisation upstream in a multi-tier supply chain. In this model, we assume there is an incumbent application for transmitting original equipment manufacturer (OEM) requirements to first-, second- and third-tier suppliers. The proposed model is regarded as being embedded within a web-enabled, multi-tier, supply chain information system that provides the application for transmitting the production requirements. The principal motivation for having second- and third-tier suppliers that are synchronised with OEM and first-tier activity is the significant inventory, lead time and responsiveness gains that can potentially be achieved. Here, inventory is considered as a whole across a supply chain, and stock-outs are prohibited for the first-tier supplier. For illustration purposes, an example based on a real, automotive case study is provided. The model results proved better in terms of inventory and bullwhip reduction than those found in a previous simulation-based approach. Also, a comparison of the proposed case results with those of a conventional MRP application is provided.     

Material and capacity requirements planning with dynamic lead times

Traditional material requirements planning (MRP) does not consider the finite capacity of machines, and assumes fixed lead times. This paper develops an approach (material and capacity requirements planning; MCRP) to integrate capacity planning into MRP. To obtain a capacity feasible production plan, different measures for capacity adjustment such as alternative routeings, safety stock, lot splitting and lot summarisation, are discussed. Additionally, lead times are no longer assumed to be fixed. They are calculated dynamically with respect to machine capacity utilisation. A detailed example is presented to illustrate how the MCRP approach works successfully.     

Improving manufacturing lead time using holistic approach to planning and execution with integrated data structures: numerical simulation and comparison

Manufacturing planning and control (MPC) plays a significant role in effective planning of requirements and execution of those plans. It also influences various performance measures, in particular on-time delivery, which is dependent on many factors including manufacturing lead time. This paper aims to address key issues of infinite loading of resources, lack of forward planning, and limited simultaneous planning capabilities of MRP through a holistic approach to planning of various components and execution of those plans, based on integrated data structures. It is shown that integrated data structures, combining hierarchical single-level BOMs and sequential operations routings using a holistic approach to planning and execution enable reducing lead time by eliminating slack times associated with traditional methods. Furthermore, manufacturing lead time, based on integrated data structures is a true representation of operation times and is shorter compared with traditional lead time. Sensitivity analysis on lead-time with bills-of-materials (BOM) levels and floats shows that the holistic approach to planning and execution with integrated data structures is an effective way of improving lead-time, in particular when product structures involve many levels of BOMs and floats (slack times). The proposed holistic approach provides forward planning, simultaneous planning of components, and finite loading of resources. It is expected that the concept of integrated data structures be extended to model transaction data with process functions (e.g. production order cycle), which would improve overall cycle times by eliminating manual functions associated with current methods.     

Robust material requirement planning with cumulative demand under uncertainty

In this paper, we deal with the problem of tactical capacitated production planning with the demand under uncertainty modelled by closed intervals. We propose a single-item with backordering model under small uncertainty in the cumulative demand for the Master Production Scheduling (MPS) problem with different rules, namely the Lot For Lot rule and the Periodic Order Quantity rule. Then we study a general multilevel, multi-item, multi-resource model with backordering and the external demand on components for the Material Requirement Planning (MRP) problem under uncertainty in the cumulative demand. In order to choose robust production plans for the above problems that hedge against uncertainty, we adopt the well-known minmax criterion. We propose polynomial methods for evaluating the impact of uncertainty on a given production plan in terms of its cost and for computing optimal robust production plans for both problems (MPS/MRP) under the assumed interval uncertainty representation. We show in this way that the robust problems (MPS/MRP) under this uncertainty representation are not much computationally harder than their deterministic counterparts.     

以计划网络为基础的MRP算法研究及实现

针对复杂产品制造企业应用常规MRP算法编制的生产计划存在有效性差、调整频繁、重复计算量大等问题,提出了一种以计划网络为基础的MRP算法.该算法是在常规MRP算法的基础上,将运算产生的多级需求计划指令与生产实际安排建立关联关系,应用网络计划技术构建零组件计划网络,增强和扩展了MRP功能,提高了需求计划指令的有效性,并在原型系统开发中得到可行性验证.     

Capacity-constrained reorder intervals for materials requirements planning systems

This paper develops a model for the reorder interval problem for general production systems with constant demand, multiple capacity constraints, commonality, non-instantaneous production, and non-nested reorder intervals. We present this model in the context of a materials requirements planning (MRP) system.

