Abstract

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Introduction

The building and infrastructure industries are gradually transitioning towards building information modelling (BIM) as an object-based digital representation of an asset’s physical and functional characteristics [1], [2] and digital engineering (DE) practices to enable more productive methods of planning, designing, constructing, operating, and maintaining assets, using digital processes [3]. As detailed later in the paper, “digital” encompasses approaches, tools and technologies such as BIM and DE together with data science, the Internet of Things (IoT), and AI solutions integrated to digitise assets throughout the lifecycle. Asset owners recognise the importance of digital transformation (DT) in delivering and managing assets [4], as it offers many benefits throughout the asset lifecycle and can enhance efficiency, value for money, productivity, innovation, and safety.

1.1. DT of large infrastructure asset ownersGovernments and government-owned corporations (GOCs) are typically owners and operators of large assets, such as buildings, infrastructure, and other asset holdings across the spectrum of services. Governments and industries must transition from ‘digital by exception’ towards ‘digital by default’ [5], [6]. The infrastructure sector should adopt an evidence-based approach to infrastructure planning, delivery, and operations, including BIM, DE, embedded sensors, and digital asset management [7]. DT offers significant potential value and benefits to government agencies that manage assets across the whole-of-asset lifecycle. DT can assist in strategic investing and partnering with local governments and industry to deliver more benefits, including [8]:

Conclusion

The dynamic evolution of the building and infrastructure sectors towards digitalised practices underscores the growing importance of asset owners engaging in comprehensive DT. This research project has developed a multifaceted framework for assessing DT maturity tailored to the needs of large GOCs.

The existing assessment tools and methods have been reviewed for suitability in assessing the DT in whole-of-life asset owner organisations. The reviewed tools offer varying assessment scopes, including a capability assessment, maturity assessment, and benchmarking. The need and potential benefit of a new maturity assessment tool that would balance assessment accuracy and completion efficiency were established based on the shortcomings and limitations of existing DT maturity tools and methods. The key contributions of the new DT maturity assessment framework and tool developed by the research team include (i) a holistic view on the assessment of how well organisations use data and digital solutions, focusing on BIM capabilities, the value of information, data integration aspects, components of the smart city concept and critical information management competencies; (ii) an evidence-based scoring approach including assigning weight to various assessment indicators based on their importance to ensure the accuracy of the assessment; (iii) a multiperspective assessment allowing for more accurate organisation-wide analysis; (iv) short-term and long-term recommendations based on the current and target levels gap to facilitate performance improvement while realising the benefits and value of getting to each maturity level.

Abstract

In the era of the competitive environment, the improvement in current products is ensured through activities aimed at increasing a product’s quality level and, consequently, reducing the amount of waste. The dynamically changing production environment and sudden changes in customer expectations force us to take precise and well-thought-out development steps. Furthermore, it is important to anticipate favourable product changes to prepare for market changes over time. This is still an open problem. The aim of this study was to develop a method to predict the quality of potential product prototypes resulting from the proposed modifications of the product features. This methodology takes into account current customer expectations. The method was created based on the principles of creating Quality Function Deployment (QFD) in the context of taking into account current and future customer expectations regarding product features. This is a new approach to analysing product quality within the principles of the traditional QFD method. The originality of the study is the technique used in the method to estimate the expected values of product features and their importance (weights), taking into account current customer expectations. Its originality is also manifested in drawing conclusions supporting the decision-making process of product improvement, because it involves ensuring the pro-quality modification of selected features of current products in the order that is most advantageous from the customer’s point of view. The use of the proposed method allows for the analysis of the impact of modifying the current value of a product feature. The method is illustrated with an example of a vacuum cleaner for home use. However, the proposed method can be applied to the design of any product to predict products that will meet customer expectations.

Introduction

The improvement in product quality covers a product’s customisation to expectations [1,2,3]. In this regard, it is necessary to have a skilful shape of the attributes of a product to achieve product quality that meets the level of expectation. This shaping includes acquiring the Voice of Customers (VoC) [4,5], and then involves its analysis and transformation in product attributes [6,7,8]. The product quality level mentioned is the level of compliance of a product with customers’ expectations and refers only to specific product uses. On the other hand, customer expectations (i.e., their desires and assumptions in the context of the future) are customer satisfaction with what they received (current product) and what they expected (product they expected) [9,10,11]. Despite that, designing products is still a problem; therefore, a search for methods to solve this problem is needed.

