Department of Decision Sciences

Permanent link for this collection

https://hdl.handle.net/20.500.14078/22

Browse

Now showing 1 - 20 of 85

Bridging the gap: a systematic analysis of circular economy, supply chain management, and digitization for sustainability and resilience
(2024) Kang, Parminder Singh; Bhawna; Sharma, Sanjeev Kumar
The primary objective of this research paper is to conduct a comprehensive and systematic literature review (SLR) focusing on Sustainable Supply Chain Management (SSCM) practices that promote Circular Economy (CE), sustainability, and resilience through adopting emerging digital technologies. A SLR of 130 research articles published between 1991 and 2023 was used to analyze emerging trends in CE, supply chain management (SCM), and digitalization. This study meticulously examined research publication patterns, the intricate themes explored, influential scholars, leading countries, and substantial scientific contributions that have shaped this multifaceted domain. This paper contributed to the collective understanding of how SSCM practices, driven by the principles of CE and empowered by the adoption of digital technologies, foster sustainability, resilience, and innovation within contemporary SCs. The research findings presented herein are primarily based on an analysis of the current literature from only Scopus and Web of Science (WoS) databases, which may restrict the generalizability of implementing these results. Based on this study, organizations and practitioners can assess the maturity of their SCM practices, gauge the resilience and digitalization levels of their SCs, and align them with academic literature trends. This enables practitioners to bridge the gap between scholarly advancements and real-world SCM implementation. Through its systematic review, the study provides a structured literature review that offers a collective understanding of SSCM practices driven by CE principles and empowered by digital technologies. This understanding enables sustainability, resilience, and innovation within contemporary SCs, benefiting academicians and practitioners.
Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem
(2025) Kang, Parminder Singh; Bhawna, Bhawna
Purpose This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature. Design/methodology/approach This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations. Findings The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service. Research limitations/implications The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties. Practical implications The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly. Social implications The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains. Originality/value Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.
On the effectiveness of option contracts under supply disruption
(2023) Son, Joong Y.
This paper studies the effectiveness of implementing option contracts for the procurement of seasonal products subject to short selling season, demand uncertainty, and supply-side disruption. The research intends to show how profitability and product availability can be enhanced both locally and globally by combining the long-term wholesale price contract and option contracts. Using the newsvendor model, the paper aims to identify business settings with respect to disruption parameters, demand uncertainty, and the option pricing under which the use of option contracts could improve supply chain performance. The main contribution of this research is that the effectiveness of option contracts is investigated under the impact of the supply-side disruption in addition to the demand uncertainty. The option contract-based portfolio procurement displays significant performance enhancement in terms of both the retailer profitability and the reduction in the lost sales quantity when supply-side disruptions prevail. The study of the procurement management subject to seasonal disruption can be readily applied to numerous business situations where the disruption can lead to devastating impacts such as the insufficient or untimely supply of COVID-19 vaccines with limited shelf life.
Service 4.0: Technology-enabled customer-centric supply chains
(2024) Kang, Parminder Singh; Wang, Xiaojia; Son, Joong Y.; Jat, Mohsin
This book presents a systematic framework for Service 4.0, including service digitization, digitalization, and digital transformation, which is an integral part of Supply Chain 4.0 in coping with complex, dynamic, and interdependent systems. It provides a comprehensive state-of-the-art review of digital technologies to support Service 4.0 and Supply Chain 4.0, and discusses important pillars of customer-centric supply chain models. It then explains the role of big data in customer-centric service-based supply chains and links the different types of data needed to promote end-to-end transparency and value co-creation activities to promote these key pillars. Moreover, the book introduces practical models to support analytics for customer-centric supply chains and sheds light on how the industry practically uses existing models to promote service co-creation. A chapter of a case study on women's clothing e-commerce reviews and demonstrates how various data visualization and text mining methods can be used to uncover meaningful insights within the review data. The book is intended to help students and researchers quickly navigate through various technologies and future research directions in the areas of Service 4.0 and Supply Chain 4.0. It is also a valuable read for practitioners in this field.
