Abstract

The Balanced Scorecard (BSC) is a performance management tool that analyzes the relationship between tangible and intangible assets. Its ability to render business strategy visible has earned it the distinction of being a strategic management model. Sustainability has become an important part of daily and business life today. With the increasing importance given by stakeholders to environmental and social issues, companies find it necessary to evaluate sustainability activities in their performance evaluations. In order to meet this need, the Sustainability Balanced Scorecard (SBSC) emerged with the addition of a sustainability perspective or parameters to BSC. The number of academic studies pertaining to the application of BSC, which numerous companies and subjects to research have utilized, is inadequate. Due to its hierarchical structure and the necessity to evaluate numerous criteria, BSC is suitable for using multi-criteria decision-making (MCDM) methods. In this paper, an integrated approach consisting of SBSC, the Picture Fuzzy Analytic Hierarchy Process (PF-AHP), and the Objective Matrix (OMAX) method is proposed for the performance measurement of companies. The PF-AHP method has been reorganized for ease of application in determining the perspectives, strategic objectives, and Key Performance Indicators (KPIs) weights within SBSC. Additionally, the OMAX method has been employed for calculating the performance scores. This proposed approach was implemented in a factory operating in the glass industry. The SBSC created for the company includes five perspectives, 16 strategic objectives, and 34 KPIs. PF-AHP method was used to determine the weights of perspectives, strategic objectives, and KPIs to be used in the performance score calculation with OMAX. The weight of financial perspective is 0.267, the weight of the customer perspective is 0.247, the weight of the internal process perspective is 0.191, the weight of the learning and development perspective is 0.161, and the weight of the sustainability perspective is 0.134. The evaluations made by the decision-makers indicate that the company’s performance measurement still adheres to the traditional framework. The performance score of the company is 6.14 out of 10.

Introduction

The concept of sustainability, which includes economic, social, and environmental perspectives, has become an important issue in today’s world in order to cope with global challenges such as increasing poverty and climate change. This situation has brought pressure from stakeholders on terms of sustainability. At this point, the concept of corporate sustainability has emerged. Corporate sustainability is defined as meeting the needs of its direct and indirect stakeholders without compromising its ability to meet their future needs, taking into account both the administrative and operational activities of the organization (Nicoletti Junior et al., 2018). In addition, corporate sustainability also stands out as systematic management efforts for companies to voluntarily integrate environmental and social issues into management (Hansen and Schalteggar, 2016).

National and international legislation on sustainability perspectives is being developed (Kaplan and Norton, 2004), but reporting of social and environmental activities is still voluntary for companies (Huang et al., 2014). However, the importance given to the concept of sustainability will create a difference among competitors (Nicoletti Junior et al., 2018).

For a company to be successful in terms of corporate sustainability, it is not enough to be successful in a single perspective of sustainability. Corporate sustainability aims for companies to make progress in all three perspectives of sustainability (economic, environmental, and social) (Eifert and Julmi, 2022). Companies adopt environmental management systems practices to achieve the goals they set in order to meet the social and environmental expectations of stakeholders. However, these applied environmental management systems cannot fully reveal the contribution of sustainability performance to economic performance (Tsalis et al., 2013). As technological developments towards sustainability are increasing, companies need an effective tool to achieve their sustainability goals (Al-Mawali, 2023).

Conclusion

In order to succeed in their competitive environment, companies should embrace strategic management and measure the effectiveness of their management systems. The BSC has emerged as a performance measurement method to assess this success. The traditional BSC consists of four perspectives: financial, customer, internal processes, and learning and growth. With the growing importance of the concept of sustainability, which defines the measurement of environmental and social performance, the sustainability perspective has been added as the fifth perspective to the BSC, giving rise to the concept of SBSC. Since its introduction to the literature, the BSC has maintained its popularity and applied in numerous companies and investigated in various research studies. The multi-criteria nature of the BSC has led researchers to integrate it with MCDM.

In this paper, the modified PF-AHP method was used to weight the criteria in the SBSC. Additionally, the SBSC was combined with the OMAX method to express the performance numerically. Firstly, a team was selected within the company to create the SBSC, and strategic objectives, KPIs and indicators were identified through references from the literature and the company’s knowledge base. Existing data was collected for calculating the performance score, and based on this data, targets were established. The weights for SBSC perspectives, objectives, and KPIs were calculated using the PF-AHP method with the input of opinions from five DMs. The obtained weight values were then utilized in the OMAX method to determine the company’s performance score.

