Maurice Bonney

Production breaks and the learning curve: The forgetting phenomenon

Abstract In this paper, the forgetting slope is shown to be mathematically dependent on the following factors: 1. (1) the learning slope, 2. (2) the quantity produced to date, and 3. (3) the minimum break at which total forgetting occurs. It is also shown that it is possible to determine the value of the forgetting rate once the curves mathematical form is assumed. This answers some potential considerations inherent in the arbitrary assumption of forgetting rate. This paper also investigates the effects of learning and forgetting on both the optimum production quantity and the minimum total inventory system cost.

The economic manufacture/order quantity (EMQ/EOQ) and the learning curve: Past, present, and future

Traditionally, inventory has been viewed as an asset, one that can be converted to cash. In recent years there has been a move towards the use of JIT production methods. The JIT view is that inventory does not add value but instead incurs costs, and thus is a waste. The JIT concept of continuous improvement applies primarily to a repetitive manufacturing process where the learning phenomenon is present. This paper surveys work that deals with the effect of learning on the lot-size problem. It also explores the possibility of incorporating some of the ideas adopted by JIT to such models.

LOT SIZING WITH LEARNING AND FORGETTING IN SET-UPS AND IN PRODUCT QUALITY

Abstract Managers at manufacturing firms make every effort to improve the performance of their operations through the adoption of continuous improvement programmes, e.g. reducing set-ups times, increasing production capacity and eliminating rework. The learning curve can be used to describe and predict such improvements. This paper investigates the effects that learning and forgetting in set-ups and product quality have on the economic lot-sizing problem. Two quality-related hypotheses were empirically investigated: (1) The time to rework a defective item reduces if production increases conform to a learning relationship, and (2) quality deteriorates as forgetting increases due to interruptions in the production process. Mathematical models are developed and numerical examples illustrating the solution procedure are provided.

Environmental performance measures for supply chains

Purpose – The paper seeks to develop an analytical decision model that is used to investigate the performance of a supply chain when product, process, and environmental quality characteristics are considered.Design/methodology/approach – Environmental performance measures and methods to quantify quality are reviewed and then used to develop a method to measure environmental quality and its associated costs. This was translated into a two‐level supply chain coordination model that captures most aspects of green supply chains. Numerical examples are provided and solved using Excel Solver enhanced with VBA codes.Findings – The results confirmed some findings in the literature that investing to reduce environmental costs improves environmental performance and increases total profits.Research limitations/implications – The environmental quality cost function that was used was of a form that guarantees a global optimal solution. A limitation is that the function may take more complex forms where different analy...

A comparative study of learning curves with forgetting

Abstract Although there is almost unanimous agreement that the form of the learning curve is as presented by Wright,1 scientists and practitioners have not yet developed a full understanding of the behavior and factors affecting the forgetting process. Mathematical models of the forgetting process are reviewed, and then three models, the VRIF, VRVF, and LFCM models are compared and their differences and similarities are discussed.

The application of discrete linear control theory to the analysis and simulation of multi-product, multi-level production control systems

This paper examines how multi-product, multi-level production control systems may be represented in terms of discrete linear control theory models. The transfer functions are derived for material requirements planning (MRP) and re-order cycle systems. The performance of each type of system is derived under selected conditions and the way in which a general system may be analysed is described. The method has proved easy to use. Computer programs exist which enable one to assemble the elements of proposed forecasting, inventory and production control systems, and hence determine the stability and transient and steady state responses to standard inputs. This has proved to be a quick and efficient way of evaluating alternative production systems and examining whether better methods of, for example, stock re-ordering or forecasting are required.

