Sudipendra Nath Roy and Tuhin Sengupta
Operations Management
Abstract
Subject area
Operations Management
Study level/applicability
MBA/Post Graduate
Case overview
This case attempts to highlight a very common resource allocation dilemma in a real-life scenario. The majority of today’s problems are solved by the methodology of trial and error. This case shows how a generic trial-and-error solution, if buttressed by a proper quantitative methodology, can have substantial impact on the bottom-line of an organization. The case concentrates on three disparate focus areas in a didactic fashion, namely, the ability to retrieve raw data and convert it into a utilizable form if a quantitative method is to be applied; the ability to comprehend the resource constraints of a typical real-life situation; and the skill required to develop and solve an optimization problem in Excel Solver, a product which can easily be accessed by any practitioner.
Expected learning outcomes
Expected learning outcomes are as follows: students learn to formulate a Mixed-Integer programming model; to interpret optimal solutions and appreciate the application of “Optimization”; to recommend a resource allocation strategy; and to understand the importance of cost minimization in organizations.
Supplementary materials
Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.
Subject code
CSS: 9: Operations and Logistics
Details
Keywords
Sudipendra Nath Roy and Tuhin Sengupta
The purpose of this paper is to provide a systematic literature review and contributes to academic understanding and practitioner needs of third party logistics (3PL) which were…
Abstract
Purpose
The purpose of this paper is to provide a systematic literature review and contributes to academic understanding and practitioner needs of third party logistics (3PL) which were developed over the past decade.
Design/methodology/approach
Through a structured selection search of the entire English-language academic literature, after final refinement, the authors identified 95 relevant studies in this context. A succinct classification of literature has been performed to collate the entire literature encompassing viewpoints of both academicians and practitioners.
Findings
The key finding of this review suggests the opportunities exist in the understanding of the “optimization capabilities” of a 3PL provider. The authors found that when on the one side, constructs such as “supply chain vision”, “creativity”, “impact of geographical reach of services on the selection criteria” need more attention; on the other hand, operational dimensions of 3PL such as packaging ability, repair service, procedural compliance, conflict resolution, role of HR policies and mitigation of external risks provides a chance for future researchers to delve deeper into these domains. Furthermore, the authors also presented a comprehensive research gap framework highlighting potential research topics to be explored in near future.
Research limitations/implications
The paper captures peer-reviewed literature from the past decade and has been selected from the Web of Science database only.
Originality/value
The paper identifies different research gaps, namely, conceptual, contextual and methodological, to understand and develop opportunities for future research in the domain of 3PL. The paper makes a careful attempt by linking the synthesis of this literature review to previous literature reviews to establish the knowledge continuum of third party logistics.
Details
Keywords
Mona Bokharaei Nia, Mohammadali Afshar Kazemi, Changiz Valmohammadi and Ghanbar Abbaspour
The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right…
Abstract
Purpose
The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.
Design/methodology/approach
This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.
Findings
The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.
Research limitations/implications
The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.
Practical implications
The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.
Originality/value
In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.