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Development and implementation of new process equipment within the process industries frequently necessitate strong collaboration between process firms and their equipment suppliers in joint process development projects. However, collaboration in this setting entails significant challenges over the lifecycle of these projects. Accordingly, the purpose of this article is to explore the problems and opportunities faced by equipment suppliers during collaboration with process firms, throughout the various lifecycle stages of process development projects.

The article synthesizes results from 22 interviews in a multiple case study of eight equipment suppliers in the process industries and a comprehensive review of relevant literature to identify critical problems of opportunities during the lifecycle. In total, data were gathered from firms in six different countries.

A deficient pre‐study may create problems due to miscommunication during development when close interaction is required. Purchasing discussions can be done simultaneously to development when a supplier has been selected, although uncertainty is a problem. It is important to get end‐user feedback and commitment during development and later stages. During assembly and installation and start‐up, a variety of actors are working simultaneously which requires coordination and planning from an early stage. Close interaction and education with end‐user is critical for the technology transfer in the start‐up stage. Sharing of experiences enhances operational performance during production.

This study contributes by employing information rich case study data to describe the problem and opportunities faced over the full lifecycle in joint process development projects in the process industry. In particular, key issues over the lifecycle have been identified and described (e.g. purchasing, start‐up, education). Moreover, the adoption of a lifecycle perspective has indicated how activities, issues, and managerial challenges in specific stages are interconnected and affect the joint work in the following stages.

The findings of this article serves as guidelines to managers in equipment supplier firms and their customers by highlighting the problems and opportunities for improvement that occur during the interconnected stages of process development projects.

By focusing on the collaborative activities in different stages, this study higlights the critical problems and opportunities in the lifecycle of process equipment. In addition this article outlines how joint process development activities can facilitate enhanced operational performance, by means of collaborative design, installation and operation of new process equipment – i.e. “open operation”.

Approaches maintenance management in a systematic way so as to achieve its business objectives. From a managerial point of view, this so‐called object/objective‐oriented maintenance management (OOMM) is an integral process of asset (equipment) management with behavior‐based maintenance (BBM) as a major element. Within the OOMM concept, the objective‐approach focuses on the managing of the maintenance processes so as to achieve the business objectives, and the object‐approach emphasizes the object (asset or equipment) and the behavioral failures. Furthermore, combined together, the two approaches reflect the basic characteristics of the maintenance process. Also, both sides affect and influence each other, and are inseparable within OOMM. BBM addresses the monitoring and controlling of the technical and economic behavior of a piece of equipment in two ways.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

This paper empirically investigates the processes by which manufacturing firms create radical innovations in their core production process, referred to as radical manufacturing technology innovations (RMTI). The purpose of this paper is to improve the understanding of the processes and practices manufacturing firms use to create RMTI.

Creation processes for 23 RMTI projects from diverse industry and technology contexts are explored. Data were collected via semi-structured interviews, and an inductive analysis was carried out to identify similarities and differences in RMTI types and creation processes.

Three types of RMTI and three alternative RMTI creation processes are revealed and characterized. An integrated view is developed of the activities of the equipment supplier and the manufacturing firm, highlighting their different roles and interaction across the three RMTI creation process types.

The exploratory design limits the depth of the analysis per RMTI project, and the focus is on manufacturing technology innovations in one country. The results extend previous case and context-specific findings on RMTI creation processes and provide novel frameworks for cross-case comparisons.

The manufacturing firms’ proactive role in RMTI creation is defined. A framework is proposed for using different RMTI creation processes for different types of RMTI.

This study addresses recent calls for empirical research on understanding the ways in which process innovations unfold in manufacturing firms. The findings emphasize the role of manufacturing firms as creators of RMTI in addition to their role as innovation adopters and implementers and reveal the suitability of different RMTI creation processes for different RMTI types.

The purpose of this paper is to provide theoretical insight and practical guidance on how both process firms and equipment manufacturers can address the challenges posed by collaboration during the operational stage of the process technology/equipment life cycle.

Motives and driving forces for entering collaborative projects far from always converge, and while some projects require deep and long‐lasting relationships, others call for pure transactions and arms‐length relationships. The questions of why, when and how collaboration should take place and be organised and managed are addressed and discussed in the light of the literature on technology diffusion and technology transfer, and supplemented by ideas from industry professionals.

A tentative list of potential pros and cons has been compiled to serve as an embryo for further creation of a more complete set of expected outcomes with a view to developing a firm benchmarking instrument for establishing new collaborative relationships. Subsequently, a conceptual model of the full life‐cycle of process technology/equipment is developed to create a platform for determining collaboration intensity and success factors during different phases. Finally, a matrix with the dimensions “type of capability” and “expected performance improvements” is introduced as a tool for selection of different forms of collaboration.

The main limitation is that so far this is only a theoretical framework, but as such it will serve as a new platform and a guide for further empirical studies of this important yet under‐researched area.

This area of technology and innovation management research for the process industries has not been addressed before in depth. The new framework can already be deployed by industry professionals in their efforts to improve inter‐company collaboration and technology transfer, but also as a means of avoiding unintended technology diffusion.

The purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and process condition monitoring with plasma optical emission spectroscopy data, simultaneously. The suggested research contributes e-maintenance capability by remote monitoring in real time.

