Abstract

     The mission of the Institute of Industrial and Systems Engineers (IISE) is to serve those who solve complex and critical problems of the world. Notably, the research–practice gap in Operations Management (OM) marginalizes the value and relevance of the IISE. To maintain and enhance the impact of the IISE, we identify major bottlenecks that limit the industrial installation of OM research outcomes. Ranked by the relative importance, the three bottlenecks are verifying the performance improvement, building trust with practitioners, and balancing model accuracy and simplicity, respectively, in the stages of value verification, implementation and development. We propose potential research opportunities and illustrate the challenges and opportunities using real case studies from three Fortune Global 500 companies. In particular, we emphasize the role of data-driven decision methods in dealing with the three bottlenecks.

Introduction

     It is crucial for the Institute of Industrial and Systems Engineers (IISE) to guide Operations Management (OM) practitioners toward achieving OM practice excellence. The mission of the IISE is to serve OM practitioners who solve complex and critical problems encountered in the world, and often need to deal with complicated and integrated systems that suffer from customer dissatisfaction, excess inventory costs, and low product quality. Therefore, it is imperative for the IISE to capture the full potential of successful OM practice.

     However, the academia that studies OM has generally been following, rather than leading, business practices (Simchi-Levi, 2014). The inability to lead OM practices is often due to numerous failed implementations of OM models. After many years of OM projects with industry collaborators, we cannot emphasize enough how important a successful implementation is for a research project. Further, we are astonished by the difficulties associated with ensuring an OM implementation with precise and unified standards across different departments. Companies have been appropriately cautious about new OM models because they cannot fully reap its benefits (Ibanez et al., 2018; Sun et al., 2021).

Conclusions

     The future is promising as more OM research tends to bring the theoretical studies close to practical issues. The authors have extracted the data of all the 300 OM-related projects funded by the National Natural Science Foundation of China (NSFC) from 2016 to 2021. Table 1 categorizes the subjects of all the projects by new business model, new technology & Supply Chain Management (SCM) finance, product & behavior, green & health-care & agriculture, and traditional SCM. It is surprising to see that almost all the subjects are driven by real practices, and few project proposals even involve preliminary plans for industrial implementations.

     The academic community has always emphasized novel and mathematically provable methods and reproducible results, yet none of which are required for complex realworld issues (Sodhi and Tang, 2008). To thrive, the ISE research community should focus on overcoming the challenges that hinder successful practical applications and embrace cooperation with data-driven decision methods so that our research community will stay relevant and impactful. Combined with real case studies, we list and analyze the key challenges. Ultimately, these challenges can stimulate research opportunities such as user-centric algorithms and explainability. We hope that this article will encourage more ISE researchers to embark on research that can help close the gap when it comes to implementing new OM research ideas into practice.

1- Opportunity-ambiguity dilemma in emerging market categories

2- The influence of socio-cognitive cues

3- Data and methodology

4- Results

5- Discussion and conclusion

References

Abstract

Industries with emerging market categories offer greater opportunities for firms to innovate. However, such opportunities are not a matter of “the more, the better.” An increasing number of emerging market categories poses a dilemma. While more emerging market categories arguably bring about increasing growth opportunities, they can also generate greater ambiguity for incumbent firms, which may hinder their innovation efforts. This study attempts to address this dilemma by proposing that the number of emerging market categories in an industry will have an inverted U-shaped relationship with incumbent firms’ innovation efforts. We further argue that this curvilinear relationship will be influenced by the socio-cognitive context of a firm’s focal industry, in the sense that the degree of collective identity incoherence at the industry level will intensify the proposed inverted U-shaped relationship, whereas the prevalence of trade associations in the industry will depress this relationship. We test our hypotheses by examining research and development (R&D) investments of a sample of U.S. high-technology manufacturing firms and find support for our main prediction and the hypothesized effect of collective identity incoherence. We also find a surprising but intriguing moderating effect of trade associations.