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.

Abstract

1. Introduction

2. Background and review of literature

3. Discussion

4. Conclusion and Future Scope

Acknowledgment

References

Abstract

     Enterprise Resource Planning (ERP) system is a collection of collaborative software programs. It handles transactions through enterprise-wide business processes using shared databases, standard methodologies, and data exchange across and within functional domains. Setting up an enterprise system is a complex activity and a costly and dangerous investment. Further, ERP system potentially affects core business and supporting processes, especially in complex and cyber-physical domains such as Industrial Internet of Things (IIoT) and Smart Factory. Cloud ERP (C-ERP) and Edge ERP (E-ERP) are alternatives to traditional, centralised and monolithic ERP implementation for incorporating the benefits of Cloud and Edge Computing. Their main benefits include ease of use, resource balancing, bandwidth, cost-saving, and higher privacy/security. This paper discusses the benefits and limitations of using C-ERP and E- ERP in IIoT and Smart Factory domains, along with future directions in the ERP era of demand.

Introduction

     One of the most widely utilised information technology (IT) solutions in organisations are ERP systems [1]. These are well-organised collections of software that works together to timely integrate business processes and support the management of effective cross-functional operations within an organisation. In particular, in ERP systems, a single database has all of the information. It acts as a central location for storing, distributing, and sending data between departments and business processes to manage transactions and data exchange across and within different functional areas. Setting up such a complex, shared and monolith system as an ERP is more than a computer project. It’s a costly and hazardous investment, and the process impacts the organisation’s core and support operations [2]. However, business operations’ technical and functional integration to synchronise the flow of information with the content flow of products or services is very attractive for the companies, representing one of the significant reasons for ERP adoption [3] [4].

     Simultaneously, companies can also benefit from novel enabling technologies such as Cloud and Edge computing. First, they provide the capability to process and store large amounts of data. Second, they allow continuous and realtime information sharing between customers and consumers; and third, they can rapidly and agilely respond to consumer behaviour and organisation needs [5]. The main benefits and features of Cloud and Edge computing perfectly match with ERP systems’ needs. Indeed, Cloud-based ERP (C-ERP) and Edge-based ERP (E-ERP) systems consist of a host ERP that operates, respectively, on a cloud and edge provider’s infrastructure. These infrastructures include software, hardware, storage, networks and other resources which can overcome the limits of conventional, centralised ERP systems. C-ERP and E-ERP solutions have been recently developed for many application domains, including the Industrial Internet of Things (IIoT) and Smart Factories. ERP systems can simplify the facility and business automation and support the integration of internal/ external resources and management decisions. Indeed, especially in such domains, bringing data and cyber-physical processes together by simultaneously complying with business and organisational needs is key for addressing the increasingly complex challenges of economy 4.0.

Conclusion and Future Scope

     The adoption of C-ERP and E-ERP is a strategic choice made by any industry. ERP’s competitive advantage stems from its integration with newer technologies (IIoT), which hides the constant need for change in an organisation’s operation. The study of the industrial decision-making process began with examining the factors that impact C-ERP and E-ERP adoption. E-ERP solutions can beat C-ERP solutions in terms of faster provisional delivery, better query execution, and closeness to the client. Edge computing may also be utilised in a variety of ERP systems and modules. Edge nodes may handle specific task allocations right at the edge network, simplifying and dividing activities to be done partially at the edge and transmitting only the most intensive computing jobs to C-ERP. Finally, the E-ERP model delivers real-time data, making it a more effective and efficient decision-making and reporting tool. Sharing workloads with the central ERP application server and controlling partial execution at the edge nodes achieve low latency and high performance.

