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Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.

The Resource Constrained Project Scheduling Problem (RCPSP) is a combinatorial optimization problem which is non-deterministic polynomial-time (NP)-hard in nature. Due to the diversified applications of RCPSPs, they have been commonly used as scheduling procedures in real-world problems. Since, in practice, project data are prone to changes or disruptions, this paper introduces a mathematical model for a reactive scheduling approach, called the Event Based Reactive Approach (EBRA). This proposed EBRA approach is employed to examine its recovery performance under both a single disruption and a series of independent resource disruptions. Several simulated disruption data are hypothesized to represent real-world disruption scenarios and, without loss of generality, the proposed reactive approach is proved to be efficient in reducing the number of variables and computational complexity and also to be resilient in realistic changes, such as duration inflation and dynamic resource usages. Along with employing an exact method by LINGO software, this paper also proposes an enhanced iterated greedy (EnIG) approach to meta-heuristically solve larger and computationally expensive benchmark instances taken from the Project Scheduling Library (PSPLIB).

Lean six sigma (LSS), a process improvement tool to achieve operational excellence in any industry, has become popular among practitioners over the last few decades. In this study, a framework for identifying barriers to LSS implementation in supply chains has been developed using the interpretive structural modeling (ISM) method. The ISM technique was used to identify the contextual relationships among the barriers. Barriers were classified based on their dependence power and driving power using MICMAC (Matriced Impacts Croises Multiplication Appliquee a un Classement). This framework will provide a comprehensive understanding of how the barriers of LSS affect each other. The proposed framework has been tested using data from a real-world apparel manufacturing company in Bangladesh. 10 barriers to LSS implementation were identified from a literature review and industrial managers’ feedback. This study is expected to guide practitioners in implementing LSS in supply chains by helping to focus their effort on removing the most important barriers.

A new real-time disruption recovery plan is developed.An imperfect production-inventory system is considered.A new mathematical and dynamic solution approach is developed for managing disruptions.We solved the problem for the both single and multiple disruptions on a real-time basis.We compared the results for a good number of randomly generated disruption test problems. In this paper, a disruption recovery model is developed for an imperfect single-stage production-inventory system. For it, the system may unexpectedly face either a single disruption or a mix of multiple dependent and/or independent disruptions. The system is usually run according to a user defined production-inventory policy. We have formulated a mathematical model for rescheduling the production plan, after the occurrence of a single disruption, which maximizes the total profit during the recovery time window. The model thereby generates a revised plan after the occurrence of the disruption. The mathematical model, developed for a single disruption, is solved by using both a pattern search and a genetic algorithm, and the results are compared using a good number of randomly generated disruption test problems. We also consider multiple disruptions, that occur one after another as a series, for which a new occurrence may or may not affect the revised plan of earlier occurrences. We have developed a new dynamic solution approach that is capable of dealing with multiple disruptions on a real-time basis. Some numerical examples and a set of sensitivity analysis are presented to explain the usefulness and benefits of the developed model. The proposed quantitative approach helps decision makers to make prompt and accurate decisions for managing disruption.