Conclusions

In this study we endeavor to resolve an important but relatively understudied dilemma posed by our collective understanding of industry dynamics and emergence. On the one hand, a dominant realization in the literature that has been borne out by a significant body of empirical work, is that emergence in industries spurs innovation and growth. On the other hand, emergence can pose significant socio-cognitive challenges for industry incumbents due to its inherent ambiguity. We have tackled this paradox in industries belonging to the high-tech U.S. manufacturing sector and have empirically assessed the effects of the multiplicity of emerging market categories in such industries on innovation-related strategic behaviors of incumbent firms. Our findings suggest a co-existence of both opportunity and ambiguity, and the relative prevalence of one over the other is largely driven by an increasing multiplicity of emerging market categories and the socio-cognitive context of an industry. We believe that the explicit emphasis on the effect of this multiplicity on the opportunity-ambiguity tension in emerging contexts contributes to our collective knowledge of the interplay between market emergence and innovation.

1- Introduction

2- Material and methods

3- Extended producer responsibility (EPR) concept and experiences

4- The context of EPR implementation in Brazil

5- The case study: change management in São Paulo EPR implementation

6- Case study evaluation and discussion of results

7- Conclusions

References

Abstract

Contemporary environmental challenges have led governments to seek innovative ways to overcome the limits of traditional regulatory models, demanding a “change management”. This article addresses a case study regarding innovations promoted in the extended-producer responsibility (EPR) implementation by São Paulo state, Brazil. After facing resistance from industry, local government changed its approach from traditional regulation to negotiated agreements. As one of the researchers was an active part of the process, the Active Research method was adopted, through the “changing as three steps” model, aiming to understand how change occurs and what its critical factors (facilitators, barriers and improvement opportunities). Results indicate that the incorporation of the principles of regulatory quality has made it possible to overcome the gridlock and reopen the dialogue, leading to agreements between industry and government. The evidence also shows that the EPR strategy has been continued and expanded, with the renewal of commitments with more ambitious goals, besides the inclusion of retailers in some EPR systems. Regulation has also evolved, with the incorporation of EPR in the environmental permitting, intending to combat free riders. Facilitators for change included recognition of the government, a gradual approach, cultural changes in companies and the systematization of information. Among the barriers, the most crucial were the low adherence of retailers and importers, the considerable presence of free riders, difficulties with the participation of municipalities, the absence of tax incentives, the low adhesion of generators, high operating costs, and collection problems. Finally, among the opportunities for improvement, results show the expansion of participants, mainly municipalities and retailers, a tax review, inclusion in environmental education programs, incorporation in environmental permitting, increased surveillance to combat free riders, and incentives to the recycling chain. Finally, the conclusions emphasize the success in incorporating principles of environmental regulatory quality in the São Paulo EPR implementation strategy. This fact strengthens the conclusions of previous research and suggests that its incorporation in policy design has the potential to overcome implementation gridlocks in complex environmental regulations.

Introduction

Traditional regulation faces limits in dealing with contemporary environmental problems, highlighting the need to develop and apply new policy approaches (Fiorino, 2006). Additional hindrances occur in cases of complex regulations, in which resistance from the regulated community demands a “change management” from the environmental agencies (Mol et al., 2009). This transition is even harder in developing countries, where institutional fragility brings additional challenges for regulation to evolve (Ribeiro and Kruglianskas, 2015; OECD, 2016). This paper presents a successful experience in how change management can overcome the hurdles of a complex new environmental regulation in a developing country. Adopting action research and the CATS model (Lewin, 1947) as methodological approaches, the study evaluates the experience of the São Paulo State Environmental Agency (CETESB), Brazil, to implement a sub-national extended-producer responsibility (EPR) policy.

Methods

Results

Discussion

Conclusion

References

Abstract

Purpose: The objective of this study was to examine adolescents’ self-reported exposure to cannabis marketing in states with legalized cannabis and its association with past-year cannabis use. Methods: We conducted a cross-sectional, online panel survey of 469 adolescents aged 15e19 years residing in four states with legal retail cannabis for adult use. Adolescents self-reported exposure to cannabis marketing on social or traditional media (i.e., outdoor or print) and past-year cannabis use. Logistic regression generated estimated odds of youths’ past-year cannabis use by marketing exposure after adjusting for demographic factors and cannabis-related social norms. Results: Exposure to cannabis marketing on Facebook, Twitter, and Instagram was associated with increased odds of past-year cannabis use of 96% (95% confidence interval [CI]: 15%e234%), 88% (95% CI: 11%e219%), and 129% (95% CI: 32%e287%), respectively. Odds of past-year cannabis use increased by 48% (95% CI: 16%e87%) with each additional social media platform where adolescents reported exposure. Conclusions: Despite restrictions that prohibit cannabis advertising on social media, adolescents are exposed to cannabis marketing via social media, and this exposure is associated with recent cannabis use. States should consider further regulation of cannabis marketing on social media.