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

1 Introduction

2 Literature review

3 Methodology used in research

4 Problem formulation

5 Proposed research methodology

6 Case study on ERP consultant selection problem

7 Sensitivity analysis

8 Discussion and conclusions

Declarations

References

Abstract

Enterprise Resource Planning (ERP) projects are risk prone due to software complexity, budget overruns, long duration, poor project delivery and a broad range of organization transformation processes. Companies are hiring experienced external consultants to achieve successful implementation of ERP system. Proper selection of ERP consultant is challenging task for any top management of organization because consultant fees accounts a major portion of project cost and this problem may be considered as Multi criteria decision-making problem. This article presents the hybrid approach of Fuzzy Analytical hierarchy process (F-AHP) and COPRAS-G in selection process of ERP consultants. F-AHP is used to obtain the weight of selection criteria and COPRAS-G is used to get the final ranking of consultants based on utility degree. The proposed model is illustrated with a numerical example whose data has been collected from survey in Indian industries. Finally, sensitivity analysis was performed to determine the robustness of the approached. The result of F- AHP highlights that reputation, ERP project experience and partner of ERP vendor and cost is an important selection criterion for selecting consultant and rank reversal problem has been greatly reduced by COPRAS-G. This proposed hybrid technique is quite flexible and provides efficient decision making in the selection of an ERP consultant.

1 Introduction
Companies are investing huge amount in information technology systems so as to meet the challenges of business organizations and smooth flow of information in the industries. ERP is an advanced information technology tool which supports business functions and information flow of an organization in a single software system. Shadi et al. (2019) has defined ERP is an Information Technology (IT) business solution which enables Organizations and their Stakeholders for management of project effectively & efficiently throughout the lifecycle of project. Thus, ERP system is amalgamation of business, technology and human resource. Implementation of ERP system has been considered among the most challenging investment projects due to the high costs involved, complexity and higher duration (Yusuf et al. 2004). Continuous high failure rates of ERP projects are major concern in recent years. Aloini et al. (2007)

1- Introduction

2- Literature review

3- SCNR framework

4- Network resilience analysis

5- Implications

6- Conclusion

References

Abstract

This study aims to investigate overall supply chain network resilience (SCNR) in the presence of ripple effect, or risk propagation, i.e. the phenomenon that disruptions at a few firms in a supply chain network (SCN) can spread to their neighboring firms, then eventually spread to other firms in the SCN. We begin by developing a multi-dimensional quantitative framework to measure SCNR, which includes three resilience dimensions based on three different network performance indicators. Given this framework, we then systematically explore the determining factors of SCNR and present a comprehensive analysis of how network structure and node risk capacity influence different aspects of SCNR. Our results clearly indicate the following important implications for managers. First, the influence of network type on SCNR tends to be more significant in the short-term than it is in the longer-term, given the ripple effect. Second, SCNR can be improved more effectively by enhancing node risk capacity than by adjusting network structure. Third, tradeoffs exist between the robustness of the network against a disruption and its ability to recover from that disruption. Fourth, different network performance indicators can provide different perspectives on SCNR. Together these help show that the multi-dimensional framework enables a better characterization of the complexity of SCNR, and thus that it provides support for more informed managerial decision-making about investing in improving resilience. The paper concludes the discussion by addressing opportunities for further extending the research effort.

Introduction

Modern supply chains are complex networks that are exposed to supply chain disruptions, which are operational shutdowns directly or indirectly caused by various risks such as natural disasters, political and economic factors, labor strikes, and material shortages (Bode and Wagner 2015; Craighead et al. 2007; Scheibe and Blackhurst 2017). A supply chain network (SCN) is vulnerable to disruptions not only because of the direct impacts of those disruptions, but also because of the ripple effect (also known as risk propagation) – the phenomenon that a sudden disruption at a few nodes in a SCN can spread to neighboring nodes, and eventually adversely impact other firms (Dolgui, Ivanov, and Sokolov 2018; 2 Scheibe and Blackhurst 2017; Li et al. 2019). The consequence of what is initially a local disruption can thus be substantial and long-lasting. As an example of this behavior, the hard disk drive (HDD) manufacturer Western Digital, which has a number of local factories in Thailand, suffered a 50% slump in HDD sales in the last quarter of 2011 because of major flooding in that country. These losses then affected a number of other firms within its extended supply chain. One of these firms was Hewlett Packard, a customer of Western Digital, which subsequently reported a 7% drop in revenues and blamed the HDD shortage for more than half of this decline (HP 2011). Intel, a supplier for HP, also posted a decrease in 4th Quarter revenues of $346 million as a result of lower demand following the flood (Intel 2011). Such results are common, as indicated by a recent study showing that 42% of supply chain disruptions originate below the tier one suppliers (Business Continuity Institute 2013).