Are performance explanations credible or strategic? Evidence from a large sample of MD&As1
(2024) Gong, Sabrina; Hao, Yamin; Wang, Xiaojia
This paper examines managers’ explanations of firm performance (i.e., management attributions) in a large sample of the Management's Discussion and Analysis (MD&A) section of annual reports. We find that managers of poorly performing firms tend to attribute firm performance to external factors. We further propose a prediction model to decompose management external attributions into a credible part and a strategic part and find that both components are negatively related to firm performance. This evidence suggests that management external attributions partially reflect the actual impact of external conditions on firm performance and are not entirely subject to managerial opportunism. Additionally, we find that investors react more strongly to firm performance when managers provide credible external attributions, especially for firms without a bad reputation for strategic external attributions. We also show that executive compensation is less sensitive to firm performance when managers make more strategic external attributions.
Externalities of sales information along the supply chain
(2024) Hao, Yamin; Wang, Xiaojia
Prior studies have shown that earnings information of customer firms is value relevant to the investors of their suppliers, but it remains unclear whether sales information has similar effects. In this study, we investigate the value relevance of customer firms’ sales information to suppliers’ investors using a large sample of monthly sales disclosures from U.S. retailers. We find that within the narrow window of retailers’ monthly sales disclosures, suppliers’ abnormal stock returns are positively related to retailers’ sales growth in both same-store sales and store numbers. This finding suggests that sales information has spillover effects, or externalities, along the supply chain. We further conduct cross-sectional tests and find that the externalities of sales information vary with a supplier’s dependence on the retailer. We also develop a prediction model to separate the expected and unexpected components of retailers’ sales information and find that the unexpected component of sales growth is the primary source of externalities. Overall, this study provides new insights into the value relevance of sales disclosures.
Greenwashing risks in environmental quality competition: detection and deterrence
(2025) Mukherjee, Arka; Ghosh, Subhadip
The rising prevalence of greenwashing by firms has emerged as a major concern for regulatory authorities over the past decade. This paper examines the impact of regulation on firms’ strategic decisions regarding greenwashing and environmental quality in an oligopolistic market. We model two firms that compete on environmental quality and greenwashing levels, operating under the oversight of a regulatory authority. The authority’s policy instruments include a detection mechanism and fines imposed on firms engaging in greenwashing. Using a differential game-theoretical framework, we examine the effectiveness of regulatory interventions like detection and penalties in reducing greenwashing behavior and enhancing environmental quality. Additionally, we discuss the post-detection trajectories of both firms, providing insights into the effects on consumer perceptions and market competition. We find that while regulation can reduce greenwashing as expected, it may also reduce firms’ environmental quality efforts. Indeed, when penalties are sufficiently high, the marginal returns on investment in greenwashing exceed those from actual green quality improvements.