Results

The analytic sample included 469 participants (70% female, 26% male, and 4% other) after excluding 28 youth with missing data on cannabis use or demographic factors (Table 1). A majority of participants (61%) were from California, with 13.6% from Colorado, 7.9% from Nevada, and 17.9% from the State of Washington. All adolescents in the sample reported using at least one social media platform. Most adolescents (93.6%) in the sample reported some form of exposure to cannabis marketing (Table 2). A majority of adolescents reported seeing cannabis marketing on at least one social media platform, with a higher proportion of past-year cannabis users reporting exposure (89.6%) than nonusers (74.0%; p < .001). Of the three social media platforms examined, the proportion of adolescents reporting exposure to marketing via Facebook was highest, and more past-year cannabis users reported exposure on Facebook (72.4%) than those who did not use cannabis in the past year (52.5%, p < .001). Of the traditional platforms, exposure to outdoor advertising (e.g., billboards) was more common (72.9%) than exposure via newspapers or magazines (43.1%). A higher proportion of cannabis users (79.9%) reported exposure to outdoor advertising than nonusers (70.1%; p ¼ .033). Cannabis marketing exposure on Instagram was higher for blacks than for other racial/ethnic groups and on Facebook and Twitter for older adolescents versus younger adolescents, but no other differences in exposure were observed by demographic variables (Supplemental Table S1). When controlling for demographic factors, injunctive norms about cannabis use, cannabis outcome expectancies, and the interaction between gender and cannabis outcome expectancies, we found that past-year cannabis use was positively associated with perceived exposure to cannabis marketing. The increase was highest for Instagram (odds ratio [OR] ¼ 2.29; 95% confidence interval [CI]: 1.32e3.97), followed by Facebook (OR ¼ 1.96; 95% CI: 1.15e3.34), and Twitter (OR ¼ 1.88; 95% CI: 1.11e3.19) (Table 3). There were no associations between exposure to cannabis advertising outdoors (e.g., billboards) or via newspapers/magazines and past-year cannabis use (Supplemental Table S2).

1- Introduction

2- Innovation measurement in the bioeconomy

3- Innovation indicators for the bioeconomy

4- Discussion and ways forward

5- Conclusion

References

Abstract

Innovations in the bioeconomy are expected to provide new solutions to major economic, societal and ecological challenges like resource depletion, food insecurity or climate change. However, information about innovation activities in the bioeconomy and its outcomes is scattered and more systematic measurement efforts are useful for policy making to assess its impact and whether objectives are met. This article provides an overview of information needs and data availability for innovation indicators. Furthermore, data for key input and throughput indicators are presented and discussed for the bioeconomy in Germany. The data indicates a rather strong role of Germany for publications and patents. However, the commercial success remains unclear, because of current limitations in information availability about the output and outcome of innovations efforts. Here, the most critical information gap in exist. In order to improve this situation additional data collection such as innovation survey for the bioeconomy would be needed.

Introduction

The bioeconomy is expected to provide solutions to major economic, societal and ecological challenges like resource depletion, food insecurity or climate change. It is expected that new applications are developed and existing markets will be transformed. Hence, the bioeconomy may have significant impact on value added, employment, distribution of profits and sustainability. To achieve these goals, several developments such as appropriate political framework conditions or better addressment of societal concerns are needed. Moreover, significant progress in the production and use of biogenic resources needed and further innovation is necessary. Life and biological sciences and technologies are required to provide superior performance of bio-based processes, products and services, ensure their sustainability and to improve cost-competitiveness. Indeed, there is a high innovation potential in the bioeconomy ranging from the usage of mostly untapped feedstock (CO2, waste, algae), optimized microorganisms, digitalization in farming, social innovations (urban gardening, collective agriculture, etc.) [1, 2]. Bio-based innovations are not only relevant in high-tech sectors, but also in traditional segments, such as the provision of new materials in textiles (e.g. spider silk) or developing new protein alternatives in the food sectors. Many strategies emphasize the need for new innovations to achieve the respective goals [3–5]. Various countries are pursuing large efforts to support R&D and innovation in the bioeconomy with significant advances. However, information about the progress of the bio-economy and in particular regarding activities and outcomes of these innovation activities is scattered.