After reviewing the literature, it was concluded that the House of Quality (HoQ) [12] i.e., Quality Function Deployment (QFD) [13], is the most popular and most-used method to design a satisfactory product [14,15,16,17,18]. This method is a correlation matrix. The method supports the process of design by collecting and verifying expectations, e.g., of customers and the producer, to achieve the required product quality level. Previous work as part of HoQ concerns mainly the validity (weights) of customer requirements, e.g., mainly as part of the use of the AHP method (Analytic Hierarchy Process) [15,19,20,21]. Also, as part of HoQ, the customers’ requirements were specified, mainly using the fuzzy Saaty scale and FAHP method (Fuzzy Analytic Hierarchy Process) [16,22,23]. For this purpose, the HoQ with the AHP method and the Kano model have been integrated relatively often [21,24,25], where the objective of this combination is to precisely determine the importance of customer expectations in the context of product satisfaction. This precision was achieved by using the 2-tuple linguistic [17], in which the customer’s needs were characterised by description and number, which is a distance of the central value of the linguistic term. Despite this, the importance of customers’ requirements in the HoQ was determined by implementing a mathematical model according to LGP (Linear Goal Programming), in which the ordinal scale was used [26]. Another example is a combination of the HoQ with the Yager algorithm [27] to determine customer requirement weights also in the case of indifferent requirements. Another example concerning the design of the expected product is the combination of the HoQ method with the TRIZ method (Theory of Innovative Problem Solving) [28,29,30]. This combination encompasses determining customers’ requirements and their expectations as part of providing a satisfactory product.

Conclusions

In a competitive environment, it is reasonable to look for various solutions to improving products. This mainly concerns shaping the current features of products as part of customising them to customers’ expectations. However, designing a satisfactory product ahead of the competition is still a problem. This refers to the prediction of the quality level of the expected product, which can be designed based on the current customer expectations. For this purpose, a method was proposed to predict the pro-quality modification of product attributes considering current customers’ expectations. The method was developed by transforming the House of Quality in the context of considering the values of the attributes of the current and expected product.

The proposed method was tested by using a domestic vacuum cleaner as an example. The purpose was to predict the quality of the modification of the attributes of the vacuum cleaner considering the expectations of current customers. By using a survey and the Likert scale, the requirements of 166 customers were obtained. In the research, 20 attributes of the vacuum cleaner were included, for which the customers assessed their satisfaction with the current and modified values of the features. In addition, customers determined the importance (weights) of the product attributes. According to the Pareto rule, the important attributes were selected. The important product attributes were the vacuum in the suction pipe, working range of the vacuum cleaner connected to the power cord, length of the power cord, and vacuum cleaner motor power. For these attributes, the current and favourable modification values were determined. As part of the calculation model, the quality of the product attributes was estimated. In addition, the importance and influence of the modification of the current product attribute values were estimated. As a result, the pro-quality product modifications were predicted. Therefore, a classification (order) of the changes of the product attributes in the context of achieving the expected level of product quality was predicted. It has been predicted that in the first order, the working range (above 19 m) and motor power (above 900 W) should be changed. The change in these attributes allows for an expected increase in the product quality level. The next anticipated changes were length of the power cord to above 15 m and then vacuum in the suction pipe to above 27,000 Pa. Herein, the manufacturer has the final decision as to which of the product attribute modifications will be implemented.

Abstract
Innovating manufacturing systems can substantially support a sustainable transformation of value creation across the industry towards higher sustainability maturity levels. The research presents a novel systems engineering approach for innovating manufacturing systems with a focus on sustainability. The systems engineering approach aims at supporting manufacturing companies in realizing sustainable innovations. It allows companies to cope with current and future global challenges while maintaining and improving their competitiveness. The concepts of integrated values and global innovation pathways set the research’s scope and practical relevance. A narrative literature review of the earlier and later phase of the sustainable-oriented innovation process is presented. The systems engineering approach for creating sustainable manufacturing innovations with a defined macro and micro cycle is introduced. The macro cycle determines the important design phases for the life cycle of the innovation while the micro cycle provides a process for finding sustainable solutions. A case study grounded on a start-up that developed an innovative wind turbine tower system is introduced for verifying, validating, and evaluating the proposed systems engineering approach within the application field of manufacturing large-scale products.