Agriculture-based offsets for voluntary carbon markets: review of current state, extent of markets, smallholder and gender concerns, and addressing research gaps
(2023) Jindal, Rohit; Vardhan, Mamta
Agriculture and forestry are responsible for 22% of the global greenhouse gas emissions, which makes them crucial for meeting the ambitious carbon reduction targets under the Paris Climate Agreement. While there exist several papers on forestry-based emissions reduction projects, relatively little is known about similar projects in the agricultural sector. Indeed, the last major report on agriculture-based carbon offset projects was published in 2011. We bridge this gap in current knowledge by exploring carbon mitigation efforts in agriculture, especially the growth in the Voluntary Carbon Markets. Our review is based on a careful selection of peer-reviewed literature, international databases, and websites of carbon registries. Voluntary carbon markets have grown rapidly, transacting 493.1 million tCO2-eq in 2021, valued at nearly $2 billion. Of these, agricultural offsets contributed about 1 million tCO2-eq at an average price of $8.81 per tCO2-eq. There are currently 720 agriculture projects that generated voluntary carbon offsets in the recent past or are still active. Of these, the main ones are methane reduction (331 projects with 16.8 million tCO2-eq emissions reductions), followed by 277 projects on rice cultivation (4 million tCO2-eq). Methane reduction projects have the highest average size of 50,625 tCO2-eq per project, followed by improved irrigation management (28,322 tCO2-eq), solid waste separation (20,322 tCO2-eq), and rice cultivation (14,298 tCO2-eq). Over 90% of projects (648) are 'reduction' projects, while less than 10% (71) combine carbon removal with carbon reduction. China leads with 333 projects, followed by the US (207) and India (59). North America leads in emission reductions (11.1 million tCO2-eq), followed by Asia. Africa has 345,825 tCO2-eq reductions from one project - the Kenya Agricultural Carbon Project. Among carbon registries, 65% of all agricultural offset projects are registered through Verified Carbon Standard, followed by Climate Action Reserve (22%), the Gold Standard (9%), and the American Carbon Registry (4%). Smallholder farms contribute nearly 32% of agricultural greenhouse gas emissions and are highly susceptible to climate change risks. Carbon offset projects in agriculture have varying local impacts, including positive and negative outcomes. Gender equality is often overlooked in these projects, even though most stakeholders acknowledge its importance. Despite their impressive growth, agricultural carbon offsets represent a small fraction of the overall carbon market, with only 1% of the voluntary and 2.3% of the compliance markets. This is due to the perceived high risks, including concerns about additionality, leakage, permanence, monitoring, and transaction costs. To address these issues, we recommend that projects follow standardized methodologies, collaborate with research institutions, and adopt monitoring innovations. In conclusion, despite its small size, the voluntary carbon market in agricultural offsets plays a vital role by allowing experimentation, enabling participation in jurisdictions without climate regulations, and encouraging smallholders to engage in mitigation efforts.
Natural gas Matters: LNG and India’s quest for clean energy
(2024) Ghosh, Subhadip; Majumder, Rajarshi; Chatterjee, Bidisha
India, the world’s most populous country, is the world’s third-largest emitter of greenhouse gases (GHGs). Despite employing several energy sources, it still relies heavily on coal, its primary energy source. Given India’s swiftly rising energy demand, this challenges meeting emission reduction targets. In recent years, India has significantly increased investments in renewables like solar and hydrogen. While commendable, these initiatives alone cannot meet the country’s expanding energy demands. In the short term, India must rely on both domestic and imported fossil fuels, with natural gas being the most environmentally friendly option. In this context, this paper attempts to forecast energy consumption, natural gas production, and consumption in India until 2050, using both univariate and multivariate forecasting methods. For multivariate forecasting, we have assumed two alternative possibilities for GDP growth: the business-as-usual and the high-growth scenarios. Each of our forecasts indicates a notable shortfall in the projected production of natural gas compared to the expected demand, implying our results are robust. Our model predicts that nearly 30–50 percent of India’s natural gas consumption will be met by imports, mainly in the form of LNG. Based on these findings, this paper recommends that Indian government policies emphasize increasing domestic natural gas production, importing LNG, and expanding renewable energy resources.
Effect of detection by regulators on greenwashing: a differential game theoretic analysis
(2024) Mukherjee, Arka; Ghosh, Subhadip
This paper investigates how environmental quality competition between a greenwashing firm and a genuinely eco-conscious one affects the two firms' quality choices and consumer perceptions of environmental quality. Using a differential game theoretical framework, it highlights the significance of early detection mechanisms and regulatory interventions in mitigating the adverse effects of greenwashing, thereby fostering a more conducive environment for genuine environmental stewardship. Furthermore, this study explores the potential trajectories for both firms’ post-exposure, addressing the implications for consumer perceptions, market dynamics, and the overall corporate environmental responsibility landscape.
