BOHR International Journal of Operations Management Research and Practices

A study of vendor-managed inventory (VMI) and cloud-based inventory management tools for MSMEs in Boisar MIDC of Palghar District

2025-06-02T10:53:33+00:00

Micro, Small, and Medium Enterprises (MSMEs) are pivotal to India’s manufacturing growth, yet they struggle with efficient inventory management due to infrastructural bottlenecks, outdated practices, and limited digital integration. This study explores the adoption potential of two transformative approaches—Vendor-Managed Inventory (VMI) and Cloud-Based Inventory Management Tools—within the industrial cluster of Boisar MIDC, Palghar District. A survey of 380 MSMEs reveals that while awareness of cloud-based systems is growing, actual implementation remains limited. VMI adoption is even lower, hindered by trust gaps and integration challenges. However, early adopters report tangible benefits, including reduced lead times, improved collaboration, and operational cost savings. Using path modeling, the study confirms significant positive effects of technology adoption on manufacturing efficiency (β = 0.49) and strategic planning. These findings underscore the need for policy-backed digital training, phased technology rollouts, and stronger vendor partnerships. The study offers a scalable framework for enhancing inventory efficiency across similar industrial clusters in India.

Operational excellence through passive RFID: Rapid deployment solutions for indoor localization

2024-09-25T21:40:07+00:00

The demand for indoor positioning has grown significantly due to applications that rely on location awareness in
spaces such as medical facilities, warehouses, and smart buildings. Unlike outdoor environments, where global
positioning system operates effectively, indoor positioning faces the challenges of signal blocking and multipath
effects common in enclosed areas. Current solutions such as Wi-Fi, Bluetooth, and Ultra-Wideband (UWB) provide
effective results, but they often involve high costs and complex install processes, especially in big settings. Given
these challenges, passive radio frequency identification (RFID) has become a viable choice for indoor positioning.
Not only the technology is cost-effective, but also eliminates the need of active power supply to its tags, making
efficiency increase. This paper is proposing a passive RFID-based positioning system which designed for rapid
deployment to solve signal interference, a common problem in the area with neighboring small spaces. We explore
the limitations of the current RFID system and propose simple strategies to ensure location accuracy with minimal
installation needs.

Application of TSGA, a hybrid meta-heuristic model, to solve a real size problem of flow shop scheduling with changeover times in operations

2025-10-11T11:31:27+00:00

Flow shop scheduling (FSS) problems are NP-hard combinatorial optimization problems. It’s quite difficult to achieve an optimal solution for real size problems with mathematical modeling approach because of its NP-hard structure. Meta-heuristic algorithms, like Tabu Search (TS) and genetic algorithm (GA), play a major role in searching for near-optimal solutions for NP-hard optimization problems. In the case study, the current scheduling method is ineffective, and the total tardiness time of orders is still quite high. This paper develops a Tabu Search and Genetic Algorithm (TSGA) model for solving the real FSS problem, with the objective of dispatching orders more effectively than the current dispatching method. The TSGA model is a hybrid meta-heuristic model, combining TS and GA. In the model, TS is used as the platform for local search, and GA is used to support TS in global search. The performance of the model is compared with the traditional heuristic being used. The result indicates that the model is a good approach for FSS problems. However, the factors of the model are only selected empirically; therefore, the results are not particularly satisfactory. The future research is to use experimental design (DOE) to determine the model parameters to get better suboptimal results.

Applying value stream management to improve packaging processes in cosmetics industry – a case study

2025-11-25T05:30:26+00:00

The company under study is a company specializing in producing cosmetics. Sales of the company have declined sharply in recent years due to failure to deliver on time. After analyzing, the root causes of the problem of low on-time delivery rate are high production lead time and high cycle time in the packaging processes. The study applied value stream management, VSM, to improve packaging production process of the company. The primary objectives were to reduce the production lead time and reduce production cycle time to meet the takt time, thereby improving the on-time delivery rate and the company’s sales. The results had shown that the lead time had been reduced by 46.23% from 42.4 to 22.8 h, the cycle time had been reduced 31.25% from 32 to 22 s, meeting the takt time.

AI-driven innovations in IT and logistics outsourcing: enhancing LSP performance in the automotive sector

2025-11-05T09:21:19+00:00

The business landscape of today has been revolutionized, if not completely, by the advent of artificial intelligence (AI) in almost all the sectors concerning logistics and information technology (IT) of the automobile industry in particular, which are so vital. The combination of logistics outsourcing and IT with AI has enabled automobile manufacturers to enhance their operations optimally by reducing the costs and increasing the agility of the supply chain. The core functions like inventory planning, transportation management, warehousing planning, and supplier collaboration have led to a pathway in taking up decisions regarding build-to-order or purchase with Logistics Service Providers (LSPs) that has been rudimental. The study aims to investigate the impact of AIdriven innovations in IT and logistics outsourcing on LSP performance within the automotive sector. Specifically, it examines how AI adoption level, IT integration, supply chain risk mitigation, sustainability practices, and AIenabled collaboration influence LSP performance across five strategic levels: execution of essential operations, value-added services, inventory planning, distribution network design, and relationship management through the logistics outsourcing and IT, driven by AI impacting its LSP’s performance and transformative using survey data from 584 respondents. ANOVA test and other statistical test results indicate that LSPs with higher levels of AI and IT adoption are more likely to implement advanced logistics solutions and enhance the LSP’s performance.