An Integrated Materials Management System: Its Design and Formulation

An integrated management control system which makes use of quantitative business methodology is discussed. Areas where decisions need to be made and requiring assistance from the system are: distribution, production planning, inventory control and production sequencing. Quantitative techniques employed include time series forecasting, linear and integer programming, inventory and production sequencing theory.     

Mixed-Integer Linear Programming approaches to shelf-life-integrated planning and scheduling in yoghurt production

In the production of perishable products such as dairy, meat or bakery goods, the consideration of shelf life in production planning is of particular importance. Retail customers with relatively low inventory turns can benefit significantly from longer product shelf life as wastage and out-of-stock rates decrease. However, in today's production planning and control systems, shelf-life issues with regard to specific products or customers are only seldom accounted for. Therefore, the objective of this paper is to develop Mixed-Integer Linear Programming (MILP) models that integrate shelf-life issues into production planning and scheduling. The research is based on an industrial case study of yoghurt production. Relying on the principle of block planning, three different MILP models for weekly production planning are presented that apply a combination of a discrete and continuous representation of time. Overnight production and, hence, the necessity for identifying two different shelf-life values for the same production lot is included in the model formulation. Numerical experiments show that near-optimal solutions can be obtained within reasonable computational time.     

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     

Optimal production and pricing policies in a combined make-to-order/make-to-stock system

In this paper, we consider a production system which is capable to produce two types of products. The first type of products is make-to-order, while the second type is make-to-stock whose demand is satisfied by the on-hand inventory. The demand arrival rates of both types of products are price-sensitive. The excess demand that cannot be satisfied immediately is either backlogged or lost. The system costs include the holding costs of product inventories and shortage costs of unsatisfied demand. The objective is to maximise the total discounted profit over an infinite planning horizon by coordinating the production process and pricing decisions. By analysing the properties of objective functions, we characterise the optimal control policy by two switch curves and the optimal price is also given for different ordering and inventory levels. We also explore the monotonicity of both switch curves which will reduce the computation effort. Numerical experiments are conducted to demonstrate the use of the switch curves in managing the production system and illustrate that compared with the static pricing policy, the optimal integrated price and inventory control policy can result in a significant profit improvement in the make-to-order/make-to-stock system that is much higher than in a single-product system.     

Scheduling manufacturing systems with work-in-process inventory control: Reentrant systems

In this paper, we propose a procedure for production flow control in reentrant manufacturing systems. The system under study consists ofN machines and producesM product types simultaneously. Each part goes through the system following a predefined process and may visit a machine many times. All machines are subject to random failures and need random repair times. The scheduling objectives are to keep the production close to demand and to keep the WIP inventory level and cycle times at low values. The model is motivated by semiconductor fabrication production. A three-level hierarchical controller is constructed to regulate the production. At the top level of this hierarchy, we perform capacity planning by selecting the desirable buffer sizes and the target production level for each operation. A production flow rate controller is at the middle level which recalculates the production rates whenever a machine fails or is starved or blocked. The loading times for individual parts are determined at the bottom level of the hierarchy. Comparison with alternative control is made through simulation and it shows that the control policy performs well.     

Production planning system for a combination of make-to-stock and make-to-order products

This paper deals with the problem of designing a production planning system for a combination of make-to-stock (MTS) and make-to-order (MTO) products where the production facility produces both types of products. Important management points in such production systems are to design an efficient production planning system which can shorten the manufacturing time of MTO products as well controlling the unfilled rate of MTS products to the market demand at a low level. A hierarchical production planning model is introduced in order to design the production planning system. The buffer capacity is set as a design variable for determining production capacity at a higher planning level, and the rule for allocating the production capacity to types of products is adopted as a design variable at a lower level. First, we analyse how these two design variables affect the unfilled rate of MTS products and the average manufacturing time of MTO products. Second, we clarify the relationship of the buffer capacity with the manufacturing time of MTO products and with the required buffer inventory level of MTS products which can maintain the unfilled rate of MTS products to the market demand within an acceptable limit. Third, we show an example of the range of design variables which can control the unfilled rate of MTS products and the manufacturing time of MTO products within their acceptable limits, if the individual limits of the performance measure is given to respond to customer orders quickly.     