1- Introduction

2- WL/SL properties

3- Background: CDFs for link failure time, link property value at link failure, and time at which LOAS occurs

4- CDFs for failure time margins

5- CDFs for system property values at LOAS

6- CDFs for margins based on WL and SL property values

7- CDFs for margins involving only SL property values

8- Summary discussion

References

Abstract

Representations for margins associated with loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems involving multiple time-dependent failure modes are developed. The following topics are described: (i) defining properties for WLs and SLs, (ii) background on cumulative distribution functions (CDFs) for link failure time, link property value at link failure, and time at which LOAS occurs, (iii) CDFs for failure time margins defined by (time at which SL system fails) − (time at which WL system fails), (iv) CDFs for SL system property values at LOAS, (v) CDFs for WL/SL property value margins defined by (property value at which SL system fails) − (property value at which WL system fails), and (vi) CDFs for SL property value margins defined by (property value of failing SL at time of SL system failure) − (property value of this SL at time of WL system failure). Included in this presentation is a demonstration of a verification strategy based on defining and approximating the indicated margin results with (i) procedures based on formal integral representations and associated quadrature approximations and (ii) procedures based on algorithms for sampling-based approximations.

Introduction

Representations for margins associated with loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems [1-6] involving multiple time-dependent failure modes are developed. As described in Ref. [7], the descriptor “loss of assured safety” for the performance under accident conditions (e.g., a fire) of a high consequence system with multiple WLs and SLs is used for the situation in which (i) a necessary set of SL failures places the system in a potentially operational condition before (ii) an appropriate set of WL failures places the system in an inoperable state. In contrast, safety is “assured” if (i) an appropriate set of WL failures places the system in an inoperable state before (ii) a necessary set of SL failures places the system in a potentially operational condition. Consistent with the preceding description, LOAS does not mean that the accident will progress to operation of the high consequence system; rather, it only signifies that certain conditions exist that could contribute to this progression.

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.

1- Introduction

2- Literature review

3- Problem definition and the proposed model

4- Solution methodology

5- Experimental results

6- Managerial insight

7- Conclusion

References

Abstract

The blood supply of hospitals in disasters is a crucial issue in supply chain management. In this paper, a dynamic robust location–allocation model is presented for designing a blood supply chain network under facility disruption risks and uncertainty in a disaster situation. A scenario-based robust approach is adapted to the model to tackle the inherent uncertainty of the problem, such as a great deal of a periodic variation in demands and facilities disruptions. It is considered that the effect of disruption in facilities depends on the initial investment level for opening them, which are affected by the allocated budget. The usage of the model is implemented by a real-world case example that addresses the demand and disruption probability as uncertain parameters. For large-scale problems, two meta-heuristic algorithms, namely the self-adaptive imperialist competitive algorithm and invasive weed optimization, are presented to solve the model. Furthermore, several numerical examples of managerial insights are evaluated.

Introduction

Supply chain management (SCM) is often described as a procedure of planning, implementation, and control of supply chain operations based on efficient practices (Melo et al., 2009). The supply chain network design (SCND) has played a dominant role in the performance of the supply chain (SC). It copes with so many prospects of the SC such as information, location of facilities and allocation of material. The SCND is considered as a significant issue in strategic and operational decisions in the SCM scope (Devika et al., 2014; Amin et al., 2017; Fu and Fu, 2015). Blood supply management and its products are vital issues for humankind. Blood is not a regular commodity since its demand is relatively random, and efficient coordination between supply and demand has not been resolved in various researches yet (Beliën and Forcé, 2012). Human blood is a rare and vital source that is produced only by human beings, and since there is currently no other product that can produce blood and also its uncertainty supply and demand side, keeping an adequate supply level is very important to fulfill demands (Duan and Liao, 2014).

