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The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.

This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.

The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.

This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.

Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.

Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor.

The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances.

The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%.

This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.

This book is a policy proposal aimed at the democratic left. It is concerned with gradual but radical reform of the socio‐economic system. An integrated policy of industrial and economic democracy, which centres around the establishment of a new sector of employee‐controlled enterprises, is presented. The proposal would retain the mix‐ed economy, but transform it into a much better “mixture”, with increased employee‐power in all sectors. While there is much of enduring value in our liberal western way of life, gross inequalities of wealth and power persist in our society.

Significant transitions in firms (e.g. outsourcing) may impact the relative importance of production and inventory assets, affecting the hierarchical separation of planning decisions. The purpose of this paper is to contribute to planning literature by investigating how the production system and the planning environment influence the performance difference between hierarchical and monolithic planning. Further, it seeks to reduce the prevailing theory-practice gap in tactical planning.

Through an action research study, a monolithic model integrating tactical production planning decisions, subject to upstream supply chain constraints, with strategic investments decisions was developed, tested and implemented in a global OEM. Using the developed model and a measure of the capital cost of production assets relative to the cost of holding inventory, it is numerically examined how the production system and planning environment influence the performance of hierarchical and monolithic planning.

The research demonstrates the potential of integrating decisions and reveals significant performance differences between hierarchical and monolithic planning for firms with low capital cost relative to inventory holding cost.

The findings suggest a fit between planning processes, the production system and planning environment. Future research should empirically validate the findings and propositions.

The paper combine capital investments and production planning decisions, which usually transpire at different hierarchical levels and on different time-horizons, and investigates the consequences of hierarchical separation through a real-life validated case and numerical analysis.

The purpose of this paper is to understand the configuration of a job production system with reference to manufacturing decision areas. The aim is to identify the process specific decisions for job shop and the non‐process specific decisions that are influenced by other contextual factors.

A case study research approach is used in the present paper to investigate the decisions of five manufacturing companies that satisfy the characteristics of job production system. Data are collected from case company's products, order winners and choices made in manufacturing decision areas. The paper uses within case and cross‐case analysis to identify various patterns in the data, with a view to meeting the required research objectives.

The present paper identifies a number of decisions specific to job shop. Further, many non‐process specific decisions are seen to be influenced by competitive priorities (order winner), strategic orientation of manufacturing (stages in H‐W model), top management and size of the company. After the study of the case companies, it is also observed that the companies employing a job production system may have high product complexity.

The findings derived from this research would facilitate practitioners in understanding both process specific and non‐process specific decisions for the job production system. The observation that the job shops can also use progressive practices, the same as other shops, to gain competitive advantage in the market could be very useful for practicing managers.

This exploratory research contributes to the existing theory in manufacturing decision areas for job production systems.

This paper proposes and tests a model to explain the outcomes of three different information presentation formats. Based on cognitive fit theory, information visualization formats that best fit task characteristics are expected to lead to improved decision-making outcomes. We apply the Judgment and Decision-Making framework (Bonner, 2008) to investigate how certain factors can impact decision quality.

This study tests whether certain production variance presentation formats (percentages, dollar amounts, and schematic faces), task complexity, understanding of the presentation format, motivation, and effort increase the accuracy of a supervisor’s bonus calculation. A total of 281 students and professionals participated in this experiment. Their responses were examined using regression analysis.

Our results indicate that individuals mostly prefer the percentages presentation format and that the use of the percentages presentation format, a lower level of task complexity, and a better understanding of the variance presentation format lead to more accurate calculations in the experimental task.

Our study provides a call for further research on factors that influence the choice of presentation format as a potentially fruitful area for management accounting researchers.

We exhort practicing management accountants to exert direct influence on employees’ decision making through the use of variance presentation formats that fit their tasks and promote understanding.

Our experiment introduced two major innovations: it uses an interactive data visualization approach allowing subjects to select their preferred presentation format; and it focuses on production variances, a topic that has received less attention in the academic managerial accounting literature, but is still very relevant to practitioners.

This paper aims to analyze the influence of business decision on process efficiency and production technology in the communication equipment and consumer electronics sectors of Indian hardware electronics industry after liberalisation.

The study is based on 26 sample firms, belonging to the two sectors of Indian hardware electronics industry with continuous operation from 1993 to 2004. Estimation of the contribution of production technology and process efficiency to output growth is based on stochastic production function. Influence of business decisions on production technology and process efficiency is based on indigenously developed business decision model.

Sectors, in general, fail to achieve improvement in process efficiency in comparison to progress in production technology. The business decisions factors like improve capacity utilization and operational efficiency together with decisions to adopt vertical integration to help sample firms to achieve both progress in technology and improvement in process efficiency in the sectors. However, R&D investments and technology imports are costly for both, indicating inadequate and inappropriate investments made by the sample firms. Disinclination of firms towards improving or developing their own technology is evident from their high dependence on import of technology.

This study only analyses the role and importance of business decision factors in the past. What lies ahead is to meet the challenges and improve performance to withstand the stiff competition. This study suggests for further research about how to improve on the effectiveness of business decision factors that can help Indian hardware electronics industry to survive competition.

The paper develops a business decision model which analyses the impact of business decisions on production technology and process efficiency for a sample of firms in Indian hardware electronics industry.

The purpose of this paper is to propose an integrated model of decision support system (DSS), artificial neural network, information and communication technologies and statistical process control (SPC) to facilitate agreement by different stakeholders with special interests to commit to the decision that will be taken to stop the production line when something goes wrong somewhere in a supplier network environment.

A DSS is proposed to capture exceptional signals from source on deterioration of product quality to alert preventive actions needed before the problems are getting out of hand. The supervisors are given a set of guidelines to support making the decision. Real‐time SPC and rule‐based decision support procedures are used to trigger pre‐defined exceptional signals for forwarding to the most appropriate person (the knowledge holder in the problem domain) to make a decision to stop the line. All servers in all remote sites are internet‐connected and provide real‐time quality data to the regional supply chain manager. A case study is described to show how this is implemented in a lens manufacturing company.

A significant improvement in quality level can be achieved by holding the knowledge worker accountable for making the decision to stop the production line rather than made by default as is with most traditional operations.

To provide a concept to structure activities for decision support so that the persons responsible for making the decision to stop the production line is held accountable by all stakeholders.

Practitioners can replicate the approach used in this paper to their own situations involving decisions to be made to address un‐structured problems and unclear responsibilities.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

Manufacturing offshoring has received substantial attention within international business studies that have explored where activities are located and how they are governed. However, recent examples of manufacturing relocation to the home country/region have put the advantages of offshoring under scrutiny, since the location of production activities in high-cost countries may have positive impacts in terms of innovation and marketing opportunities. Despite the growing interest in offshoring and “relocations of second degree,” there is a lack of knowledge on the alternative strategies firms may implement after offshoring. This chapter aims to propose a comprehensive framework to summarize and classify the multiple alternatives firms may implement after the initial relocation abroad of manufacturing activities. Based on an extensive literature review and a comparative analysis of Italian case studies, the chapter suggests theoretical advancement in the theory of location of business activities, offering multiple post-offshoring strategic options that may be implemented individually or in combination. In so doing, the analysis also stresses the variety of strategic paths and the complexity of choices concerning manufacturing location, emphasizing reshoring as a nuanced phenomenon and exploring how domestic and foreign locations can complement each other and be mutually reinforcing.