International Journal of Supply Chain and Logistics

Does Packaging in Green Pay off? Role of Green Packaging on Firm Performance: Evidence from a Developing Economy

2024-02-11T00:03:11+03:00

Purpose: Greening supply chains has become important more than ever among business firms carving out competitive niches. With the ever increasing complexity in business operations characterised by demand for higher value, supply chain firms are increasingly turning to green practices to not only achieve better results but also be socially responsible. While this is so, Euro monitor International report, 2020 discloses that globally, the manufacturing sector experienced 4.6% drop in market share by 2021, partly orchestrated by environmental upheavals due to business practices. The study sought to examine whether adopting green packaging pays off by establishing effect of green packaging on firm performance in a developing economy, Kenya.

Methodology: The study achieved this through a mixed methods design on a population of 460 managers and 92 CEOs, interacted through a structured questionnaire and interview schedule. Standard linear regression in SPSS version 24 and thematic analysis were adopted. Results show that green packaging has significant positive effect on performance (R²=.394, β=.628, p<.05), practically denoting that unit increase of packaging in green results to 0.628 units improvement in performance.

Findings: The results provide sufficient evidence that indeed, packaging in green pays off. Further, the paper ground theoretical puts by Hart (1995) that green packaging is a pivotal resource.

Unique contributor to theory, policy and practice: We provide interesting conclusions that green packaging pays off, advising managers and industry stakeholders to adopt policies of leveraging it. A research agenda is offered to further scholarly subsequent discussions.

Effect of Geopolitical Situation on Potassium Iodide Sale

2024-01-01T02:43:35+03:00

This whitepaper explores the intricate relationship between geopolitical dynamics and the demand for potassium iodide (KI), a critical component of public health and emergency preparedness measures. KI serves as a safeguard against the adverse effects of radioactive iodine (RAI) exposure in the event of nuclear incidents. Geopolitical factors significantly influence the availability, accessibility, and trade regulations of KI, as demonstrated through case studies like the Russia-Ukraine conflict and historical nuclear accidents. International agreements, regulatory frameworks, and trade policies are key in shaping the KI market, with growing demand stemming from the nutraceutical and laboratory reagent sectors. While solid KI is the predominant market segment, there remains a challenge in the presence of iodine alternatives. Amidst the Russia-Ukraine war, KI demand has surged, depleting supplies and resulting in price gouging. Understanding these trends and the broader geopolitical impact on KI is essential to appreciate its critical role in safeguarding public health.

Unlocking Inventory Efficiency: Harnessing Machine Learning for Sales Surge Prediction

2024-05-03T06:14:12+03:00

Purpose: Sales forecasting plays a crucial role in inventory optimization for retail stores, especially during special events such as promotions, advertisements, holiday season, weather, social and economic situations etc. These events drive significant changes in customer buying patterns. Some of these events are captured in the current forecasting models as part of trend, seasonality, and cyclicality. But many times, unexpected local events such as extreme weather conditions, riots, and regional events such as marathons, concerts have a significant impact on sales surges which are usually not captured in the sales forecast. This leads to inventory being out of stock and store managers placing last-minute manual orders. By accurately predicting these sales pattern changes, retailers can make informed decisions, ensuring optimal inventory levels and maximizing profits.

Methodology: In this paper, a data-driven solution was discussed that leverages machine learning models to predict sales pattern changes and surges during local events in a specific geographical location. Web scraping can be used to gather data on local events from a range of online sources, including Google News, local news channels, and social media platforms. By extracting pertinent details about upcoming events, it is possible to compile a thorough database of local happenings.

Findings: Data Analysis of historical sales data mapped to its local events can provide insights on key department-categories where there is a surge in sales. Machine learning models can be used to analyze, experiment and train historical sales data in conjunction with the events data.

Unique contribution to theory, practice and policy: Machine learning models would be trained to capture the complex relationships between different local events and their impact on sales. Therefore the study recommends using the machine learning approaches specified to consider various factors, such as event type, location, duration, and historical sales patterns, our models can effectively predict sales fluctuations during specific events.

Future Trends and Innovations in School Bus Transportation

2024-02-08T03:07:15+03:00

Purpose: This research paper delves into the critical examination and potential transformation of the traditional school bus transportation industry through the lens of modern technological advancements. The purpose of this study is to explore how innovations in technology can revolutionize the traditional school bus transportation system, enhancing safety, operational efficiency, and environmental sustainability.

Methodology: The methodology encompasses an in-depth analysis of current challenges within the industry, followed by the exploration of technological solutions such as Internet of Things (IoT) devices, artificial intelligence (AI) for route optimization, smart communication platforms, and predictive analytics for managing driver callouts and optimizing operations and utilizing modern automated systems to get predictive and prescriptive insights that would innovate the school bus transportation system. The study incorporates methodology of getting to a baseline of how current school transportation needs are met using data and conventional methods. Analysis have been performed on industry leaders like – Durham, First Student and Zūm Services Inc. which are leading school transportation companies and how they run operations of routing, safety, customer satisfaction, ride tracking, fleet management, payroll, Dispatch operations and school operations in conjunction with various School Districts and Counties.

Findings: The findings reveal that integrating smart technologies can significantly empower stakeholders—students, parents, and school staff—by providing real-time data on bus locations, improving route efficiency, and ensuring the safety of students through advanced monitoring and emergency response systems. Innovations like automatic rerouting in emergencies, predictive maintenance, and enhanced security measures through biometric systems and emergency response integration show promising potential to address current limitations.

Unique contributor to theory, policy and practice: The unique contribution of this research to theory, practice, and policy lies in its comprehensive analysis of how technology can be leveraged to transform an outdated system into one that is efficient, safe, and sustainable. It offers a roadmap for integrating innovative technologies into school transportation, highlighting the significant impact on policy formulation, the practical application in school districts, and the theoretical implications for the future of educational transportation systems. This paper sets a foundation for future research on the longitudinal effects of these technologies and their socio-economic impacts, advocating for a policy and regulatory framework that supports the integration of these advancements into school transportation systems globally.