Are push and pull systems really so different

A distinction is frequently made between push and pull production planning and control systems. Many people believe that pull systems are inherently better at reducing stocks because they try to eliminate queues, not provide for them, whereas push systems encourage queues to cushion operations and to increase work station utilisation but at higher cost. However, the definitions of push and pull are inconsistent between different researchers. Worse, arguments about performance are sometimes circular. Thus, if the performance of a pull system is poor then it may be suggested that this is because the fundamentals of JIT are not being observed, whereas, if the performance of a push system is poor, then that is a consequence of it being a push system. After defining push and pull systems, this paper examines, by means of simulation, the effect that push and pull information flows have on system performance, under a variety of conditions. In particular, the performance of both push and pull information flow systems are considered in conjunction with high-quality levels, small set-ups and small batches, i.e. the conditions normally associated with JIT continuous improvement programmes. Similarly, the performance of both push and pull information flow systems are investigated in the presence of conditions such as large set-up times, which are frequently eliminated as part of a continuous improvement programme. The question investigated in this study is how system performance is affected by the flow of control information. The investigation uses models of the material and information flows of push and pull systems to examine the conditions which affect performance. A production sequence is chosen which consists of ordering materials, making parts and assembling products which are then despatched to customers. A set of decision rules is used to operate the systems using different demand and inventory level data.

Trends in inventory management

Abstract Inventory management is one of the success stories of recent years and it is changing rapidly in response to international competition and new technology. This paper examines some of these developments. Inventory is a major investment in most companies. It strongly influences the internal flexibility of a company, e.g. by allowing production levels to change easily and by providing good delivery performance to customers. Yet inventory ties up working capital and space and it can suffer from obsolescence, deterioration and shrinkage. It can also add to administrative complexity. In recent years attention in manufacturing industry has concentrated on an ‘inventory is waste’ philosophy using JIT production, usually accompanied by visible ‘pull’ or consumer demand driven systems. The approach is also very effective in supermarket retailing and, at its best, provides very high stock turn and high profits to the company at the same time as providing good service and fresh items to customers at low cost. Current changes in inventory management consider the total logistics chain under the term logistics management, place a greater emphasis on purchasing rather than producing in-house and use more international sourcing. Changes to recording methods include the use of different methods of information collection and processing, e.g. bar coding in retailing and manufacture and electronic exchange of information. Control methods are more computer based and are becoming part of increasingly integrated systems. There are some obvious problems still to be solved. Procedures are needed to bring one-off analyses of inventory to become part of routine systems, work needs to be done to produce performance measures which are consistent between different levels of the organisation and the modelling of dynamic performance needs to become part of the design of our production-inventory systems. More fundamentally, we still do not know how to classify companies, let alone how to determine the best approach to inventory planning and control for a particular kind of company.

The effects of learning and forgetting on the optimal lot size quantity of intermittent production runs

This paper studies the effects of learning and forgetting on the production lot size problem with infinite and finite planning horizons. It is assumed that the determination of the economic manufactured quantity (EMQ) in the succeeding production run is dependent on: (1) the maximum inventory accumulated prior to interruption; (2) the length of the interruption period which incurs total forgetting; and (3) the level of experience in equivalent units remembered at the start-up of the next production run. The optimum operating inventory doctrines is obtained by trading off procurement cost per unit time and the inventory carrying cost per unit time, so that their sum will be a minimum. A numerical example is presented to demonstrate the application of learning and forgetting to the determination of the EMQ.

Effect of errors and delays in inventory reporting on production system performance

Abstract A discrete linear control representation has been used by the authors to examine the performance of multi-level, multi-product production control systems operating in an environment where system errors are expected. The basic method was reported in [1] and its use to examine system structures and system implementation sequences was discussed in [2]. Production control systems operate in an uncertain environment. Besides the commonly considered uncertainties in demand, yield, quality and delivery times, errors frequently exist in basic data such as bills of materials and occur in stock records as a result of incorrect or late transaction recording. This paper presents the basic discrete linear control representation of MRP and ROC systems, shows how these representations may be modified to include certain types of internal system error, before examining the effects of delays in stock recording and of recording errors on the performance of the system. Performance measures include the effects on schedules, planned stock and delivery performance.