1- Introduction

2- Literature review

3- Variables and assumptions

4- Proposed model

5- Solution and analysis

6- Conclusion

References

Abstract

This research focuses on the problems that enterprises are affected by multiple stakeholders in the process of lean management. Based on the dynamic game theory, through the construction of a three-stage game model, the article has researched on the interaction mechanism between internal and external stakeholders that affects the implementation of enterprise lean management. Through the research, the following conclusions are drawn: (1) At the beginning of lean management, hiring the third-party consultant experts to train employees, which is conducive to improving management efficiency; (2) the effort level of middle managers has nothing to do with the control of senior managers; (3) senior managers should not be too many constraints on third-party consultant experts; (4) senior managers should strive to coordinate the interests of all parties. The purpose of the paper is aimed to provide a reference of theory and practice of the implementation of lean management for enterprises.

Introduction

Lean management is derived from lean production, a type of production management based on the prototype of Toyota production system proposed by experts and scholars, and organized in 1990 at the Massachusetts Institute of Technology after more than 90 automobile factories were investigated and a comparative analysis was carried out around the world involving 17 countries in the International Motor Vehicle Program (IMVP). Womack et al., who put in place the guiding principles of lean manufacturing at the corporate strategy level, proposed the concept of lean thinking, and use it as the foundation of lean management, which applies lean thinking to enterprise management activities (Taylor, Taylor, & Mcsweeney, 2013). Lean management takes “full participation, continuous improvement, [eliminating] waste, [creating] value and [stimulating] positive energy” as its core. Its remarkable innovation performance has demonstrated its validity and advantages, and, led by the manufacturing enterprises, has changed the trajectory of the development of enterprises over the past few decades (Jasti & Kodali, 2015). A large number of successful cases of lean management practice show that lean management plays an important role in enhancing corporate governance innovation. Lean management can, that is, help enterprises to improve their levels of management innovation Jing, Niu, & Chang, 2015). However, currently, the effects of the implementation of lean management are not ideal. Baker (2002) conducts an effect survey of enterprises implementing lean management in the United Kingdom and Australia, and shows that less than 10% of enterprises achieve a higher lean level. In an identical survey conducted in the US, only 26% of those companies implementing lean management obtain better lean implementation effects (Blanchard, 2007; Comm & Mathaisel, 2005). The lean management implementation status of enterprises is even less desirable in China; many enterprises see results after long periods of time, but it is difficult to achieve lean management in the continuing operations of enterprises and some enterprises even appear to exhibit the “retrograde step”, in which many aspects of production and operations return to the state prior to the introduction of the lean management model. There are two primary reasons why the effects of implementing lean management have not been significant: (1) knowledge of lean management is scarce, and (2) not enough staff members participate in lean management implementation.

1. Introduction

2. Or and ethics

3. An ethical framework for data science

4. Research approach

5. Diagnosis and planning

6. Intervention (action taking)

7. Evaluation (assessment)

8. Reflection (learning)

9. Summary

References

Abstract

The ethical aspects of data science and artificial intelligence have become a major issue. Organisations that deploy data scientists and operational researchers (OR) must address the ethical implications of their use of data and algorithms. We review the OR and data science literature on ethics and find that this work is pitched at the level of guiding principles and frameworks and fails to provide a practical and grounded approach that can be used by practitioners as part of the analytics development process. Further, given the advent of the General Data Protection Regulation (GDPR) an ethical dimension is likely to become an increasingly important aspect of analytics development. Drawing on the business analytics methodology (BAM) developed by Hindle and Vidgen (2018) we tackle this challenge through action research with a pseudonymous online travel company, EuroTravel. The method that emerges uses an opportunity canvas and a business ethics canvas to explore value creation and ethical aspects jointly. The business ethics canvas draws on the Markkula Center’s five ethical principles (utility, rights, justice, common good, and virtue) to which explicit consideration of stakeholders is added. A contribution of the paper is to show how an ethical dimension can be embedded in the everyday exploration of analytics development opportunities, as distinct from a stand-alone ethical decision-making tool or as an overlay of a general set of guiding principles. We also propose that value and ethics should not be viewed as separate entities, rather they should be seen as inseparable and intertwined.

