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Since lean manufacturing considers that “Inventory is evil”, the purpose of this paper is to find and quantify the relations between work-in-process inventory (WIP), manufacturing lead time (LT) and the operational variables they depend upon. Such relations provide guidelines and performance indicators in process management.

The authors develop equations to analyse how, in discrete deterministic serial batch processes, WIP and LT depend on parameters like performance time (of each workstation) and batch size. The authors extend those relations to processes with different lots and the authors create a multiple-lot box score.

In this paper, the relations among WIP, LT and the parameters they depend on are derived. Such relations show that when WIP increases, LT increases too, and vice versa, and the parameters they depend on. Finally, these relations provide a framework for WIP reduction and manufacturing LT reduction and agree with the empirical principles of lean manufacturing.

Quantitative results are only exact for discrete deterministic batch processes without any delays. Expected results might not be achieved in real manufacturing environments. However, qualitative results show the underlying relations amongst variables. Different expressions might be derived for other situations.

Understanding the relations between manufacturing variables allows operations managers better design, implement and control manufacturing processes. The box score, implemented on a spreadsheet, allows testing the effect of changes in different operational parameters on the manufacturing LT, total machine wait time and total lot queue time.

The paper presents a discussion about process performance based on the mutual influence between WIP and LT and other variables. The relation is quantified for the discrete deterministic case, complementing the models that exist in the literature. The box score allows mapping more complex processes.

Facing keen worldwide competition, it is not enough for companies to pursue customer satisfaction; they must actively pursue customer delight. This paper seeks to design a work‐in‐process (WIP) exception handling system (WIPEHS) not simply measuring on‐time delivery performance for managers to take necessary improvement activities. It helps managers detect abnormal WIP levels in advance, trigger rectifying actions and finally notify pertinent people to coordinate roots causes and preventive means.

The structure of WIPEHS is proposed and then constructed with a soft package, Vigilance. A typical semiconductor factory is built and production data are simulated to evaluate the effectiveness of WIPEHS.

Collecting and analyzing results from the simulated typical semiconductor factory, the paper finds that the proposed system can effectively improve on‐time delivery performance; and that durations from a WIP exception detected a WIP exception back to normal and durations between two successive WIP exceptions significantly.

It helps factories outperform due dates, achieving significantly higher performance than prior performance without the production exception handling system, which should greatly please customers.

The proposed WIPEHS provide a total solution for undesirable production variations potentially harmful to due‐date performance. It anticipates WIP exception, notifies pertinent recipients, tracks the progress of exception resolution, and provides a forum for discussion of the root causes.

The aims of this paper is to investigate simulation‐based optimisation and stochastic dominance testing while employing kanban‐like production control strategies (PCS) operating dedicated and, where applicable, shared kanban card allocation policies in a multi‐product system with negligible set‐up times and with consideration for robustness to uncertainty.

Discrete event simulation and a genetic algorithm were utilised to optimise the control parameters for dedicated kanban control strategy (KCS), CONWIP and base stock control strategy (BSCS), extended kanban control strategy (EKCS) and generalised kanban control strategy (GKCS) as well as the shared versions of EKCS and GKCS. All‐pairwise comparisons and a ranking and selection technique were employed to compare the performances of the strategies and select the best strategy without consideration of robustness to uncertainty. A latin hypercube sampling experimental design and stochastic dominance testing were utilised to determine the preferred strategy when robustness to uncertainty is considered.

The findings of this work show that shared GKCS outperforms other strategies when robustness is not considered. However, when robustness of the strategies to uncertainty in the production environment is considered, the results of our research show that the dedicated EKCS is preferred. The effect of system bottleneck location on the inventory accumulation behaviour of different strategies is reported and this was also observed to have a relationship to the nature of a PCS's kanban information transmission.

The findings of this study are directly relevant to industry where increasing market pressures for product diversity require operating multi‐product production lines with negligible set‐up times. The optimization and robustness test approaches employed in this work can be extended to the analysis of more complicated system configurations and higher number of product types.

This work involves further investigation into the performance of multi‐product kanban‐like PCS by examining their robustness to common sources of uncertainties after they have been initially optimized for base scenarios. The results of the robustness tests also provide new insights into how dedicated kanban card allocation policies might offer higher flexibility and robustness over shared policies under conditions of uncertainty.

The purpose of this paper is to extend the role of the theory of constraints (TOC) to complement, reinforce, and help integrate conventional operations management (OM) concepts by using an Excel‐based version of the dice game discussed in The Goal by Goldratt.

The paper discusses the motivation for and the development and evaluation of an Excel‐based dice game model of a production system for novice managers to experiment with. A set of experiments related to OM concepts (e.g. inventory, capacity, and variability) is designed and counterintuitive results are discussed. The paper concludes by demonstrating how TOC provides an integrative OM framework.

The novel The Goal by Goldratt serves as a comprehensive case study in OM. The computerized dice game provides a mechanism for understanding relationships among various OM concepts. The proposed set of experiments strengthens the linkages between OM and TOC concepts. Managers can conduct additional experiments and predict/interpret the results without spending time in the logistics of setting up the manual dice game repeatedly.

