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Mechanisms to adjust planned lead times based on current work loads are desirable for time‐phased planning systems. This paper investigates the use of exponentially smoothed order flow time feedback in setting planned lead times dynamically. The system studied is a supply chain with capacity‐constrained processing stations and transit times between stations. Lot sizes are based on the minimization of flow times using queuing approximations. Both seasonal and level demand patterns with uncertainty are considered. Since both dependent and independent demands are assumed at each station, customer delivery performance depends on the distribution of inventory along the supply chain. Results show that dynamic planned lead time setting can be used effectively to control delivery performance along the supply chain. Performance is also influenced significantly by appropriate lot size selection.

The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness.

A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation.

The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop.

Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines.

The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error.

Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

The purpose of this paper is to review the study of social capital focused on the level at which it is embodied, cross-comparing two prominent camps that have emerged in the social capital literature: a communal level and an individual level.

This paper reviews the intersections and departures between communal level and individual level conceptualizations of social capital according to the social dynamics of action within social exchanges that they stimulate, the processes by which social capital is activated/mobilized and the rewards they yield, and their linkages to inequality through network diversity.

This paper articulates new directions for future research in social capital: more analytical precision for studying returns to social capital; more efforts to transcend the individual-communal divide; the depreciation of social capital or tie decay; and recognizing the importance of ties whose value does not come from the ability to provide instrumental gain, but just from their very existence.

Social capital has informed many influential agendas in the social sciences, but the sheer volume of which has largely gone unscoped. This paper reviews this literature to provide an accessible introduction to social capital, organized by social processes foundational to sociology and a novel contribution to the literature by articulating new directions for future research in the area.

This paper discusses the experimental work in modelling uncertainty under a multi‐echelon enterprise resource planning (ERP)‐controlled manufacturing system. A new method known as part tagging (Ptag) is successfully implemented in a material requirements planning (MRP) planning architecture, which is used to generate a planned order release (POR) schedule for controlling purchase and manufacture operations in a batch manufacturing system using simulation. One of the most important findings is that parts tardy delivery (PTD) is a more responsive performance measure compared with finished products tardy delivery (FPTD); therefore it is recommended that PTD should be measured to reveal the unmasked effects of uncertainty. The main conclusion and implication from this experiment are that an ERP‐controlled manufacturing enterprise should diagnose for uncertainty in a way that produces significant effects on delivery tardiness, so that reduction of their levels will significantly minimise tardy delivery.

The customer enquiry management (CEM) process is of strategic importance in engineer‐to‐order contexts but existing literature does not adequately describe how firms support delivery date setting and order acceptance decisions in practice. This paper seeks to explore how and why the CEM process varies between companies in the capital goods sector, thereby taking a contingency theory approach.

Multi‐case study research involving 18 Italian capital goods manufacturers in four industrial sectors. Face‐to‐face interviews with senior representatives have been conducted. Companies have been grouped into five clusters, based on similarities in their CEM decision‐making modes, to aid analysis.

Three contingency factors were found to be particularly relevant in determining CEM modes: degree of product customization, flexibility of the production system, and uncertainty of the context. These factors affect the choice of specific CEM decision‐making modes. However, a high level of cross‐functional coordination and formalization of the process were found to constitute best practices whatever the contingency factors.

The research focuses on companies belonging to the Italian capital goods sector – findings may differ in other countries and sectors.

The results indicate that all firms, including small and medium‐sized companies, should implement high levels of cross‐functional coordination and formalization in their CEM practices, in order to improve their performance. For other aspects of the CEM process, including supplier and subcontractor monitoring, the company context will indicate whether these aspects are required, according to a need of matching the approach to CEM with specific sets of contingency factors.

This paper provides a rare insight into the CEM processes found in practice.

This paper aims to present a process to generate physics-based trade-off curves (ToCs) to facilitate lean product development processes by enabling two key activities of set-based concurrent engineering (SBCE) process model that are comparing alternative design solutions and narrowing down the design set. The developed process of generating physics-based ToCs has been demonstrated via an industrial case study which is a research project.

The adapted research approach for this paper consists of three phases: a review of the related literature, developing the process of generating physics-based ToCs in the concept of lean product development, implementing the developed process in an industrial case study for validation through the SBCE process model.

Findings of this application showed that physics-based ToC is an effective tool to enable SBCE activities, as well as to save time and provide the required knowledge environment for the designers to support their decision-making.

Authors expect that this paper will guide companies, which are implementing SBCE processes throughout their lean product development journey. Physics-based ToCs will facilitate accurate decision-making in comparing and narrowing down the design-set through the provision of the right knowledge environment.

SBCE is a useful approach to develop a new product. It is essential to provide the right knowledge environment in a quick and visual manner which has been addressed by demonstrating physics knowledge in ToCs. Therefore, a systematic process has been developed and presented in this paper. The research found that physics-based ToCs could help to identify different physics characteristics of the product in the form of design parameters and visualise in a single graph for all stakeholders to understand without a need for an extensive engineering background and for designers to make a decision faster.

This paper seeks to present the process and results of the application of a decision‐making tool, namely TAPS, which enables translation of knowledge of supply chain uncertainty into business strategy and actions.