Introduction

Business analytics is playing a greater and greater role in our daily lives, impacting on job applications, medical treatment, parole eligibility, and loans and financial services. There are undoubted benefits to algorithmic decision-making in general, and artificial intelligence (AI) in particular. For example, AI is being used to detect the early stages of colorectal cancer, achieving 86% accuracy (Mukherjee, 2017). Such is the interest in AI for healthcare that the UK Government is pledging millions to AI applications for the early diagnosis of cancer and other chronic diseases, using patient data and lifestyle information to highlight patients at risk (Perkins, 2018). However, algorithmic decision-making is not without its dark side. Mann and O’Neill (2016) question the use of algorithms in hiring decisions, d’Alessandro, O’Neil, and LaGatta (2017) raise concerns about predictive policing. The Cambridge Analytica case has thrust data analytics squarely into the public domain. It is alleged that Cambridge Analytica collected data from more than 50 million Facebook users (without permission) and used that data to build a system to target US voters with personalized political advertisements with the aim of influencing the US election outcome (Greenfield, 2018). Unsurprisingly, algorithmic decision-making is attracting the interest of researchers as well as practitioner and regulators (e.g., Kitchin, 2017; Newell & Mirabelli, 2015).

1. Introduction

2. Background

3. Method

4. Findings and discussion

5. Conclusion

Acknowledgements

References

Abstract

This paper systematically reviews the drivers, implementation processes and performance outcomes of environmental management systems (EMS) in small to medium-sized enterprises (SMEs) from a management accounting and control (MAC) stance. It finds that there are various contextual, control and performance themes which require research attention. Regarding context, there is the need to: a) explore the relationship between firm size and the development of sustainability tools; b) assess the impact of context for better environmental performance and c) on adoption motivations; and d) explore how EMS affect corporate and individual accountability. Regarding control, there is the need to: a) theoretically explore the control typologies for SMEs; b) better understand the type (i.e. formal or informal) and nature (i.e. tight or flexible) of this control; and c) develop understandings of socio-ideological controls for improved sustainability. Finally, regarding performance, there is the need to: a) understand how environmental performance is defined and the interaction between its dimensions; and b) explore the relationship between EMS and environmental performance in SMEs. The paper serves as a first step to understanding MAC in SMEs, highlighting contemporary issues that are relevant for academic and professional sustainability practice.

Introduction

Over recent years, much research has been conducted on the adoption and implementation of environmental management systems (EMS) in small to mediumsized enterprises (SMEs). Regarded as ‘voluntary’ certification schemes, EMS support the integration of sustainability into corporate policy (Hahn et al., 2015). In the management accounting and control (MAC) literature, EMS are seen as part of a broader framework of accounting tools to strategically improve both financial and nonfinancial performance outcomes (see Guenther et al., 2016) or as “management controls that arise from compliance frameworks” (Ahrens and Khalifa, 2015, p. 15). Nevertheless, research into EMS rarely features in the MAC stream (see HerasSaizarbitoria et al., 2011), even though EMS are increasingly adopted by firms – big and small – to demonstrate environment management in particular and sustainable development in general (see Aragón-Correa et al., 2008).

Defined in Europe as companies with less than 250 employees or a turnover of ≤ €50m (2003/361/EC), SMEs are important to the world economically, ecologically and socially (Stubblefield Loucks et al., 2010). SMEs constitute around 99% of Europe’s businesses, employ two-thirds of its citizens (Airaksinen et al., 2015) and, more crucially, generate most of its environmental pollution (ECAP; Kearins et al., 2010). Nevertheless, research into environmental management in SMEs is lacking from a MAC stance (Lavia López and Hiebl, 2015; Ghosh et al., 2019). Particularly, sustainability MAC research tends to focus on large firms with established control systems or fails to differentiate between firm size (e.g. Henri and Journeault, 2010; Pondeville, Swaen and De Rongé, 2013; Crutzen, Zvezdov and Schaltegger, 2017). However, SMEs are characteristically distinct from larger firms in terms of size, ownership, strategy, structure, resources, internal systems (Hillary, 2004; Stubblefield Loucks et al., 2010), and – as some argue – attitudes towards environmental issues (Perez‐Sanchez, Barton and Bower, 2003). Consequently, the tools developed for larger firms are often difficult for SMEs to operationalise (Jenkins, 2004) and downscale to their needs (Stubblefield Loucks et al., 2010; Lavia López and Hiebl 2015).