Four simple greedy heuristics are presented to find solutions to the model. A six-factor experiment with 192 test problems is used to evaluate the heuristics. The factors in the experiment included the procedures, number of items, capacity tightness, degree of commonality, setup cost to carrying cost ratio, and setup time to run time ratio. For smaller problems the heuristics are compared with optimal solutions found with an exact branch-and-bound algorithm. For larger problems, the heuristics are compared with a lower bound.

The results of the experiment show that the heuristics provide excellent solutions across all experimental factors. Computing times for the proposed heuristics appear to be practical even for realistic MRP environments with many thousands of items.     

Capacitated lot sizing with alternative routings and overtime decisions

Material requirements planning (MRP) is a basic tool for performing detailed material planning function in the manufacture of component parts and their assembly into finished items. MRP's managerial objective is to provide ‘the right part at the right time’ to meet the schedules for completed products. However satisfying end customer demands faster with lower inventories implies smarter scheduling which must simultaneously reflect actual capacity conditions. Therefore, the need is to schedule both capacity and materials simultaneously. Since MRP does not consider the availability of capacity resources to schedule production, consequently the schedules so developed are usually capacity infeasible. This paper proposes a three-step procedure to develop capacity feasible material and production schedules in a finite capacity environment. In the first step, an LP model produces capacity feasible but lot size relaxed planned order releases for all end products and assembly components which are then fed into a MRP processor, where a bill of material (BOM) explosion process generates material plans. Finally, these material plans are introduced to another LP model which assures that capacity feasibility is again restored. The mathematical models developed consider restrictions on lot sizes as well as alternative production routings and overtime decisions. A numerical example also is provided and some future research directions are outlined.     

Integrated group technology,cell formation,process planning,and production planning with application to the emergency room

In this paper an integrated approach for the formation of parts and machine families in group technology is developed. The integrated approach is used to solve cell formation, process planning, and production planning simultaneously. The given information is part processing sequence, part production volume, part alternative processing plans, and part processing times. The approach is used to determine the machine-part cells and part processing plans, while the total intercell part flow is minimized. Also, the convergence of the algorithm is investigated. The approach goes across and beyond the group technology methods by considering sequencing, production planning, process planning, and part-machine cellular information simultaneously. Two methods are investigated: exact (optimal) and heuristic. The approach first solves an integer programming problem to find processing plans and then uses a procedure to form the machine-part cells. The proposed approach solves the problem iteratively until a set of plans for machine-part cell formations is obtained with minimal intercell part flow or interflow cost. An example is presented to explain the developed approach. Experimental results are also provided. An extension of the approach for solving the operations planning of an emergency room is also covered. In this extension of the approach, the application of cell formation provides a solution to efficiently managing patients and utilizing resources. By grouping patients by their needed medical procedures, time and resource efficiency is accomplished. An application to ER of University Hospitals of Case Western Reserve University is given.     

A mathematical programming model for production planning using CONWIP

Push-based MRP and pull-based Kanban systems are effective planning tools for a wide range of manufacturing production. Both of them, however, have certain limitations when they are implemented in different production environments. In recent years, CONWIP (CONstant Work-In-Process) has been proposed and studied to take advantage of MRP and Kanban systems for optimal work-inprocess (WIP) inventory control. In this paper, an integer nonlinear mathematical programming model was developed to determine an optimal production sequence and lot sizes in a CONWIP production line. The nonlinear programming model was linearized and solved directly for a number of illustrative example problems.     

Auction-theoretic coordination of production planning in the supply chain

Most planning and optimization methods in manufacturing logistics assume centralized or hierarchical decision-making using monolithic models. Motivated by the increasing needs to coordinate diverse decision processes and systems, we investigate an auction-theoretic mechanism for production coordination in a supply chain. Our effort focuses on structural mappings between mathematical decomposition and iterative auction mechanisms wherein agents compete based on their local utilities, announced conflict pricing, and production targets. Building upon the rich literature in optimization and auction-theoretic analysis, we investigate the advantages and limitations of this distributed decision scheme on a large set problem in supply chain production planning. Experimental results show that the proposed auction mechanism provides impressive improvement over the traditional monolithic method without significant degradation to the solution quality.     

GA-based adaptive setup planning toward process planning and scheduling integration

Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setup planning.     

