Production and inventory control at ABB Motors and Volvo Wheel Loaders,two examples of MRP in practical use

The paper presents the main routines of production and inventory control at ABB Motors, Västerås and Volvo Wheel Loaders, Eskilstuna. The primary interest is to present how Material Requirements Planning (MRP) is used in these two companies. If the number of end items is large, the company assembles-to-order or makes-to-order then additions to pure MRP seem to be necessary. ABB Motors and Volvo Wheel Loader use: planning bills, a normal bill of materials with 'adding bills of materials', a master production schedule planning system with an available-to-promise function and a home-made 'system' for modules available-to-promise. One important measure for the both companies is the accumulative lead-time. An increase of the Master Production Schedule in a shorter time than the accumulative lead-time is avoided, because it will lead to suggestions of purchase in past time periods and therefore most probably to future material shortages.     

Newsvendor Problems with Demand Shocks and Unknown Demand Distributions

In today's competitive market, demand volume and even the underlying demand distribution can change quickly for a newsvendor seller. We refer to sudden changes in demand distribution as demand shocks. When a newsvendor seller has limited demand distribution information and also experiences underlying demand shocks, the majority of existing methods for newsvendor problems may not work well since they either require demand distribution information or assume stationary demand distribution. We present a new, robust, and effective machine learning algorithm for newsvendor problems with demand shocks but without any demand distribution information. The algorithm needs only an approximate estimate of the lower and upper bounds of demand range; no other knowledge such as demand mean, variance, or distribution type is necessary. We establish the theoretical bounds that determine this machine learning algorithm's performance in handling demand shocks. Computational experiments show that this algorithm outperforms the traditional approaches in a variety of situations including large and frequent shocks of the demand mean. The method can also be used as a meta‐algorithm by incorporating other traditional approaches as experts. Working together, the original algorithm and the extended meta‐algorithm can help manufacturers and retailers better adapt their production and inventory control decisions in dynamic environments where demand information is limited and demand shocks are frequent      

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

Economic manufacturing quantities under conditions of learning and forgetting (EMQ/LaF)

Abstract

Abstract. It has been empirically observed that productivity improves as production continues due to system 'learning’, but that it deteriorates once the activity is stopped due to system 'forgetting’. Both learning and forgetting follow an exponential form with a 'doubling factor’ ranging between 0.75 and 0.98. We review and critique two previously proposed models, correct some minor errors in them, and expand one of them to accommodate a finite horizon. We also propose a new model that is more in harmony with the established learning function, for the determination of the optimal number and size of the lots in the finite and infinite horizon. The methodology used throughout is dynamic programming. We investigate the impact of all three models on the optimal lot sires and their costs, and establish the functional relations between the total cost and the various factors affecting them.     

Adaptive/predictive scheduling: review and a general framework

Abstract

Abstract. Effective production scheduling can have a dramatic impact on manufacturing productivity. In modern automated manufacturing systems, the ability to revise and adjust the schedule can help reduce indirect costs which comprise a large portion of manufacturing costs in those systems. The objective of the research discussed here has been to study the feasibility of automatically scheduling multi-machine complexes and adjusting the schedule on a real-time basis by a unified computer system. We arc investigating selective real-time schedule adaptation by distinguishing between schedule generation and regeneration tasks (which might not be cost-effective) from schedule adaptation/recovery tasks (basically, causing less cosily disruption of the original plans). The article describes our general framework for automatic adaptive/predictive scheduling that includes five main functions: scheduler; monitor;comparator; resolvcr; recovery adaptor. Some experimental results demonstrating the effectiveness of the framework are discussed.     

Scheduling algorithms for computer-aided line balancing in printed circuit board assembly

Generalized flexible flow line (GFFL) is a scheduling environment comprising several machine banks which the products visit in the same order but can skip some machine banks. The type of machines in a bank can differ but they are suitable for performing the same manufacturing tasks. To change one product to another demands a set-up operation of the machine. This paper describes several scheduling algorithms for the GFFL problem. The overall structure of these algorithms is similar, consisting of machine allocation and sequencing phases. The algorithms have been integrated into an interactive production scheduling system for electronics assembly. Sample cases are used to illustrate the operation of the system in practice.     