1- Introduction

2- Literature review

3- Model setup

4- Equilibrium analysis

5-  Comparative analysis

6- Supply chain coordination

7- Conclusions

References

Abstract

The retail industry is accelerating the transition from multi-channel to omni-channel. A display showroom is a main mode of operation in omni-channel retailing. In this, consumers find products in an online channel, experience products and receive services in offline showrooms, and make a purchase by placing an order online or offline. In practice, the online channel (offline service) can be opened (invested in) by the manufacturer or the retailer. This paper explores the relevant issues by establishing and comparing four kinds of Stackelberg game models: (1) the manufacturer simultaneously opens an online channel and invests in the offline service (MM mode; (2) the manufacturer opens an online channel, but the retailer invests in the offline service (MR mode); (3) the retailer opens an online channel, but the manufacturer invests in the offline service (RM mode); and (4) the retailer simultaneously opens an online channel and invests in the offline service (RR mode). In these models, the online channel and offline channel cooperate through a display showroom. The results show that regardless of the kind of channel structure, a display showroom can generate benefits for the manufacturer, the retailer and the whole omni-channel supply chain. And from the perspectives of the manufacturer and the whole supply chain, if the consumer service perception degree is low, the price competition degree is high (low), and the service cooperation degree are high (low), and the MM (MR) mode is the optimal channel structure. Otherwise, if the consumer service perception degree is high, the RR mode is the most efficient channel structure for the manufacturer and the omni-channel supply chain. For the retailer, the RR mode is always the best channel structure. The improved revenue-sharing contract and two-part tariff contract can achieve full coordination of and improvement in the operational efficiency of the omni-channel supply chain and achieve Pareto improvement for the supply chain members.

Introduction

Along with the acceleration of technology evolution and the ongoing trend of digitalization, various channels, such as the Internet, mobile phones, and physical stores have become available for customers to interact with the retailers. However, traditionally, most multichannel retailers have siloed structures, where the physical store division and the Internet store division operate independently of each other (Gallino and Moreno, 2014). Now, some brick-and-mortar retailers are exploring integration strategies for their offline and online channels to enrich the customer value proposition. As a result, achieving the integration of information and services from multiple available channels is becoming a high priority for retailers, representing a shift from a multi-channel to an omni-channel approach. An omni-channel approach aims to coordinate the fragmented service processes and technologies in the various channels to deliver a consistent and integrated cross-channel experience for customers. In omni-channel retailing, consumers are becoming sophisticated enough to optimize their shopping experience by exhaustively considering all possible alternatives across all possible channels (Chopra, 2016). Firms provide better consumer experience by integrating advantageous resources online and offline, as well as adopting cross-channel cooperation. The collaboration projects include displays, services, advertisements, promotions, inventories, and logistics. Evidence shows that offline display showrooms (ODS) (Gao and Su, 2017a; Bell et al., 2018) is one of the main kinds of omni-channel retailing modes.

1- Introduction

2- Literature review

3- Methodology

4- MCDM for boxboard production

5- Sensitivity analysis

6- Conclusion

References

Abstract

Papermaking is a capital-intensive industry that requires a high consumption of plant fibers, energy, and water. Previous sustainability assessments of papermaking industry primarily focused on separate evaluations for multiple criteria without the integration for criteria and could not compare the overall priority of the production alternatives. The life cycle sustainability for the most representative boxboard production is analyzed as a case study in this work. Life cycle water consumption, energy consumption, greenhouse gas emissions, and internal costs are selected as the assessment criteria. The two multi-criteria decision-making methods are applied to integrate the above criteria to obtain the sustainability sequence under different production pathways. When the papermaking enterprises are regarded as decision-makers, the alternative using waste paper as raw material to manufacture boxboard is the most sustainable, following by mixed fiber. The sustainability sequence of the alternatives using wood and straw as raw materials is controversial due to the different calculation models. Changing the proportion of raw materials and the criteria weights might adjust sustainability sequence of the alternatives.