Semiconductor processing equipment consists of more than a thousand of components, and unreliable condition of equipment parts leads to the failure of wafer production. This study presents a web-based remote monitoring system for physical vapor deposition (PVD) systems using programmable logic controller (PLC) and Modbus protocol. A method of obtaining electron temperature and electron density in plasma through optical emission spectroscopy (OES) is proposed to monitor the plasma process. Through this system, parts that affect equipment and processes can be controlled and properly managed. It is certainly beneficial to improve the manufacturing yield by reducing errors from equipment parts.

A web-based remote monitoring system provides much of benefits to equipment engineers to provide equipment data for the equipment maintenance even though they are physically away from the equipment side. The usefulness of IIoT for the e-maintenance in semiconductor manufacturing domain with the in situ monitoring of plasma parameters is convinced. The authors found the average electron temperature gradually with the increase of Ar carrier gas flow due to the increased atomic collisions in PVD process. The large amount of carrier gas flow, in this experimental case, was 90 sccm, dramatically decreasing the electron temperature, which represents kinetic energy of electrons.

Semiconductor industries require high level of data security for the protection of their intellectual properties, and it also falls into equipment operational condition; however, data security through the Internet communication is not considered in this research, but it is already existing technology to be easily adopted by add-on feature.

The findings indicate that crucial equipment parameters are the amount of carrier gas flow rate and chamber pressure among the many equipment parameters, and they also affect plasma parameters of electron temperature and electron density, which directly affect the quality of metal deposition process result on wafer. Increasing the gas flow rate beyond a certain limit can yield the electron temperature loss to have undesired process result.

Several research studies on data mining with semiconductor equipment data have been suggested in semiconductor data mining domain, but the actual demonstration of the data acquisition system with real-time plasma monitoring data has not been reported. The suggested research is also valuable in terms of high cost and complicated equipment manufacturing.

Overview All organisations are, in one sense or another, involved in operations; an activity implying transformation or transfer. The major portion of the body of knowledge concerning operations relates to production in manufacturing industry but, increasingly, similar problems are to be found confronting managers in service industry. It is only in the last decade or so that new technology, involving, in particular, the computer, has encouraged an integrated view to be taken of the total business. This has led to greater recognition being given to the strategic potential of the operations function. In order to provide greater insight into operations a number of classifications have been proposed. One of these, which places operations into categories termed factory, job shop, mass service and professional service, is examined. The elements of operations management are introduced under the headings of product, plant, process, procedures and people.

The Brazilian textile industry has been facing fierce competition from low-cost imports from China and other Far East countries. To maintain their competitiveness in the local market, Brazilian companies have been adopting the product differentiation strategy. By using new technologies, they are able to develop new products with better quality at lower costs. With regard to new technologies, companies in the Brazilian textile industry have been using get-some and buy-some strategy, and international technology transfer (TT) has become an important part of their business strategies. However, due to lack of planning, many projects failed to achieve the desired results. This paper aims to provide theoretical insights and practical guidance on how textile firms could use a stage-gate model to enhance the effectiveness of their TT projects.

In order to investigate the TT practices in the Brazilian context, three issues are assessed. First, the paper evaluates the possibility of deploying TT practices used by firms in similar industries, to enhance the effectiveness of TT process. Second, it verifies whether it is possible for the textile firms to use a stage-gate model to manage their TT processes, using as a normative framework the stage-gate model proposed by Jagoda and Ramanathan and Jagoda et al. Finally, possible changes to the stage-gate model are evaluated to specifically fit the Brazilian textile sector. This step is accomplished through four case studies from the Brazilian textile industry.

The analyses of TT projects carried out by four companies show that there are many similarities and differences among the TT practices that are employed by the four companies that were investigated. The evaluation of the TT practices of the Brazilian textile companies against the stage-gate framework allowed authors to identify the gaps between the model and the TT practices of the companies investigated. Broader guidelines in adapting the stage-gate model to improve the TT process in the textile industry are discussed in the final part of this study.

The TT process in the Brazilian textile industry is not a widely investigated phenomenon; however, this process has been critical to enhancing Brazil's competitiveness. Thus, providing a better framework to support the TT process in the local textile sector could be relevant information for improving management action in the area.

The purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.

The analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the “abnormal” value in the fault data. Based on the analysis of the impact, an “offset operator” is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.

It is discovered the “offset operator” can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the “noise” in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.

The proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.

The offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.

The purpose of this paper is to explore the specific practices of management in the dispatching unit and to identify mechanisms for supporting transfer of shop floor knowledge embedded in operating manufacturing equipment.

The paper applies an inductive and a case study approach for exploring three empirical cases that represent different manufacturing facility relocation processes and differences in the applied managerial practices.

The paper identifies two important gaps in international production literature when firms relocate equipment to other sites; a time gap (from dismantling to re-assembly of production facilities) and a space gap (from the current to the new site abroad). These gaps are important for understanding why relocation processes are difficult and what management can do to facilitate such processes.

The paper identifies four issues that management faces in the dispatching context when relocating manufacturing facilities.

The paper gives new insights to a limited literature of shop floor knowledge transfer when relocating manufacturing facilities.