Introduction
The reactions of the global community to recent shocks, such as the Corona pandemic, droughts in Europe, or the war in Ukraine, demonstrate that the global community is insufficiently prepared both individually and collectively for disasters on a global scale. Among the most crucial short-term risks are, according to the World Economic Forum, crises in employment and livelihood, widespread youth disillusionment, digital inequality, economic stagnation, human-made environmental degradation, erosion of societal cohesion, and acts of violence (World Economic Forum , The Global Risks Report, 2021). These risks translate into long-term global challenges for the global community.

Against this backdrop of intensifying global challenges, manufacturing companies continue to apply individual business models within globalized value creation networks, i.e., the formation of inter-organizational cooperation to create value, often through technological advancements. Competitiveness in global markets is a major tenet of these networks with a focus on economic objectives, often without considering missed or detrimental environmental or social values. The concept of value must radically change for business activities in early and newly industrialized countries with the potential for establishing more sustainable practices that incorporated profit with social well-being and environmental stewardship (Neace, 1999; Cezarino et al., 2021). Value creation of companies needs to shift towards a modern paradigm with an emphasis on sustainable development and community resilience (Koliou et al., 2020; Neumann et al., 2021).

Conclusions

The research presented a novel systems engineering approach for creating sustainable innovations in manufacturing systems. The approach aims at supporting manufacturing companies in coping with the global challenges while maintaining and improving the competitiveness of the companies’ value creation. Against this backdrop, the research paper first discussed the concept of integrated values and global pathways of innovation for setting the scope and practical relevance of the research. Subsequently, the state-of-the-art based on a literature review for the earlier and later phase of the sustainable-oriented innovation process was described, and the resulting research gap, contribution, and novelty was specified. Then, the novel systems engineering approach for creating sustainable innovations in manufacturing systems, based on a specific macro and micro cycle was explained. The macro cycle determines the important design phases for the life cycle of the innovation while the micro cycle provides a process for finding sustainable solutions during the design process. For this purpose, the micro cycle utilizes specific sustainability principles which have been derived from the idea of integrated values and global pathways of innovation. A case study based on an innovative wind turbine tower system was introduced to verify, validate, and evaluate the proposed systems engineering approach. Eventually, the principal efficacy and limitations of the research were discussed taking into consideration the findings of the case study.

Abstract

Recently, the defense industry in South Korea has been regarded with strong suspicion since a company responsible for producing parts for the aviation sector is being investigated for cost irregularities. Many people are concerned about these issues and the government is distrustful of defense industries. If the current situation continues, the defense industry market in South Korea (henceforth “South Korea”) will be hard to maintain. The South Korean government will have to rely on importing from abroad instead of buying products in the local market. This study looked at the sustainability of earnings management adopted in the defense industry of South Korea, exploring options for the government to maintain the scheme’s soundness. In particular, we used Kothari’s discretionary accrual as a proxy of earnings management and OLS method to analyze the relationship between them. The results are as follows. Firstly the firms with a high percentage of defense sales showed significant discretionary accruals; the higher the profit, the more likely it was that earnings adjustments were made; secondly, firms that had been certified by the government for defense cost-management were proven to be effective, because the earnings management activities were low compared to those of firms that did not have the defense cost-management certification; lastly, based on the accounting rules for the cost of defense products, it was found that significant results confirming defense firms were exercising their influence, through earnings management, on the improvement of the government system.

Introduction

In South Korea, defense scandals have recently become problematic [1]. The company that produces parts for the aviation sector is being investigated for cost irregularities. There is mounting suspicion that illegal costs in the defense industry are rising. Many people are concerned about these issues and the government is distrustful of defense industries. If the situation continues, the defense industry market in South Korea will be hard to sustain. Preventing earnings management in the defense industry is thought to be the first step to achieving transparency to help strengthen South Korean military power. The South Korean government will need to rely on importing instead of buying products in the local market. In this study, the relationship between the characteristics of domestic defense firms and earnings management in South Korea were examined.

We studied the unique cost structure of the defense industry, which compensates for profits based on the costs incurred, and examined the cost-management certification system in terms of the share of defense sales, the political cost hypothesis, and the characteristics of defense firms arising from the asymmetry of cost information between the government, which is the consumer, and the defense industry, which is the supplier.