1- Introduction

2- Literature review

3-  Optimization model

4- Sample average approximation method

5- Computational results

6- Managerial implications

7- Conclusion

References

Abstract

The supply chain (SC) ability to return quickly and effectively to its initial condition or even a more desirable state after a disruption is critically important, and is defined as SC resilience. Nevertheless, it has not been sufficiently quantified in the related literature. This study provides a new metric to quantify the SC resilience by using the stochastic programming. Our metric measures the expected value of the SC’s cost increase due to a possible disruption event during its recovery period. Based on this measure, we propose a two-stage stochastic program for the supply chain network design under disruption events that optimizes location, allocation, inventory and order-size decisions. The stochastic program is formulated using quadratic conic optimization, and the sample average approximation (SAA) method is employed to handle the large number of disruption scenarios. A comprehensive computational study is carried out to highlight the applicability of the presented metric, the computational tractability of the stochastic program, and the performance of the SAA. Several key managerial and practical insights are gained based on the computational results. This new metric captures the time and cost of the SC’s recovery after disruption events contrary to most of previous studies and main impacts of these two aspects on design decisions are highlighted. Further, it is shown computationally that the increase of SC’s capacity is not a suitable strategy for designing resilient SCs in some business environments.

Introduction

Resilience is the ability of a system or firm to recover after a disruption event effectively and quickly, and in supply chain management, this ability is affected by SC’s design decisions and resources. As emphasized by Chopra & Sodhi (2014), Simchi-Levi et al. (2014), and, Ivanov et al. (2016) from 2000 to 2015, disruption events such as economic crises, earthquakes, terrorist attacks, and strikes occurred in SCs in more frequency and severity, and hence such an ability is crucial for SCs. The Business Continuity Institute reports that one-third of 408 surveyed companies experienced at least one SC disruption in 2017 and one-fifth of the corresponding disrupted companies stated cumulative losses of at least one million euros because of disruption events (Alcantara et al., 2017). Recently, quantitative models have received significant attention to optimize design and planning decisions in SCs with consideration of disruption events, and many companies such as IBM and Ford Motor used these methods (Simchi-Levi et al., 2014; Lu et al., 2015; Hosseini et al., 2019).

Sustainable Enterprise Resource Planning is an enterprise system that incorporates essential entities of the corporate sustainable value chain into a centralised system. It is a massive information system, and its deployment requires a considerable investment of money and time. Unwell plan, limited resources, and weak commitment can lead to the failure of system implementation. Few studies have discussed the concept of Sustainable Enterprise Resource Planning systems. However, little research has focused on the implementation aspect of the system, especially on proposing a general plan that can guide practitioners to implement the Sustainable Enterprise Resource Planning systems. To address this gap, this study aimed to develop comprehensive guidelines that provide steps and activities for implementing the system. The guidelines are developed using a conceptual research method that relies on the examination of literature to find and integrate various concepts, including sustainability aspects, project management, organisational decision levels, and stratc management. The method envelopes three phases: (i) Collection of steps and activities from the existing sustainability and ERP guidelines, (ii) Classification and evaluation of the identified steps and activities, and (iii) Identification of steps and activities required for the guidelines. The proposed guidelines entail three main modules comprising implementation steps, levels, and activities. The results would be useful for practitioners in giving a formal direction to efficiently implement Sustainable Enterprise Resource Planning systems in their corporate value chains.

Introduction

Sustainability is a concept and process of enforcing environmental, economic, and social transformation towards a better quality of life (Scheidel et al., 2018). The philosophical reason for the emergence of this concept is the occurrence of environmental and social problems in society. This notion directs people to think of renewable resources, protection of the natural environment, human development, and other related aspects, and act for present and future generations (Sauvé et al., 2016). The importance of sustainability has been firstly acknowledged in the Brundtland Commission, the Rio Earth Summit, and other international conferences. International and regional governments impose industries to change their corporate’s vision, mission, goals, and strategies in the direction of sustainability. Organisations now prioritise sustainability in organisational and social policies and regulations (van Zanten et al., 2018). Embracing sustainable business practices in an organisation’s value chain can have a number of significant benefits such as increasing productivity and creativity (Bryson, 2017), evading fraud and mismanagement (Moosa and Ramiah, 2018), improving employees’ loyalty (Law et al., 2017), and more reliable products (Küçüksayraç, 2015). Sustainability should be intertwined as an essential component of the corporate strategy, culture, and business processes.