Introduction

Papermaking is a capital-intensive industry with a long industrial chain, it contributes to about 3% of global end uses of energy (Ma et al., 2018), 2% of global industrial carbon emissions (Bajpai, 2016), and 40% of global industrial wastewater (Ashrafi et al., 2015). In China, the rapid development of papermaking industry brings extensive energy demand and high emission generation, the sustainable development of papermaking industry has received widespread attention (Ingwersen et al., 2016; Wang et al., 2016; Corcelli et al., 2018). As the largest paper producer around the world, the papermaking industry in China is surrounded by several challenges: the energy structure dominated by coal leads to significant emissions of greenhouse gases (GHG) (Wang et al., 2016); the technology level varies greatly and too much capital is invested for unit product in some enterprises; low forest cover (Zhu et al., 2017) and policies on restricting deforestation prompt that papermaking industry has to import large quantities of wood and pulp board, resulting in increased cost and material replacement. Therefore, in order to achieve sustainable development of the papermaking industry, it needs to overcome environmental, economic and social obstacles. Since 2007, the production of boxboard has headed the list in China’s papermaking industry for 11 years and it accounted for 21.4% of the total paper production in 2017. Boxboard papermaking industry is still in a rising state with the development of packaging and logistics industries (CPA, 2017). The boxboard papermaking enterprises are widely distributed in every province in China, most of them use recycled waste paper to produce non-deinked pulp to manufacture boxboard, some sorts of high-grade boxboard are manufactured by using natural wood pulp cooked from wood chips or using natural straw pulp, the latter is mostly distributed in the major straw-producing area in north China. Besides, mixing virgin pulp and waste paper pulp as a raw material is also a common solution for the production of boxboard. Variety of production pathways have a different effect on the sustainability criteria for papermaking industry. The industrial process assessment (Wen et al., 2016; Li et al., 2015) and life cycle analysis (Man et al., 2018a; Ewijk et al., 2018) of papermaking industry analyzed environmental and economic criteria from different boundaries.

1- Introduction

2- Key concepts – Decentralized versus centralized decision-making

3- How decentralization impacts operational safety – evidence from a case study

4- Conclusions

References

Abstract

This paper (Part 1) is about the influence of organizational structures on the way major accident risks are managed. It discusses how decentralization, meaning the dispersion of decision-making autonomy within the company, undermines operational safety. A case study is presented, and three real situations experienced by an oil and gas company are described, revealing how the decentralized structure contributed to the negative outcomes observed in each case. The examples demonstrate the need for an operational safety structure with a higher degree of centralization and a greater independence from business pressures. Then, in a separate paper (Part 2) following on from this discussion, the authors propose a design strategy to strengthen the operational safety function. In the suggested structure, a more centralized and independent control of risks is achieved, without losing the ability to quickly identify and effectively address the safety issues at the asset level.

Introduction

Man-made disasters in different high hazard industries have proven the influence of organizational factors in the development of such accidents. Failures in the acquisition, analysis and flow of relevant information have contributed to an inadequate understanding of the operational risks, leading to a poor decision-making marked by a riskblind or even a risk-denying that is only perceived in a retrospective view of these catastrophes. Information flows and decision processes cut across a company’s structure and are largely dependent on the organizational design. The structures in place directly affect the ability an organization has to identify, make sense of, and escalate whatever bad news there may be about safety to top-level managers who have the power and authority to effectively respond and act on them. This paper is mainly concerned with improving the understanding on how structures affect catastrophic risks management. A better comprehension on this organizational factor can enable major hazard companies to identify their design weaknesses and act on them before these vulnerabilities contribute to accidents. In the oil and gas industry, the influence of organizational structure was recognized as a contributing factor to the British Petroleum (BP) Texas City refinery accident, in 2005. The BP US Refineries Independent Safety Review Panel concluded that: “BP’s decentralized management system and entrepreneurial culture have delegated substantial discretion to U.S. refinery managers without clearly defining process safety expectations, responsibilities, or accountabilities” (BP, 2007, p.94). The BP’s decentralized system to manage process safety deserves further explanations. Hopkins (2008) clarifies that: “A decentralized structure means that decisions about how a particular site will operate are made, as far as possible, at the site or local business level rather than at head office” (Hopkins, 2008, p.91). Hence, establishing safety as a decentralized function means that the decisions that could impact the management of major hazards at BP facilities were made at business unit level, with little or even no influence or oversight by the corporate safety experts. Now, add to this decentralized management system, a lack of understanding about process safety and its differences from personal safety, and it is possible to visualize the state of organizational confusion established within the company where it was not always clear who was responsible for process safety related issues. Another major accident where the BP organizational structure acted as a contributing factor was the Gulf of Mexico blowout, in 2010, frequently referred as the Macondo disaster.