Conclusions

This study aimed to examine the earnings management behavior of firms in the defense industry in South Korea and find a solution for the government to reduce the earnings management. The results of the study are as follows.

First, firms with higher defense product sales increased their degree of earnings management. This differs from the study by Kim et al. [13] that showed that the higher the reliance on the defense industry is, the lower the reported profit is recorded. That is, for defense firms, a profit-centered system does not produce only one result, but creates different earnings management systems that can increase and/or reduce reported profit.

Second, the defense industry cost-management system, which shares cost management information about defense firms, was examined in relation to earnings management. We found that the level of earnings management was lower for the companies with the certified system than those without the system. This can be interpreted in two ways. Firstly, for a company that has the certified defense cost-management system, due to the information asymmetry between the government and the company about the decrease in costs after the implementation of the system, the level of earnings management decreased; secondly, the firms with the certified system have already received the cost-related certification from the government because they did not engage in earnings management activities. Of these two interpretations, the latter is valid because the system for cost control in defense firms has been in effect since 2012. However, because there is not enough data (4 firms in 2012, 9 in 2013, and 12 in 2014), the effectiveness of the test could not be measured.

Abstract

Keywords

1. Introduction

2. What is the industrial Internet of Things (IIoT)

3. Need of IIoT

4. Research objectives

5. Different supporting technologies of IIoT

6. Major benefits of IIoT in manufacturing

7. Collective features of IIoT for Manufacturing Domain

8. Smart Transformations made into manufacturing through IIoT culture

9. Applications of IIoT in the field of manufacturing

10. Discussion

11. Limitations and future scope

12. Conclusion

Conflicts of interest

Declaration of competing interest

References

Abstract

Now a day’s manufacturing has become more intelligent and data-driven. A smart production unit can be thought of as a powerful connected industrial system with materials, parts, equipment, tools, inventory, and logistics that can transmit data and communicate with one another in the age of the Industrial Internet of Things (IIoT). This IIoT refers to linked devices, sensors, and other equipment that may be networked in the industrial environment to provide remote access, effective monitoring, better data collecting, analysis, & exchange etc. In Industry 4.0, IIoT is fundamental to transforming cyber-physical systems and production processes through big data and analytics. This paper provides an overview of the IIoT and the technologies that underpin it. The primary benefits and features of IIoT in manufacturing are discussed in detail. Smart Transformations made into the manufacturing field through IIoT Culture are discussed diagrammatically. Finally, twenty-nine significant applications of IIoT in the field of manufacturing are identified and discussed. IIoT can monitor the transport, supply of the goods, consult details on things in warehouses, and check the conditions related to product storage and delivery and allow all dispersed and outsourced operations to be monitored. Therefore, the industry is being revolutionised by IIoT, altering the way industrial enterprises function daily to improve efficiency and performance levels.

1. Introduction

Industrial Internet of Things (IIoT) is the next level of Internet of Things (IoT) technology and is unique in its manufacturing transformation. Companies seeking a competitive edge today need just look at the capabilities that IIoT can offer the advantages all from maintenance, logistics providers, workflows for employees, and product delivery. IIoT enables manufacturers to digitise almost all parts of their businesses. Manufacturers can decrease the main risks associated with manual work and human error by minimising hand processes and entries. IIoT can help decrease cyber risk and human mistake data violations. There are hundreds of sensors that generate data in a typical industrial plant [[1], [2], [3]]. With IIoT, producers can proactively enhance performance by recognising possible bottlenecks, breakdowns, and production process gaps. Combining data from a site network may also contribute to more effective management of material flows early detection and identification of production, and optimising machinery and equipment performance at all installations [4,5].

Abstract

Introduction

Theoretical background

Production planning and control theory and the limitations of classical PPC systems

Emerging uses of smart technologies for smarter PPC

Design and development considerations

Choosing an appropriate machine learning algorithm

Data architecture considerations

Systems architecture considerations

A methodology for designing and developing a smart PPC Solution

Case study

Determine objectives and priorities in ftting with the planning environment variables

System requirements specifcation: operations’ problems and performance indicators