The impact of random machine unavailability on inventory policies in a continuous improvement environment

There are production situations where a production facility (e.g. a machine) is used intermittently to produce lot sizes of certain products. Upon completion of production run, the facility may not be available for a random amount of time due to several reasons, such as: the facility needs to be maintained and the maintenance time is random due to unforeseen circumstances; or that the facility is leased by different manufacturers and the demand for the facility is random. As a result of machine unavailability, stock-out situations might arise. This paper extends the work of Abboud et al . (2000, Computers and Operations Research , 27 , 335-351) by assuming learning and forgetting in production. A new mathematical model is developed with numerical examples and sensitivity analysis provided. Furthermore, this paper determines how the overall inventory cost is influenced by the nature of the random variable that represents the unavailability time of the production facility.     

Telepresence in cross-site business process simulation - lessons learnt in technology,social interaction and organizational learning

Re-engineering global business processes requires innovative methods for inter-organizational learning. This article describes and analyses an experimental telepresence process simulation that was applied to develop and train the cross-site New Product Introduction process of a global telecommunication company. Interactive, real-time audio and video connections over the Internet supported the process-oriented discussion between two groups in distant locations. Identical visual process charts and other information, projected on the walls of the two auditoriums, helped the two teams to discuss and understand the process. Audibility of the discussion was crucial for learning, but the visual 'talking heads' of the participants distorted concentration. Facilitation at both ends and local simulation periods smoothed the interaction and the imbalances between the locations. The simulation achieved positive effects in immediate learning and in motivation for change, although the telepresence caused some limitations. The ultimate goal, the feeling of real presence, was not reached. But when time gets more expensive, and travelling less preferable, telepresence simulation can be a feasible group-mode e-learning method for inter-organizational learning.     

Flow shop scheduling by transportation model

This paper describes the classical problem of scheduling n jobs on m machines in a flow shop. A schedule evaluation algorithm is presented, which for job-pairs, generates a schedule evaluation matrix. The matrix is the input data to a transportation problem, the solution of which gives near-optimal jobsequence and makespan. The performance of the algorithm is discussed.     

Dynamic scheduling of manufacturing job shops using extreme value theory

Most job shop scheduling approaches reported in the literature assume that the scheduling problem is static (i.e. job arrivals and the breakdowns of machines are neglected) and in addition, these scheduling approaches may not address multiple criteria scheduling or accommodate alternate resources to process a job operation. In this paper, a scheduling method based on extreme value theory (SEVAT) is developed and addresses all the shortcomings mentioned above. The SEVAT approach creates a statistical profile of schedules through random sampling, and predicts the quality or 'potential' of a feasible schedule. A dynamic scheduling problem was designed to reflect a real job shop scheduling environment closely. Two performance measures, viz. mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. Three factors were identified, and varied between two levels each, thereby spanning a varied job shop environment. The results of this extensive simulation study show that the SEVAT scheduling approach produces a better performance compared to several common dispatching rules.     

Simulation-based production schedule generation

Abstract

While simulation has been used in manufacturing for many years, predominantly for facility design, it is within the last few years, that simulation languages have been developed to a point where they can be used on a day-to-day basis to generate schedules and predict their performance. This paper describes our use of different modelling techniques to develop a production schedule generation system. An example describing a system for a small- to medium-sized order producing company using SIMAN/CINEMA is included. While addressing the shortcomings of existing scheduling systems it is shown how the approach taken is a feasible way of creating a dynamic goal-driven simulation-based production scheduler. The paper does not aim to describe an ‘off the shelf’ scheduling system product, but rather to give an overview to methods, techniques and experiences which enable us rapidly to tailor a simulation-based scheduling system to the specific needs of a company.     

Effect of forecast errors on rolling horizon master production schedule cost performance for various replanning intervals

Master production schedules are usually updated by the use of a rolling schedule. Previous studies on rolling schedules seem to form the consensus that frequent replanning of a master production schedule (MPS) can increase costs and schedule instability. Building on previous research on rolling schedules, this study addresses the impact of overestimation or underestimation of demand on the rolling horizon MPS cost performance for various replanning frequencies. The MPS model developed in this paper is based on actual data collected from a paint company. Results indicate that under both the forecast errors conditions investigated in this study, a two-replanning interval provided the best MPS cost performance for this company environment. However, results from the sensitivity analysis performed on the MPS model indicate that when the setup and inventory carrying costs are high, a 1-month replanning frequency (frequent replanning) seems more appropriate for both of the above forecast error scenarios.     