Constraints to adopting soil fertility management practices in Malawi: a choice experiment approach
(2019) Krah, Kwabena; Michelson, Hope; Perge, Emilie; Jindal, Rohit
Though problems related to low and declining soil fertility continue to impede agricultural production and food security in Sub-Saharan Africa, smallholder farmers in this region – those cultivating two hectares or less – have shown reluctance to adopt practices at scale that help conserve or enhance soil quality. Employing a discrete choice-based experiment, we find evidence that farmers’ propensity to adopt soil fertility management (SFM) practices increases with improved access to mineral fertilizers, and when farmers receive relevant technical training on soil fertility improving technologies. A unique aspect of our study is our focus on understanding how smallholders’ stated SFM preferences relate to their perceptions of recent local climatic variation. We find that farmers who perceive that rainfall amounts are decreasing are less willing to adopt crop rotations to improve soils. Our findings suggest that policies designed to increase adoption of SFM practices are more likely to succeed when they provide farmers with inputs that farmers perceive as complementary to SFM, including mineral fertilizer, and when they are built around an understanding of farmers’ perceptions of climatic variability.
The harmonized information-technology and organizational performance model (HI-TOP)
(2024) Enstroem, Rickard; Kang, Parminder Singh ; Bhawna, Bhawna
This study introduces the Harmonized Information-Technology and Organizational Performance Model (HI-TOP), which addresses the need for a holistic framework that integrates technology and human dynamics within organizational settings. This approach aims to enhance organizational productivity and employee well-being by aligning technological advancements with human factors in the context of digital transformation. Employing a two-phased methodology, the HI-TOP model is developed through a literature review and text mining of industry reports. This approach identifies and integrates critical themes related to ICT integration challenges and opportunities within organizations.
Research recast(ed): S3E13 - Innovation in supply chain risk analysis and machine learning
(2024) Leschyshyn, Brooklyn; Smadis, Natalie; Kang, Parminder Singh
On today’s episode we talk with Dr. Parminder Singh Kang about Alberta and innovations knowledge mobilization workshop and strategic network development grant, as well as his work with his MITACS grants. We discuss how Dr. Parminder Singh Kang’s research is promoting student learning.
Teaching information flow in supply chains: a role-playing game using TagScan
(2024) Wang, Xiaojia; Enstroem, Rickard
Information flow is one of the three main flows of supply chains. It is an abstract concept that can be challenging for students to grasp in its entirety. This article describes a role-playing game for teaching the topic of information flow in an undergraduate supply chain management course. The game allows students to simulate receiving and fulfilling customer orders by playing five roles within a manufacturing company. Students use TagScan, an augmented reality barcoding and logistics system launched by a technology company in western Canada, to track information throughout the game. Pre- and postsurvey results demonstrate the effectiveness of the proposed game in helping students visualize abstract course concepts and understand the types of information being tracked, the available information transmission technology, and the dynamics of information flow in a supply chain. Students were actively engaged in this in-class activity and responded positively to the learning-by-gaming experience.
Enterprise education in undergraduate business programmes advances students' negotiating competence and self-confidence
(2024) Enstroem, Rickard; Benson, Lyle
Purpose Business graduates’ enterprising capability augments their work readiness, transforming them into professionals capable of driving successful outcomes. At the core lie self-confidence and negotiating competence. However, embedding enterprise education and developing assessments to evidence learning is challenging. This study aims to offer a blueprint for establishing enterprise learning in the classroom and investigating the effectiveness of cultivating negotiating competence and self-confidence. Design/methodology/approach Modelled on Kolb’s experiential learning cycle, students engage in in-class and real-life negotiations, assessing self-confidence using a scale founded in Bandura’s self-efficacy theory. Open-ended reflections are also submitted. Quantitative data is analysed through multiple linear regression, while quantitative and qualitative data triangulation substantiates enterprise learning in negotiating competence and self-confidence. Findings Students’ reflections show that low self-confidence poses an initial barrier in negotiations, overcome with successive engagements. Quantitative analysis uncovers response-shift biases, with female and male students overestimating initial self-confidence levels. The gender and difference score type interaction reveals a more pronounced bias among female students starting from a lower baseline than male students, implying a more substantial self-confidence improvement for female students. These findings challenge traditional assumptions about gender differences in negotiations and emphasize the need for nuanced perspectives. Originality/value Enterprising capability is pivotal for business professionals. This study highlights the advancement of negotiating competence and self-confidence. It contributes uniquely to the development of enterprise education pedagogy. Focusing on nuanced gender differences challenges prevailing assumptions, providing a perspective to the discourse on negotiating competence and self-confidence in management training.