Requirements specifcation

Performance indicators

Insights from the case study and implications for research and practice

Conclusions, limitations, and further research

Appendix

References

Abstract

     In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

Introduction

     Production planning and control (PPC) refers to the activities of loading, scheduling, sequencing, monitoring, and controlling the use of resources and materials during production. Loading concerns how much to do; scheduling concerns when to do things; sequencing concerns in what order to do things; and monitoring and control is concerned with whether activities are going to plan, and corrective actions needed to bring activities within plan (Slack et al., 2013). Commonly, these activities of PPC are carried out and coordinated using enterprise resource planning (ERP) systems (Arnold et al., 2011) and spreadsheet solutions (de Man & Strandhagen, 2018). However, ERP systems are typically unwieldy and do not support real-time decision-making that today’s market environments demand. Furthermore, manufacturing execution systems (MES) and advance planning and scheduling (APS) systems have also been developed in the last two decades to address some of these weaknesses of ERP systems (Öztürk & Ornek, 2014). While APS systems have been associated with various potential benefits, including support for real-time decision-making, the challenges associated with their implementation and integration with ERP systems render these benefits far from achievable in practice (Lupeikiene et al., 2014).

Conclusions

     The question of how a smart PPC system should be designed and developed for an environment has been addressed in this paper through a five-step methodology. The steps of the methodology have been formulated and structured with the consideration that the resulting PPC system should fit the characteristics of the environment in question. Furthermore, the importance of contextual fit in algorithm selection, solution scalability and amenability of the smart PPC system to address future demands have been highlighted. In summary, the principles and considerations that guide the design in a smart PPC system are as follows:

     The design of the smart PPC system should fit the characteristics of planning environment. This highlights an issue that has been observed in numerous ERP and APS implementation case studies – expensive monolithic systems forcing managers to modify the production system to fit an inflexible PPC system. The proposed methodology can guide the design and development of such a fitting smart PPC system.

Abstract

Introduction

Section snippets

References (31)

Cited by (20)

Recommended articles (6)

Abstract

The industrial Internet of Things (IIoT) plays an important role in the industrial sector, where secure, scalable, and easily adopted technologies are being implemented for the smart industry. The traditional IIoT architectures are generally based on centralized architectures that are vulnerable to a single point of failure and to several cyber-attacks. Blockchain technology is frequently adopted in the modern industry because of its security and decentralization. This paper proposes a blockchain-based architecture that ensures secure and trustworthy industrial operations. A private and lightweight blockchain architecture is proposed to regulate access to valuable sensor and actuator data. To enhance the computational performance of the proposed architecture, real-time cryptographic algorithms are processed using a low-power ARM Cortex-M4 processor, and a highly scalable, fast, and energy-efficient consensus mechanism proof of authentication (PoAh) is deployed in the blockchain network. Extensive experiments and analysis proved the effectiveness of the proposed framework for smart industrial environments. Finally, we transform a conventional fruit processing plant into a secure and smart industrial platform by implementing the proposed architecture.

Introduction

Modern technologies and innovative concepts such as big data, artificial intelligence (AI), cloud computing, and cyber–physical systems have sparked a revolution in the industrial sector [1]. The integration of the Industrial Internet of Things (IIoT) with smart industry has shifted industrial operations to a new level [2]. The IIoT dramatically changes several industrial processes by providing facilities and introducing new systems and business models [3], [4]. IIoT platforms offer several services to modern industries, including high-speed connectivity, edge and cloud computing, big data analysis and application development [5]. These services enhance existing industrial processes by optimizing production processes, improving customer services, and reducing manufacturing costs using emerging and cutting-edge techniques [6], [7]. The current industry trends and initiatives are targeted at realizing the fourth industrial revolution by connecting the existing industrial environments to Internet services [8], [9].

