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Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems.

This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources.

Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework.

Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.

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.

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.

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.

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.

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.

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.

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.

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.

This research indicates that successful ICT integration requires balancing technological advancements with human-centric considerations, including addressing technostress and promoting skills development. The HI-TOP model’s four components – Workforce Empowerment and Resource Strategy (WERS), Technology-Enhanced Information Architecture (TEIA), Organizational Information Processing Strategy (OIPS) and Knowledge Sharing Platform (KSP) – demonstrate operational and strategic synergy required to achieve enhanced organizational performance and adaptability.

The HI-TOP model contributes to the body of knowledge by providing a structured framework for understanding the interplay between technology and organizational dynamics, with an emphasis on employee well-being and overall organizational performance. Its originality lies in the integrative approach to model development, combining theory with empirical insights from industry data, thus offering actionable guidance for organizations navigating the complexities of digital transformation.

The aim of the paper is to shed light on the use of chitosans and chitooligosaccharides as biopreservatives in various foods animal. Foods of animal and aquatic origin (milk, meat, fish, eggs, sea foods, etc) become contaminated with a wide range of microorganisms (bacteria, molds and yeasts) during harvesting, transporting, processing, handling and storage operations. Due to the perishable nature of these foods, their preservation is of utmost importance. Though many synthetic chemicals are available, yet their use is quite restricted due to their hazardous effects on human health.

Within the domain of food industry, traditionally chitosan is used for biopreservation of foods, which is well known for its nutritional and medicinal properties in human nutrition. However, chitooligosaccharides also possess a number of nutraceutical and health promoting properties in addition to their preservative effect and shelf-life extension of foods. In this study, the comparative effects of both chitosan and chitooligosaccharides on preservation of foods of animal and aquatic origin have been summarized.

Though chitosan has been extensively studied in various foods, yet the use of chitooligosaccharides has been relatively less explored. Chitooligosaccharides are bioactive molecules generated from chitosan and have several advantages over the traditional use of chitosan both in food products and on human health. But unfortunately, little or no literature is available on the use of chitooligosaccharides for preservation of some of the foods of animal origin. Notable examples in this category include cheese, beef, pork, chicken, fish, sea foods, etc.

This paper focuses on the effects of chitosans and chitooligosaccharides on the processing and storage quality of foods of animal and aquatic origin, which offers a promising future for the development of functional foods.