A literature survey on planning and control of warehousing systems

We present a literature survey on methods and techniques for the planning and control of warehousing systems. Planning refers to management decisions that affect the intermediate term (one or multiple months), such as inventory management and storage location assignment. Control refers to the operational decisions that affect the short term (hours, day), such as routing, sequencing, scheduling and order-batching. Prior to the literature survey, we give an introduction into warehousing systems and a classification of warehouse management problems     

Optimal inventory costs in a complementary flexible manufacturing system

Flexible manufacturing systems (FMS) have provided job shop-type operations a variety of solutions to their widely documented inefficiencies. FMS come with a wide spectrum of capabilities and are used in a wide variety of manufacturing environments. The present study proposes there are situations where inventory control issues need to be considered during the FMS set-up planning. It has developed a non-linear mathematical programming model, which also has binary variables, to optimize inventory costs during the set-up planning for a sample FMS installation. The solution to the mathematical model is non-trivial. It shows that using this model in conjunction with analytical methods such as constraint propagation and generate-and-test, and sigmoid function approximations, facilitates robust solutions to some of the subproblems of the FMS set-up problem. The study also provides an exchange curve based framework for analysing the implications of inventory policy variables on decisions made at the FMS set-up stage.     

Dynamic scheduling for complex engineer-to-order products

The current paper considers dynamic production scheduling for manufacturing systems producing products with deep and complex product structures and complicated process routings. It is assumed that manufacturing and assembly processing times are deterministic. Dynamic scheduling problems may be either incremental (where the schedule for incoming orders does not affect the schedule for existing orders) or regenerative (where a new schedule is produced for both new and existing orders). In both situations, a common objective is to minimize total costs (the sum of work-in-progress holding costs, product earliness and tardiness costs). In this research, heuristic and evolutionary-strategy-based methods have been developed to solve incremental and regenerative scheduling problems. Case studies using industrial data from a company that produces complex products in low volume demonstrate the effectiveness of the methods. Evolution strategy (ES) provides better results than the heuristic method, but this is at the expense of significantly longer computation times. It was found that performing regenerative planning is better than incremental planning when there is high interaction between the new orders and the existing orders.     

Performance analysis of re-entrant manufacturing networks under surplus-based production control

Motivated by the problem of planning and scheduling in large semiconductor manufacturing facilities we study the performance of re-entrant production networks. In such networks, each product has to go multiple times through the entire manufacturing line before it is ready. Each machine in a re-entrant network can receive products from different routings, but the products from only one of the routings can be served at a time. This complicated network structure makes it difficult, and at the same time important, to estimate in advance the performance of such a network under a demand-driven control policy. In this paper we present results on the influence of perturbations, buffer inventory levels and the number of manufacturing stages on the production tracking error of each machine in the re-entrant network operated under a surplus-based production control policy. The results are provided in the form of so-called ‘worst case scenario’ bounds on the steady-state production demand tracking accuracy of each stage as well as bounds on the content of each intermediate inventory level. By means of simulation examples we show that the results obtained have an important meaning and can be used as a practical reference tool.     

Process and production planning for circuit board assembly

Process planning output can be post-processed into criteria for job scheduling decisions in printed circuit board assembly using surface-mount technology (SMT). Artificial intelligence-based techniques used in computerized planning and scheduling systems in other industries can be extended to printed circuit board operations. These techniques include blackboard architectures, object-oriented programming systems, and nonmonotonic reasoning systems. These techniques were used to demonstrate a unique architecture of blackboard systems that communicate via object-oriented messages to arrive at a shop-floor process plan and production schedule. The methodology was specialized to the assembly of printed circuit boards using surface-mounted components in a high-variety/low-volume product mix. The assembly facility was represented as a hierarchical object of product, process, and organizational knowledge. The system of working heuristics was integrated within a prototype environment with the practitioners that assisted in its development. The end result is a good working methodology for system development, implementation, and maintenance by knowledge worker involvement.     

Disaggregate Planning For Parallel Processors

Successful aggregate planning and effective production scheduling both rely heavily upon the availability of sound disaggregation procedures. This paper describes a large scale mathematical program that translates long range plans into operational assignments of labor, production and inventory among competing products and processes. The standard lot sizing problem, characterized by marginal tradeoffs between holding costs and setup costs, is constrained further as multiple processors compete for efficient assignments to individual products. The model's application in a major manufacturing corporation is described.     

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.     

Planning stability in a product recovery system

Recovery of used products is an issue of growing importance due to customer expectations and environmental regulation. As a consequence, companies need to adapt their material management taking into account inbound flows of used products. Corresponding inventory control models have been proposed in literature. In this paper we address the issue of planning stability in a product recovery context. To this end, we consider rolling horizon planning for a stock point facing stochastic demand and product returns. We analyze the impact of the return flow on planning stability and compare the system behaviour with a traditional production environment. We show that structural results derived for traditional inventory models remain valid in a product recovery context. Moreover we discuss counterintuitive effects resulting from interaction between planning stability and stock levels.     