1- Introduction

2- Literature review

3- Methodology

4- Results and discussion

5- Conclusion

References

Abstract

Biomass is becoming an increasingly widespread source of energy. Yet land, as one of the most important resources in biomass production, is surprisingly understudied in the literature of biomass supply chain planning. This study proposes a novel framework that combines the literature of bioethanol supply chain design with agricultural land planning to simultaneously address optimal supply chain planning and sustainable land use in a bioethanol supply chain. A bi-objective mixed-integer linear programming (MILP) model is proposed to formulate the optimal design and planning of a bioethanol supply chain network considering competition of food and biomass feedstock over the available croplands. The proposed model is capable of making strategic decisions (i.e. locations and capacities of facilities, sourcing and allocation of biomass feedstocks to biorefineries), along with some tactical decisions (i.e. land planning, inventory and production of both biomass feedstock and bioethanol). The model incorporates the two objectives of minimum cost and maximum suitability of crops with their assigned croplands. A novel integration of the FAO framework, the best-worst multi-criteria decision-making method, PROMETHEE II and GIS is used to determine the suitability of available croplands according to the croplands’ soil and topographical characteristics. The performance of the proposed model is demonstrated through a multi-feedstock bioethanol supply chain in Fars province, Iran. It is concluded that the proposed integrated land planning-network design framework outperforms hierarchical approaches in which network design and land planning problems are solved separately in a sequential manner. Also, the case study shows that conditional on implementing second generation bioethanol production, Fars province has the potential to satisfy three percent of the fuel demand for transportation in the country.

Introduction

With the rapidly increasing energy demand and the destructive environmental implications of fossil fuel consumption, renewable energies (REs) play an important role in protecting the environment for the future. Among all the renewable energy resources, biofuel is very attractive due to its worldwide availability, storage potential, and the efficiency of its conversion technologies [1]. In the recent years, many studies have been conducted on its future contribution to the global energy demand [2,3]. Iran holds the largest natural gas and the fourth largest oil reserves in the world and is within the ten countries with the highest emissions of CO2 [4]. The country’s main primary energy resources include oil (fuel and crude), natural gas, electric power (generated mainly by natural gas, followed by fuel oil, gas/diesel, wind and Hydro), gas/diesel and motor gasoline [4,5]. As reported by Aslani et al. [6], transportation sector is the second most energy-intensive sector, following residential sector, in the country and is currently utilizing gasoline, gas/diesel, natural gas and fuel oil as its main energy resources [4]. Also, as reported by Mousavi et al. [4], transportation has produced the second largest amounts of carbon dioxide, again following residential, on average between 2003 and 2014. Biofuel is one of the few renewable energy resources which can contribute to satisfying the energy demand of the transportation sector [7]. Therefore, biofuel production could potentially play an important role in sustainability of energy consumption and meeting the increasing gasoline demand in the near future in Iran [8,9]. In addition, biofuel production contributes to waste management, specially the extensive agricultural waste produced annually in the country, produces jobs and promotes development specially in rural areas [10]. According to Ghobadian [11], only utilizing the 17.86 million tons of wasted crops in Iran, would be able to produce 4.91 billion liters of bioethanol per year.

1- Introduction

2- Theory and hypotheses

3- Methodology

4- Results

5- Discussion and conclusion

References

Abstract

Managing supplier relationships in an environmentally responsible way may prevent risk of adverse publicity and reputational damage to the buying firm. Drawing on the stakeholder approach and the environmental management capability framework, the purpose of this paper is to gain further knowledge regarding the impact of strategies oriented to green supply chain management on a firm’s corporate reputation. We test a set of hypotheses in a panel data of European manufacturing companies for a period of ten years. Our findings provide strong support for the premise that supplier selection, monitoring and partnership termination based on environmental criteria positively influence corporate reputation. Additionally, evidence suggests that the implementation of those strategies in an integral way as well as progressing towards the adoption of green supply chain management benefit a firm’s reputation. This study has implications for theory and practice.

Introduction

A strong reputation is a decisive resource to reach and maintain competitive advantage (Bergh, Ketchen, Boyd, & Bergh, 2010; Walker, 2010). This strategic potential is due to its own value-creation capability, and to its intangible nature which are qualities that make it difficult for the competitors to imitate (Martín de Castro, Navas López, & López Sáez, 2006). A firm’s reputation is boosted through positive actions, and a suitable management of its resources and capabilities, rather than by increasing advertising or effective corporate communication (Burke, 2011; Hoejmose, Roehrich, & Grosvold, 2014). Environmental sustainability is one of the greatest current challenges that a firm must face to guarantee its legitimacy and good reputation. Governmental legislation and growing environmental concerns among society imply that companies cannot undervalue environmental issues if they want to maintain their competitive advantage. The environmental management comprises responsible use of energy, water, materials, and hazardous substances; emissions reduction, and waste management of water and materials (Porteous, Rammohan, & Lee, 2015). Reputation is not only built around the focal firm’s activities, but is also influenced by the actions developed in B2B partnerships (Money, Hillenbrand, Day, & Magnan, 2010).

