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JIT is a manufacturing philosophy, successfully implemented mostly in large‐scale industries. Research reviews indicate it is rarely adopted in SMEs; there appear to be problems in promoting and deploying the concept of JIT to such organizations. Reports on a study performed in SMEs to select and prioritize various techniques for the implementation of JIT in a seasonal order‐manufacturing environment through the QFD technique. Analysis indicates that buffer stock removal and lot size reduction were the key issues to be assigned higher priority than throughput improvement. However, this prioritization has to be carried out on a case‐by‐case basis as each manufacturing system has different predefined goals and targets, and is set within a different context.

The purpose of this paper is to develop an optimal model of an integrated quality and safety management system (QSMS).

Keywords related with these systems were identified from international standards and subsequently mined from a selection of peer reviewed articles that discuss and propose varying forms of integrated models for both systems. Cluster analysis was used to establish the degree to which integrated models, as described in the articles were quality dominant vs safety dominant. Word counts were utilized for establishing content and attributes for each category. An optimal integrated model was developed from the final cluster analysis and substantiated by a one-way analysis of variance. Experts from industry were consulted to validate and fine-tune the model.

It was determined that characteristics of an optimal integrated model include the keywords “risk,” “safety,” “incident,” “injury,” “hazards,” as well as “preventive action,” “corrective action,” “rework,” “repair,” and “scrap.” It also combines elements of quality function deployment as well as hazard and operability analysis meshed into a plan-do-check-act type work-flow.

Given the vast array of clustering algorithms available, the clusters that resulted were dependent upon the algorithm deployed and may differ from clusters resulting for divergent algorithms.

The optimized model is a hybrid that consists of a quality management system as the superordinate strategic element with safety management system deployed as the supporting tactical element. The model was implemented as a case study, and resulted in 13 percent labor-hour saving.

The purpose of the paper is to study effect of the implantation of oxygen and helium ions on the corrosion performance of the AISI3l6L stainless steel. It presents useful new results which allows one to draw conclusions as to the suitability of the helium and oxygen ion implanted AISI 316L stainless steel for biomedical use in the body.

The implantation of oxygen and helium ions was done on AISI 316L SS at an energy level of 100 keV at a dose of 1×10¹⁷ ions/cm², at room temperature. In order to simulate the natural tissue environment, an electrochemical test using cyclic polarization was done in a 0.9 percent sodium chloride solution at a pH value of 6.3 at 37°C. This was carried out on both the virgin and implanted AISI 316L stainless steel for the purpose of comparing performance. In addition to this, the hardness of the virgin and implanted samples was also studied using Vickers microhardness tester with varying loads. Besides, the surface morphologies of the implanted samples and the corroded samples were studied with XRD and SEM.

From the study the following findings are made. First, the XRD and SEM results were found to be in accordance with the corrosion test results. Second, the general corrosion behavior showed a significant improvement in the case of both helium implanted (icorr=0.0689 mA/cm²) and oxygen implanted (icorr=1.104 mA/cm²), when compared to the virgin AISI 316L SS (icorr=1.2187 mA/cm²). The pitting corrosion showed a significant improvement for helium implanted (Epit=230 mV) when compared to virgin material (Epit=92 mV). The oxygen implanted has not shown any improvement (Epit=92 mV). The surface hardness is found to be 1202 HV for helium implanted and 1020 HV for oxygen implanted, while it is found to be 195 HV for the virgin material. The hardness of the helium and oxygen implanted samples is found to be increased by about 600 percent and 500 percent, respectively, when compared to the virgin samples. Helium implanted samples show better performance in terms of corrosion resistance and hardness when compared to those of the oxygen implanted samples.

Although a number of authors have conducted many research on AISI 316L stainless steel, this work has original experimental results in terms of the oxygen and helium ion implantation parameters used and the specific tests: microhardness, electrochemical corrosion test, SEM and XRD that were used. It thus presents useful new results which allows one to draw conclusions as to the suitability of the Helium and Oxygen ion implanted AISI 316L stainless steel for biomedical use.

To provide a modified orthogonal array‐based model for enabling the researchers and practitioners to exploit the technique, “design of experiments” in an agile manufacturing environment.

The characteristics of Taguchi's off‐line models and agile manufacturing were studied. A theoretical model of modified orthogonal array‐based experimentation was designed. This model was subjected to implementation study in an Indian pump‐manufacturing company.

The model contributed in this paper has shown its feasibility in achieving quality in agile manufacturing environment.

The authors are residing in an Indian city where the majority of the companies have not adopted agile manufacturing criteria. Hence, it was not possible to carry out implementation study in an agile manufacturing company. Future researchers should examine the practical validity of the proposed model in agile manufacturing companies.

Since the manufacturing organizations are fast becoming agile, due to the customers' dynamic demands coupled with competition, the traditional quality improvement techniques are becoming obsolete. The model contributed in this paper is found to be useful in achieving continuous quality improvement in AM environment. Hence the model would be a useful technique for today's practitioners whose activities are increasingly focused towards achieving agility in manufacturing.

The literature survey covering articles on agile manufacturing indicates that no researcher or practitioner has contributed a model that would exploit the technique, “design of experiments” in an agile manufacturing environment. Hence the proposed model is expected to be of high value for researchers and practitioners to explore the way of achieving continuous quality improvement in agile manufacturing environment.

The purpose of the paper is to illustrate an approach for developing a framework of indicators for integrating environmental protection into corporate performance. This paper also highlights Green Productivity Index (GPI) of the Continuous Improvement (CI) performed in a foundry casting, which includes both economic and environmental performances.

