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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.

This paper reports the results of our collaborative work with Changde Telecommunication Equipment Company (CTEC) which produces digital transfer control systems for telephone networks. The manufacturing operations consist of two principle stages of module production and system assembly. Critical system components are produced in the module production stage where high inventories were a major managerial concern. We studied CTEC’s module production system and proposed three modifications to reduce inventories and improve system performance. The current system and the proposed modifications were simulated and compared in terms of various operational and financial criteria. The simulation was repeated in its entirety under a high level of demand due to CTEC’s anticipation of increased market share. The results, endorsed by CTEC’s management, demonstrate that significant operational and financial benefits can indeed be realized using the proposed modifications. This study provides motivation for the development of concepts and methodologies that will be useful in future theoretical and practical research.

This monograph will review recent thinking as applied to the management of materials within organisations. In considering the type of organisation to which the comments will apply, it is of use to recognise the following sectors:

Cell formation design in cellular manufacturing systems (CMS) has been the focus of recent manufacturing research literature. A great amount of research has been published addressing either technical issues (e.g. part‐machine grouping algorithms) or operational issues (e.g. planning and scheduling in the CMS). Research addressing strategic issues in cell formation design has been minimal. Addresses strategic considerations in cell formation design, specifically, the linkage and relationships between specific cell design issues and the firm′s competitive advantage in the marketplace. It is demonstrated that cell formation design decisions must be addressed in alignment with the firm′s strategic plan and manufacturing competitive priorities.

Most of the literature on just‐in‐time (JIT) production lines has focused on modelling the system into Markov‐chain. However, in practice, demands for products are known in a short planning horizon. Further, there is scarcity in the literature describing the dynamics and timing of material movements in JIT systems. Models a serial line into a discrete event dynamic system (DEDS) by a Max algebra approach incorporating the timing of material movements. Uses perturbation analysis to study the impact of parameter variation on the system performance. Compares the performances of push and pull systems using simulation analysis. The simulation results show that the pull system dominates the push system in lead time, in‐process inventories and stability of the production process.

The learning effect is well established in the repetitive performance of industrial tasks and it is essential that production management be aware of the potential for improved performance (measured by dynamic gain) and the rate at which this may be achieved/measured by the time constraint. Performance and contingency logging and trend modelling should be part of the management information system so that the appropriate learning curve can be established and stored in the databank for future planning purposes. The application of the learning curve to batch production shows that performance slippage caused by elapsed time between batch completions is a significant factor in manufacturing time increase, and, hence, labour costs. This should be allowed for when optimising batch production policy, forecasting deliveries, and

With an increasingly fierce competition of the shipbuilding industry, advanced technologies and excellent management philosophies in the manufacturing industry are gradually introduced to domestic shipyards. The purpose of this study is to promote the lean management of Chinese ship outfitting plants by lean production strategy.

To promote the lean implementation of Chinese shipyards, the lean practice of ship-pipe part production is highlighted by lot-sizing optimization and strategic CONWIP (constant work-in-process) control. A nonlinear programming model is formulated to minimize the total cost of ship-pipe part manufacturing and the particle swarm optimization (PSO)-based algorithm is designed to resolve the established model. Besides, the pull-from-the-bottleneck (PFB) strategy is used to control ship-pipe part production, verified by Simulink simulation.

Results show that the proposed lean strategy of the programming model and strategic PFB control could assist Chinese ship outfitting plants to leverage competitive advantage by waste reduction and lean achievement. Specifically, the PFB double-loop control strategy shows better performance when there is high productivity and the PFB single-loop control outperforms at lower productivity scenarios.

To verify the effectiveness of the proposed lean strategy, a case study is performed to validate the formulated model. Also, simulation experiments realized by FlexSim software are conducted to testify results obtained by the constructed programming model.

Lean production management practice of the shipyard building industry is performed by the proposed lean production strategy through lot-sizing optimization and strategic PFB control in terms of ship-pipe part manufacturing.

This study aims to synthesize Prussian blue {Fe^III₄[Fe^II(CN)₆]₃} pigment by reacting ferric chloride with different ferrocyanides through the same procedure. The influence of the ferrocyanide used on resulting pigment properties is studied.

Prussian blue is commonly synthesized by direct or indirect methods, through iron salt and ferrocyanide/ferricyanide reactions. In this study, the direct, single-step process was pursued by dropwise addition of the ferrocyanide into ferric chloride (both as aqueous solutions). Two batches – (K-PB) and (Na-PB) – were prepared by using potassium ferrocyanide and sodium ferrocyanide, respectively. The development of pigment was confirmed by an identification test and characterized by spectroscopic techniques. Pigment properties were determined, and light fastness was observed for acrylic emulsion films incorporating dispersed pigment.

The two pigments differed mainly in elemental detection owing to the dissimilar ferrocyanide being used; IR spectroscopy where only (Na-PB) showed peaks indicating water molecules; and bleeding tendency where (K-PB) was water soluble whereas (Na-PB) was not. The pigment exhibited remarkable blue colour and good bleeding resistance in several solvents and showed no fading in 24 h of light exposure though oil absorption values were high.

This article is a comparative study of Prussian blue pigment properties obtained using different ferrocyanides. The dissimilarity in the extent of water solubility will influence potential applications as a colourant in paints and inks. K-PB would be advantageous in aqueous formulations to confer a blue colour without any dispersing aid but unfavourable in systems where other coats are water-based due to their bleeding tendency.

THE change in the Institute's title has been accomplished very quietly, as if it were a matter of minor importance, the mere substitution of some words for others. If it were no more than that it would scarcely justify the time and trouble which it involved.

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.