1- Introduction

2- Methodology

3- Application of the model to design of RAF

4- Results and discussion

5- Conclusion

References

Abstract

In this article, a novel hybridized Multi-Attribute Decision Model (MADM) is developed to identify an optimal design of a Reconfigurable Assembly Fixture (RAF) from a set of alternative design concepts. The model combines the comparative advantage of Fuzzy Analytic Hierarchy Process (FAHP) and the computational strength of the Fuzzy Weighted Average (FWA) based on left and right scores in order to obtain aggregates for the design alternatives considering the relative importance of the design criteria as needed in the optimal design. The model was applied to evaluate four design concepts of a RAF with six design features having numerous sub-features. Results obtained from the evaluation process shows that there are differences in final values of the design alternatives. However, a close variation exists between these values. These differences can be accrued to the interrelationships between the design features and sub-features obtained from the Fuzzy Synthetic Extent (FSE) of the FAHP and an unambiguity judgment of the FWA when aggregating availability of the design features and sub-features in the design alternatives.

Introduction

Robust design of Products and industrial machineries is important from conceptualization to manufacturing and up till usage in order for manufacturers to obtain a share of the competitive market flooded with changeable designs (Olabanji, 2018). The need for these robust designs calls for development of different design concepts of a particular product or machine before a detail design analysis can be done (Song et al., 2013). The availability of alternative design concepts necessitates the need for selection of optimal design concept (Wei and Chang, 2008). Concept selection in engineering design has attracted importance in recent times because it has a direct implication on the quality of the final design. Problems that arises when it is not properly done includes; repetitive alterations and modifications of drafted designs, prolonged developmental time and amplified cost of actualization (Tiwari et al., 2017). In order to arrive at robust design of a new product or engineering component, identification of design attributes and sub features viz a viz the numerous functional requirements from the customers or intended end users becomes the first task (Ayag and Ozdem, 2007; Brackea et al., 2017). The engineering design process attempts to give a holistic approach to identification of the design attributes, sub features and functional requirements. It follows an established design standard by proposing four phases (product planning and clarification of task, conceptual design, embodiment design and detail design). These phases are usually applied to arrive at a detail design of the new product (Yeo et al., 2004; Olabanji and Mpofu, 2014). Also, the engineering design process can be imagined to have a set of eleven steps as described by (Ayag and Ozdem, 2007). The relationship between these two analogies is described in Figure 1 where these four phases are disintegrated into the eleven steps. It may be assumed that the information needed in each step and phase will also follow the same manner. However, since the concept selection step is a decision-making process, adequate information is needed for successful selection process. Concept selection in engineering design can be modelled as a multicriteria decision-making (MCDM) problem since it involves multiple design attributes that are having different sub features. Considering the steps in the engineering design process as an all-inclusive approach, it is possible to develop a relationship between the constraints, design attributes and sub features for determining optimal design concept using the multi-criteria analysis as presented in Figure 2.

Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research. Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement. Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.

Introduction

Maintenance considerably increases the budget in manufacturing industries. Even though a cost focus belongs to the past and maintenance has shifted towards being an organizational strategic capacity (Simões, Gomes & Yasin, 2011), the tradeoff between invested costs and their benefits is still of great concern for decision makers. A cost focus leads to reactive maintenance, which according to Geary, Disney and Towill (2006), potentially leads to increased disruption in real-world supply chains, causing excess variance in performance. Recent developments in terms of increased automation, more expensive equipment, and more complex production systems have required larger capital tied up in assets (Garg & Deshmukh, 2006), and proactive maintenance policies are therefore considered a necessity (Pinjala, Pintelon & Vereecke, 2006). Nonetheless, identifying appropriate practices and implementing sound strategies for developing maintenance performance are still non-trivial. A clear measure of this is the frequently-emphasized gap between theory and practice in the maintenance optimization literature (e.g. Fraser, Hvolby and Tseng (2015), Linnéusson, Ng and Aslam (2018a). One aspect of this gap is that little attention has been paid to making model results understandable to practitioners (Dekker, 1996, p.235). Moreover, Woodhouse (2001) identifies the organizational capabilities to manage the implementation of sustainable maintenance practices a crucial limiting factor. According to Baldwin and Clark (1992), capabilities such as identifiable combinations of skills, procedures, physical assets, and information systems are sources of superior performance.