Work-in-process clearing in supply chain operations planning

In this study we provide insights into the effectiveness of the clearing function concept in a hierarchical planning context. The clearing function is a mathematical representation of the relation between the Work-In-Process (WIP) and the throughput of a production process. We use it in a deterministic order release planning model to anticipate the dynamics of the operational level, which is subject to uncertainties. A multi-period single-item order release and scheduling problem is considered in which the delivery schedule of the orders is determined through the planned lead times, and the capacity loading decisions are separated from the order release decisions in a way so as to plan for on-time deliveries. Early or late delivery of the orders, which are released and delivered in batches, has not been considered explicitly in previous studies on clearing functions, and it significantly affects the inventory costs. Both linear and non-linear clearing functions are tested using a simulation approach. The results indicate that modeling the clearing of WIP by a shop should be based on the short-term operational dynamics of the shop rather than on the long-term average shop behavior, since it improves the consistency between the operational planning and scheduling levels of the hierarchy. The presented methods and results provide valuable information on modeling production characteristics in aggregate production planning and scheduling models.     

Operationalising form postponement from a decision-making perspective

Form postponement means delaying the commitment of inventory to the final configuration of a product as long as possible. Many firms today are striving to redesign their products and/or their manufacturing and supply chain processes to implement form postponement. Opportunities for form postponement, however, are sometimes lost in the companies’ production-planning processes. By focusing on the deferring of product mix decisions in the master production scheduling process, this paper shows that form postponement opportunities can be divided into two components: one whose pursuit necessarily requires product and/or transformation process redesign, the other that can be pursued by changing the sales forecasting and master production scheduling process alone. We develop an operational procedure to identify and quantify, for a given product family, all opportunities for form postponement and their two respective components. Then, we discuss and empirically illustrate how the proposed measurement procedure may support companies in changing their decision-making routines to implement form postponement. Finally, we set future research directions on form postponement suggested by our results.     

Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems

An important issue in the manufacturing and supply chain literature concerns the optimisation of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions and settings. However, as systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimising single inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such a complex application area. Decisions involving the interrelated product inventories and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be readily obtained. We propose the methodology of approximate dynamic programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup simulation approach, ADP develops policies by utilising only a fraction of the computations required by classical dynamic programming. However, there are few studies in the literature that optimise production decisions in a stochastic, multi-factory, multi-product inventory system of this complexity. This paper aims to explore the feasibility and relevancy of ADP algorithms for this application. We present the results from numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies and to policies derived from a deterministic approximation of the problem.     

A capacity-oriented hierarchical approach to single-item and small-batch production planning using project-scheduling methods

Most production planning and control (PPC) systems used in practice have an essential weakness in that they do not support hierarchical planning with feedback and do not observe resource constraints at all production levels. Also, PPC systems often do not deal with particular types of production, for example, low-volume production. We propose a capacity-oriented hierarchical approach to single-item and small-batch-production planning for make-to-order production. In particular, the planning stages of capacitated master production scheduling, multi-level lot sizing, temporal and capacity planning, and shop floor scheduling are discussed, where the degree of aggregation of products and resources decreases from stage to stage. It turns out that the optimization problems arising at most stages can be modelled as resourceconstrained project scheduling problems.     

Parallel machine,capacitated lot-sizing and scheduling for the pipe-insulation industry

We develop a solution procedure for a production problem that involves the optimal lot-sizing and scheduling of multiple products on parallel machines over a long planning horizon. The objective is to determine the product assignment, production quantities, and production sequence on each machine in order to meet demand and maximise the total production amount in a given planning horizon. We study this problem in the context of the manufacturing of pipe insulation, in which multiple features have to be considered simultaneously. The solution procedure combines mathematical programming and a post-processing sequencing heuristic. We also develop a procedure to determine safety stock levels and use Monte Carlo simulation to assess the risk associated with inventory shortages due to stochastic production rates. Computational tests are performed on both real and artificially-generated data.     

Effective allocation of idle time in the group technology economic lot scheduling problem

The group technology economic lot scheduling problem (GT-ELSP) addresses the issue of scheduling the products, classified into groups, on a single facility. The objective is to develop a schedule that minimises the sum of average inventory holding and setup costs over the infinite planning horizon. Previous research on this class of problems has assumed the cycle idle time to be equally divided among various sequence positions and has varied the frequency of production of individual products. We propose a heuristic that maintains constant frequency of products and varies the idle time among the sequence positions. We address the feasibility issue of the GT-ELSP and show that our heuristic, based on the time varying lot size approach, yields low-cost quality solutions that aid practical implementation. We tested our heuristic on a large set of randomly generated problems to assess its efficacy over the solutions of earlier proposed heuristics.