Improving production planning through finite-capacity MRP

Materials Requirement Planning (MRP) technique is widely employed by most manufacturing companies, even though field applications point out some weaknesses, including ignored production capacity constraints and fixed lead-times. These weaknesses often lead to infeasible production schedules, which trigger fluctuating workloads over time, significant adjustment effort and eventually unpredictably long lead times. This paper introduces a capacity-oriented MRP procedure that combines the traditional MRP procedure with an approach based on linear programming: in this way, requirement of lead times pre-determined a priori outside the MRP procedure is overcome. The new procedure is then applied to a real-life company and results highlight that feasible plans of orders are generated without requiring lead-times as input and without relevant computational burden.     

Lot sizing in capacitated production planning and control systems

Current production planning and control (PPC) systems often separate material requirements from capacity planning. As a result, practitioners often complain about the infeasibility of production schedules regarding capacity, which causes long and unpredictable lead times and poor customers service. This paper describes a hierarchically structured PPC system that explicitly considers production capacity at each stage of the planning process. The impact of the certainty of demand data on the integration of lot sizing and sequencing decisions is discussed. A decision model for lot sizing applicable to changing demand data is proposed. It distinguishes between resources that are critical or uncritical with respect to batching decisions. Several currently available solution procedures are discussed and compared that support lot sizing decisions in multi-level production systems subject to multiple capacity constraints, setup times and dynamic demand rates.     

Manufacturing planning and control approaches: market alignment and performance

The design of manufacturing planning and control (MPC) systems is a strategic decision for manufacturing operations. In this paper we analyze the interrelationships between the choice of MPC approaches at different hierarchical levels with market requirements and operational performance. These relationships are explored through an extensive survey comprising responses from 128 manufacturing firms. The results show that the choice of MPC approaches, primarily at the sales and operations planning and master scheduling levels, has a significant mediating role in improving performance. The alignment between market requirements and the choice of MPC approaches is significant and has a significant impact on performance. In a dynamic environment, the choice of MPC approaches is shown to have a positive mediating effect on operational performance.     

An interactive decision framework for multiple objective production planning

The formation of realistic implementable medium-range production plans requires explicit recognition of the multiple conflicting objectives of production planning. However, suggested applications of multiobjective optimization to production planning have been limited to goal programming procedures which fail to capitalize on the intrinsic flexibility of a multiobjective model. Alternatively, interactive multiobjective solution techniques could be used to allow planners to enhance decision making without excessive computational effort. This study describes an interactive multiple objective decision framework and evaluates its effectiveness via a multiobjective capacitated lot sizing model based on a real manufacturing facility. The results suggest that this approach is an effective solution strategy and useful decision aid for complex production planning problems.     

Cycle time-oriented mid-term production planning for semiconductor wafer fabrication

Wafers are produced in an environment with uncertain demand and failure-prone machines. Production planners have to react to changes of both machine availability and target output, and revise plans appropriately. The scientific community mostly proposes WIP-oriented mid-term production planning to solve this problem. In such approaches, production is planned by defining targets for throughput rates and buffer levels of selected operations. In industrial practice, however, cycle time-oriented planning is often preferred over WIP-oriented planning. We therefore propose a new linear programming formulation, which facilitates cycle time-oriented mid-term production planning in wafer fabrication. This approach plans production by defining release quantities and target cycle times up to selected operations. It allows a seamless integration with the subordinate scheduling level. Here, least slack first scheduling translates target cycle times into lot priorities. We evaluate our new methodology in a comprehensive simulation study. The results suggest that cycle time-oriented mid-term production planning can both increase service level and reduce cycle time compared to WIP-oriented planning. Further, it requires less modelling effort and generates plans, which are easier to comprehend by human planners.     

Designing the closed loop elements of a. material requirements planning system in a low volume,make-to-order company (with case study)

This paper describes how material requirements planning (MRP) can provide a working solution to job shop scheduling problems. In particular, the critical factors relating to resource and capacity management are examined within a low volume, make-to-order company. A rough cut capacity planning (RCCP) module is described which, when used with a detailed capacity management methodology, has enabled the collaborating company to achieve substantial savings in work-in-progress (WIP), a marked reduction in machining lead times and viable control over customer lead times. Finally, an MRP production methodology is described, which allows such concepts as unit cost justification and machine utilisation to be re-examined.