In the paper data were collected from a foundry and used for developing indicators and Green Productivity Index (GPI). Mass balance concept was used for quantification of environmental indicators.

The paper finds the approach adopted will provide a clear guidance for developing indicator and GPI to various types of industries. A casting case indicates that the GPI can be used as an actionable feedback for leadership to make effective decisions.

This research in this paper developed a framework of indicators for measuring direct environmental impact from the foundry, while indirect environmental impact due to electricity consumed has yet to be performed. Limited external validity of the indicator framework, as the analysis was performed in a single foundry.

The paper relies on a unique foundry data. Environmental indicators may vary, since the melting furnace used for melting metal and type of molding process used for making molds may vary from one foundry to another.

This paper is a holistic approach of combining theory and practical ideas to cover the scope of sustainable development. From a research perspective, it establishes a framework of indicators for the integration of environment into foundry and creates new research and application opportunities to other pollution‐intensive industries. From a practical perspective, the framework of indicators developed to this point provides a guideline of immediate applicability.

Deals with the dynamic scheduling problems and solutions of job shop comprising six work centres and n components. Concerns the behaviour of the system, with the arrival of urgent orders and normal orders. In a typical manufacturing system, urgent orders are scheduled for processing based on their urgency and given priority over normal orders. Describes an analysis of urgent order processing on the basis of non pre‐emptive priority and pre‐emptive resume priority over normal orders. Enumerates manufacturing system performances which had been analysed for the two most popular scheduling rules – first in first out (FIFO) and shortest processing time (SPT) – through a system simulation program. Concludes by asserting that the simulation program can be used to schedule the manufacturing system dynamically by choosing the appropriate scheduling rule to measure optimal system performance leading to higher productivity.

Quality function deployment (QFD) is a proven tool for process and product development, which translates the voice of customer (VoC) into engineering characteristics (EC), and prioritises the ECs, based on the customer's requirements. Conventional QFD evaluates these targets for crisp weights of the customer attributes (CA), identified from the VoCs. The VoCs are not crisp and generally exhibit a well‐defined distribution. Crisp weights assigned to non‐crisp CAs can lead to wrong prioritisation of the EC. In the past, fuzzy numbers have been used to represent the imprecise nature of these judgements and to define more appropriately the relationship between EC and CA. This paper proposes fuzzy logic‐quality function deployment (FL‐QFD) – the use of fuzzy logic principles in QFD. It is an innovative method of determining optimum rating of ECs by simulating the QFD matrix for randomized CA rating in the fuzzied range. The rule‐based knowledge system defines the relationship between the ECs and the CAs. The flexible manufacturing system (FMS) design problem investigated by Khoo and Ho (Khoo, L.P. and Ho., N.C., “Framework of a fuzzy quality function deployment system”, International Journal of Production Research, Vol. 34, 1996, pp. 299‐311) is presented to show the application of the proposed model. The results are compared and examined to study the effect of the CA on prioritising the EC. The paper addresses the issue of defining non‐crisp customer attributes in the QFD, and offers practical help to an individual intending to further investigate the proposed model.

To provide a new model to solve the assembly‐planning problem of a textile machine in a shopfloor which can help researchers and practitioners.

The assembly planning of a textile machine (repetitive manufacturing system) involves the allocation of operations to cross‐trained operators. Workflow is defined as the workloads assigned to the operators. Operators with smaller workloads are selected to be assigned new operations from the list of unscheduled operations. Three different scheduling strategies – random, shortest processing time, and longest processing time – are adopted for the selection of operations to be assigned to operators. Different combinations of these strategies are considered for the selection of both preceding and succeeding operations. A computer simulation program has been coded on an IBM/PC‐compatible system in the C++ language to study the performance of real data from the shopfloor.

The relative percentage of imbalance is adopted for evaluating the performance of these heuristics. The RL, SL and LL produced well balanced workload schedules with lesser RPI values for all operators other than heuristics.

Non‐traditional approaches like genetic algorithms can be applied to determine the robustness of the results obtained by this research.

The experiments on simulated and real data clearly indicate that the order of succeeding operations determines the balanced workflows to the assembly of operations among the operators.

The allocation of assembly operations to the operators is modeled into a parallel machine‐scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

Describes the current industrial scenario which is marked by the proliferation of theoretical ideas and concepts on total quality management (TQM) proclaimed by numerous quality management and engineering experts. Insists on the need to streamline these ideas and concepts so that TQM programmes in manufacturing firms are focused effectively towards quality enhancement strategies. Calls for replacing the traditional terminology, TQM, with that of strategic quality management (SQM) with an updated definition. Presents the main results of investigations made during a long research carried out in this direction which began with the identification of a TQM framework of reference on the basis of the old single manufacturer model and culminated in the evolution of a focused SQM system model. Concludes by briefing the major findings of the research and mentions the possibility of exploiting artificial intelligence techniques like expert systems for carrying out SQM practices more effectively.

Focuses on the synchronous manufacturing system (SMS), a manufacturing management methodology that introduces various operational measures that help increase the performance of the system for optimum production quantity. Enlightens the effects of bottleneck resources and capacity constraints and suggests remedies for synchronized flow in the shop floor. Also recommends methods for production control in synchronous manufacturing, brings together the concepts and elements of marketing and manufacturing along with production quantity. Gives information about several factors that should be taken into consideration while applying synchronous manufacturing methodology in marketing and manufacturing. Shows the results, conclusion and future scope.