The proposed dice game simulates a fairly simple serial production system so the generalization of results obtained might not be intuitively convincing for more complex production systems. More advanced OM concepts such as push (MRP) and pull (JIT) systems can easily be investigated using the underling logic of the dice game proposed here.

The model provides an innovative way to integrate TOC concepts with mainstream OM concepts and thereby, renews interest in OM.

Several versions of dice games, both manual and spreadsheet based, have appeared in the literature, however, none attempt to address as wide a variety of operations issues as the game proposed here.

This simulation study explores and compares the potential benefits of three work‐in‐process (WIP) inventory drive systems and their associated inventory buffer characteristics. The three inventory drives are a push, a pull and a hybrid push/pull system. While these systems have some aspects in common, their buffer management systems vary. The statistical analysis associated with the study was based on data gathered from three computer simulated flow‐shop assembly line environments. Hypotheses concerning the financial performance measurements were established. The independent variables were controlled and manipulated for each of the models. From the statistical analysis, a conclusion was drawn as to which system would afford the operation optimum results. While inventory has traditionally been considered and is currently being shown as an asset from an accounting point of view, it is obvious from the findings of this study, that excess WIP inventory, above the minimal requirements for production, will have a negative effect on the financial measurements evaluated in this study.

The purpose of this paper is to present a dynamic model to measure the degree of system’s leanness under dynamic demand conditions using a novel integrated metric.

The multi-stage production system model is based on a system dynamics approach. The leanness level is measured using a new developed integrated metric that combines efficiency, WIP performance as well as service level. The analysis includes design of experiment technique at the initial analysis to examine the most significant parameters impacting the leanness score and then followed by examining different dynamic demand scenarios. Two scenarios were examined: one focussed low demand variation with various means (testing the impact of demand volumes) while the second focussed on high demand variation with constant means (testing the impact of demand variability).

Results using the data from a real case study indicated that given the model parameters, demand rate has the highest impact on leanness score dynamics. The next phase of the analysis thus focussed on investigating the effect of demand dynamics on the leanness score. The analysis highlighted the different effects of demand variability and volumes on the leanness score and its different components leading to various demand and production management recommendations in this dynamic environment.

The presented lean management policies and recommendations are verified within the scope of similar systems to the considered company in terms of manufacturing settings and demand environment. Further research will be carried to extend the dynamic model to other dynamic manufacturing and service settings.

The developed metric can be used not only to assess the leanness level of the systems which is very critical to lean practitioners but also can be used to track lean implementation progress. In addition, the presented analysis outlined various demand management as well as lean implementation policies that can improve the system leanness level and overall performance.

The presented research develops a novel integrated metric and adds to the few literature on dynamic analysis of lean systems. Furthermore, the conducted analysis revealed some new aspects in understanding the relation between demand (variability and volume) and the leanness level of the systems. This will aid lean practitioners to set better demand and production management policies in today’s dynamic environment as well as take better decisions concerning lean technology investments.

The aim of this paper is to present the rationale, a numerical example and a case study of the application of an algorithm to convert non‐calendar based preventive maintenance (PM) schedules into calendar‐time format for semiconductor manufacturing systems (SMS). The resulting calendar‐time PM schedules can be utilized as a baseline within a PM scheduling optimization process.

The algorithm utilizes estimations of work‐in‐process (WIP) and system parameters to estimate an equivalent calendar‐time schedule for PM schedules based on different units. A numerical example based on fictitious data illustrates the utilization of the conversion algorithm within a mixed PM scheduling scenario, including wafer, processing‐time and energy‐based PM tasks for multi‐chamber tools. In addition, a case study illustrates the accuracy of the algorithm by comparing estimated PM targets (i.e. due, warning and late dates) with historical data from a real semiconductor fabrication facility.

Results from the case study validated the conversion algorithm by showing accurate estimations of PM targets (i.e. due, warning and late dates). The accuracy of the algorithm depends, however, on good estimates for WIP levels within the planning horizon.

The conversion algorithm may be utilized not only in SMS but also in other industries that require the conversion of non‐calendar based PM schedules into calendar‐time format for PM optimization and operational purposes.

Compares alternative buffer control systems used in a flow process, assembly line manufacturing system. Follows a literature analysis with the use of computer simulation to assess and measure the effects of each system on overall production performance, and individual workstation utilization.

A simulation model developed to study the performance of a JIT production planning system with respect to the reduction of inventory costs and customer delivery intervals is described by Cagatay Buyukkoc and Kiran M Rege of AT & T Bell Laboratories, USA.

Investigates the trade‐off between the average throughput rate and the average systems time using kanban discipline. Considers a multistage serial production line system with materials in the system controlled by kanban discipline. Presents simulation results to evaluate the production system performance in terms of the average throughput rate and the average system time for a fixed total number of kanbans over a given number of serial workstations. Constructs and compares efficient allocation sets for three and four workstations that are generated by kanban discipline for two processing time distributions, namely, uniform and exponential distributions. Based on the simulation results from three and four work‐stations, develops a general design rule to maximize the average throughput rate and to minimize the average system time. Analyses five and six workstations using the general design rule. Tests the validity of the general design rule by considering five and six workstations with a different number of kanbans. The results show that most of the efficient sets generated by the design rule are identical to those generated by enumerating all combinations of kanban allocations. However, using the general design rule reduces the simulation work tremendously.