Striking gold: navigating the education massification maze for work readiness
(2024) Enstroem, Rickard; Schmaltz, Rodney
Purpose This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations. Design/methodology/approach The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts. Findings This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized. Originality/value This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.
Canadian agriculture technology adoption
(2024) Easher, Tahmid Huq; Enstroem, Rickard; Griffin, Terry; Nilsson, Tomas
Objectives Statistics Canada administers the Agricultural Census every 5 years, and this paper presents unsuppressed data from the 2016 and 2021 Census. The data set encompasses detailed information on farm types, sizes, technology choices, and a demographic profile of farm operators from the 2021 Census. Data on farm characteristics and operator demographics is crucial for understanding innovation in agriculture and formulating evidence-based policies. Data description The data sets cover the two most recent agriculture censuses of 2016 and 2021, presenting data on the number of farmers by region, farm type, size, and the adoption of technologies. Additionally, a third data set lists the number of farm operators by age and sex. The census questionnaire inquires about using different technologies, varying the types across the two census periods. Notably, there is no data suppression in these data sets, and they cover all 10 provinces in Canada, excluding the three territories. Farm types are categorized based on the North American Industry Classification System (NAICS), and farm size is measured in acres.
Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines
(2017) Aboutaleb, Adam; Kang, Parminder Singh; Hamzaoui, Raouf; Duffy, Alistair
Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets.
Information technology investment and working capital management efficiency: evidence from India survey data
(2022) Gill, Amarjit; Kang, Parminder Singh; Amiraslany, Afshin
Purpose This study aims to test the relationship between information technology investment (IT_INVEST) and working capital management (WCM) efficiency. Design/methodology/approach This study utilized a survey research design to collect data from micro, small and medium enterprises (MSMEs) owners in India. Findings Empirical results show that perceived IT_INVEST plays a role in improving WCM efficiency by decreasing the inventory holding period and reducing the cash conversion cycle (CCC) in India. A three-stage least square model (3SLS) shows that IT_INVEST decreases CCC directly and indirectly through the inventory holding period, accounts receivable period and accounts payable period. The empirical analysis also shows that IT_INVEST decreases the inventory holding period and CCC by 16.80% and 26.40%, respectively, for the examined firms. Research limitations/implications If MSMEs' owners perceive a higher level of IT_INVEST, then the owners perceive a higher WCM efficiency and vice versa. Originality/value This study contributes to the literature on the relationship between IT_INVEST and WCM efficiency. This study may encourage further studies of IT investment and WCM efficiency using data from other industries and countries. MSME owners may find empirical results beneficial to improve WCM efficiency. Moreover, financial management consultants may find results helpful to provide consulting services.
An option-based capacity control mechanism for code-sharing alliances
(2023) Wang, Xiaojia; Fung, Richard Y. K.
This study addresses capacity control problems in code-sharing alliances, which deal with the determination of member airlines' booking limits. We propose an innovative option-based capacity control mechanism to overcome the drawback of inflexibility in blocked seat allotment for a two-airline code-sharing alliance. The mechanism incorporates the concept of a straddle, an advanced option strategy in finance, to allow member airlines the flexibility to tackle not only downward but also upward demand variations during the booking process. We design simulation experiments and use a case illustration to show scenarios when the code-sharing alliance can benefit from the proposed mechanism.

Browse

Recent Submissions