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.     

Reliability analysis of man–machine systems using fuzzy cognitive mapping with genetic tuning

This article offers a method for analyzing the reliability of a man–machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of “influencing factors—reliability,” the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the “driver–car–road” system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly.     

Robustness evaluation of multisite distributed schedule with perturbed virtual jobshops

A method for evaluating the robustness of medium term multisite distributed schedule is presented. The method of robustness evaluation is based on steps and tools associated with the conceptual model of a MultiSite Reactive Production Activity Control (MSR-PAC). The MSR-PAC makes it possible to study the sensitivity of the scheduled plans in presence of perturbations. These must be evaluated before being dispatched to the shop-floors. At present, to the best of the authors' knowledge, there is no means to make this evaluation in the distributed systems such as extended enterprise. The monitoring of errors is based on the discrepancy between the two flow-shape functions that model respectively the dynamics of the scheduled manufacturing orders and the state of the perturbed production. The MSR-PAC is based on a multisite agent system and on the monitoring of the perturbed virtual jobshops. The method can also be used for controlling short term distributed production activities.     

Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.     

The effects of transportation lead-time and safety stock in international co-operative knockdown production systems

Abstract

Abstract. In international co-operative knockdown production systems (ICKDPS), much longer transportation lead-time than production lead-time to procure component parts is required, because the parts are generally containerized by ship. In this paper, in order to determine an economical buffer stock level for a more reliable ICKDPS, a pull-type ordering system which considers the backorder for compensating for shortage of component parts is developed for the ICKDPS as a three-stage production, inventory and transportation system with the concept of 'just-in-time’ production systems. In addition, the effects of transportation lead-time and buffer stock level on the quantity of backlogs and the probability of requirement are clarified by simulation.     

Production scheduling for apparel manufacturing systems

In this paper, we model an apparel manufacturing system characterized by the co-existence of the two production lines, i.e. traditional, long lead time production line and flexible, short lead time production line. Our goal is to find strategies which decide: (i) the fraction of the total production capacity to be allocated to each individual line, and (ii) the production schedules so as to maximize the overall profits. In this problem, searching for the best solution is prohibited in view of the tremendouscomputing budget involved. Using ordinal optimization ideas, we obtained very encouraging results not only have we achieved a high proportion of 'good enough' designs but also tight profit margins compared to a pre-calculated upper bound. There is also a saving of at least 1/2000 of the computation time.     

A machine-learning approach for a sintering process using a neural network

This paper presents a machine-learning approach using a multi-layered neural network (NN) with application to a sintering process in an iron- and steel-making plant. Our method induces 'operational rules' that determine operational conditions to obtain products that meet a given quality specification. In our application, an operational condition decides the appropriate ranges of chemical composition and heat input to obtain sinter with desirable properties. Our approach consists of two stages. First, backpropagation (BP) training is performed to obtain a NN which decides whether a given condition is appropriate or not. Secondly, from the trained NN, we extract rules which explain what operational conditions are appropriate. In spite of the effective learning capability, a major drawback of a NN is 'unreadability' of the learned knowledge, or the lack of an explanatory capability, which is crucial in the second stage. We developed a rule extraction algorithm which contributes to overcoming this 'unreadability'. The extracted rules are found to agree well with the knowledge in material science.     

Allocation of buffer capacity to minimize average work-in-process

This paper proposes an efficient heuristic algorithm to solve the problem of allocating the buffers in a production line for given total buffer capacity and the minimum required throughput. While the traditional research has focused on the problem of allocating the buffer capacity to maximize the throughput, the objective of minimizing the average Work-In-Process (WIP) is considered. Minimization of the WIP in the production line has drawn much attention in many practical applications, due to the growing efforts in reducing the production lead times for faster customer response. Two heuristics are proposed and compared to solve the problem. One is a very simple heuristic approach, and the other is a modified 'Non-SEVA' (Non-Standard Exchange Vector Algorithm) which was originally proposed by Seong et al . ( International Journal of Production Research, 33, 1989-2005, 1995) for the throughput maximization problem. Through computational tests, the efficiency of the proposed approach is shown.