The knowledge of supply chain uncertainty is collected from previous research performed under enterprise resource planning (ERP)‐controlled manufacturing environments. The knowledge is used as the input for TAPS and is mapped to investigate and formulate appropriate business strategy and action plan to manage supply chain uncertainty in such environments.

The results of knowledge translation provide a set of guidelines to academics and practitioners, which indicates the underlying causes of supply chain uncertainty in ERP‐controlled manufacturing environments in a priority order, and the suitable business strategy and actions that could potentially be adopted to manage the uncertainty.

Owing to the increasing level of complexity and uncertainty intoday's enterprises, translating knowledge is suggested to be useful in assisting decision making and business strategy formulation.

This research provides a successful case example of knowledge translation process of supply chain uncertainty into business strategy and actions, which enables creation of sustainable strategies to manage uncertainty based on the concept of knowledge management and learning.

This paper proposes a knowledge management approach for managing uncertainty in manufacturing enterprises.

The knowledge management approach consists of a knowledge‐enriched manufacturing system, which is modelled using SIMAN simulation language and programmed using Visual Basic applications. A knowledge‐based planning module and an execution platform are simulated so that signals could be transferred, and configuration to the planned parameters could be made, in order to minimise variations due to uncertainties. A reference architecture and intelligent agent are created to store tacit knowledge and create explicit knowledge, respectively.

Manufacturing enterprises should use both tacit knowledge about uncertainties and buffering and dampening techniques, simultaneously with the explicit knowledge that is generated by the intelligent agent, for managing uncertainty. The design of the knowledge management approach enables easy integration with material requirements planning, manufacturing resource planning or enterprise resource planning systems, and complements with the adoption of advanced technology.

A new concept – management by valued‐added urgency, emerges that underpins the knowledge management approach. It is grounded from the previous literature on managing uncertainty classified into: masking approach; standardising approach; prioritising approach; and optimising approach and extended Westbrook's priority management theory. This concept focuses selectively on value‐added changes that need to be made to counteract variations caused by significant uncertainty.

This paper seeks to show how enterprise resource planning (ERP) could create a competitive advantage for small‐ and medium‐sized enterprises (SMEs).

The main methods used in this study were questionnaires and interviews based on the application of an uncertainty diagnosing business model. Data were collected, using a questionnaire administrated to 126 SMEs, in the form of percentage contributions of the underlying causes of uncertainty (structured in the business model) on product late delivery. Analysis of Variance (ANOVA) was carried out in SPSS to analyse the effects of the underlying causes of uncertainty in SMEs.

ERP could create a competitive advantage in delivery for SMEs by being responsive and agile to change, but not to uncertainty. Results suggested that only a few features in an ERP system were used to deal with change due to uncertainty. It was found that SMEs generally use their ERP system to generate a plan for production and use it as a guideline. SMEs concurrently use a range of buffering or dampening techniques to tackle uncertainty for crating a competitive advantage in delivery.

The application of the business model in SMEs has provided useful knowledge to make‐to‐stock (MTS), make‐to‐order (MTO) and mixed‐mode (MM) manufacturing enterprises in which underlying causes of uncertainty were significantly affecting their product late delivery performance.

This is a highly original application of an uncertainty diagnosing business model to SMEs using ERP systems.

The aim of this paper is to investigate how enterprise resource planning (ERP) systems could create a competitive advantage for small and medium‐sized enterprises (SMEs). The objectives of this study are to examine how responsive and agile the existing ERP systems are to change and uncertainty, and to identify the types of change and uncertainty in SME manufacturing environments.

A mixed methodology is used in this study, which involves literature review, questionnaire survey and follow‐up, in‐depth telephone interviews. An uncertainty diagnosing business model is applied to collect data from SME manufacturers in make‐to‐stock (MTS), make‐to‐order (MTO) and mixed mode (MM) manufacturing environments in a structured manner, and to analyse the effects of the underlying causes of uncertainty on product late delivery in MTS, MTO and MM manufacturing environments in SMEs. Some 108 enterprises responded (86 per cent response rate), of which 64 are SMEs. Analysis of variance (ANOVA) is carried out in SPSS to analyse the effects of the underlying causes of uncertainty on product late delivery in MTS, MTO and MM manufacturing environments in SMEs.

ANOVA results show that a different group of underlying causes of uncertainty significantly affects the product late delivery performance in MTS, MTO and MM manufacturing environments in SMEs. This study found that ERP could improve responsiveness and agility to change, but not to uncertainty. SMEs could create a competitive advantage by being more responsive to change in the ERP system before generating purchase and work order. ERP systems could not deal with uncertainty due to its stochastic and unpredictable nature. SMEs use a range of buffering or dampening techniques under uncertainty to be competitive in delivery.

It can be concluded that the application of the business model in SMEs that use ERP has provided useful knowledge about the significant underlying causes of uncertainty that affect product late delivery performance in MTS, MTO and MM manufacturing environments. Using this knowledge, similar SMEs could then prioritise the effort and devise suitable buffering or dampening techniques to manage the causes of uncertainty and hence prevent any changes to the ERP system.