1- Introduction

2- Literature review

3- Gresilient supplier selection: Research methodology

4- Application and results

5- Conclusions: Towards gresilient sourcing

References

Abstract

The vast majority of current literature handle supplier selection problem considering green and resilience aspects, bifurcately. But green supply chain performance is subject to disruption by internal /or external events. This initiates the need for a merged management approach to establish green and resilient supply chains, that we hereby present it as “gresilient” supply chain management. At the institutional level, the integration of measures of these two paradigms into supply chain processes frets substantial management challenges. This research intends to overcome some of these challenges in exploring, identifying and quantifying resiliency and greenness performance in supplier selection context. To this end, this work aims to: (1) explore and analyse the need and complementarities for or between resiliency and greenness in supply chain context; (2) identify the criteria of greenness and resiliency (a new framework for resilient supplier was developed in considering criteria of development, agility, robustness, sensing and flexibility (DARSF) into a unified framework; (3) quantify the gresilience performance measures by proposing a quantitative approach to evaluate the relative importance of the ‘gresilience’ criteria and suppliers’ gresilience performance via multi-criteria decision making (MCDM) algorithms. The main findings are: (i) a holistic gresilient supplier evaluation framework; and (ii) a user-friendly decision-making tool to gauge gresilience performance of suppliers. This research provides them with a clear insight towards gresilient advantages in today’ highly competitive business. This research bridges the literature gap in addressing the supplier selection problem from resiliency and greenness perspectives, jointly.

Introduction

In the last two decades, the business has experienced several developments and changes that make firms increasingly encounter new challenges such as globalization. Hence, the market is becoming highly challenging and competitive, and thus modern supply chain management should be accomplished to adopt these changes. Supplier assessment/selection is a vital decision among the activities of supply chain management (Damert et al., 2018 and Tseng et al., 2019). Where enterprises have become highly willing to improve sourcing in attaining a pool of robust suppliers towards a competitive industry. This is relied on the fact that purchasing dominates a share of 40 %–70% out of total cost, and so it has a significant impact on the price and superiority of products (Dyer and Singh, 1998). In the supplier selection process, decision makers (in the purchasing team) find, assess, select and contract suppliers. As mentioned previously, this process poses a significant share (i.e., an average of 55%) of an organization’s financial assets. Organizations hereby seek benefits in signing with suppliers to present high performance in return. Arguably, the core of this activity is based on supplier performance measures that is normally determined vis-à-vis several criteria; further details on supplier assessment process is presented in Section 2.4. Traditionally, purchasing costs, products quality and lead time are among the most popular criteria in supplier assessment (Chai and Ngai, 2020; Guarnieri and Trojan, 2019; Mohammed, 2019 and Ho et al., 2010). This was based on a previous study presented. Arguably, the latter presented the first thorough overview for supplier assessment criteria. The author reported a list of 23 criteria in addition to its importance. Sustainable supply chain management (SSCM) was released to the field of operations and supply chain management by consolidating sustainable goals growth and SCM (Nujoom et al., 2019 & Nujoom et al., 2018a). Since then, the niche of SSCM is gaining a growing attention (Maditati et al., 2018; Ghadimi et al., 2019; Nujoom et al., 2016; Nujoom et al., 2018b and Jakhar et al., 2018). SSCM can be presented as “the integration of supply chain management activities through improved supply chain relationships to achieve a sustainable competitive advantage” Handfield and Nichols (1999). Supplier development, with a focus on sustainable development goals, plays a significant role towards SSCM advantages (Mohammed, 2019).