Upply Chain Analytics An Integrative View Of Enablers And Impact On Performance

Abstract

A huge and enormous bundle of both internal and external data is being generated regularly newlinethroughout various processes across different levels (upstream to downstream) of the supply newlinechain. A lot of data is created and is available to firms in this era of the internet of things and newlinecloud computing. If this data is unused, it will lead to opportunity loss for the firms in many newlineways. The humungous data generated during several processes in the supply chain can be newlineutilised to create information using analytics. newlineAnalytics aids in refining and increasing the vigour of the prediction tools for market trends. newlineAnalytics in the supply chain is germane in the planning, managing procurements, newlinemanufacturing, delivery and return of the products for the firm. The skilful manoeuvre of these newlineactivities helps in the improvement of the supply chain performance. Analytics can stimulate newlinesupply chain capabilities that come from formulating implementable and cutting-edge supply newlinechain strategies. It is assumed that the firms in the supply chain using analytics will be able to newlineimprovise their operational, strategic, business and financial performance. newlineAnalytics adds value by improvising the performance of the supply chain. Although the use of newlineanalytics has started gaining the attention of many academicians and decision-makers, newlinehowever, there is a lack of its acceptance and inclusion, especially in emerging economies. newlineThis research piece with triple objectives is a novel attempt in this regard. Firstly, this work newlineprimarily focuses on determining the circumstances, which enable the adoption of analytics in newlinethe supply chain. Identification of enablers for analytics generally and supply chain analytics newlinespecifically is an understudied area. Only a few frameworks have been proposed that lack newlineempirical validation (Wamba et al., 2018). Specifically, there is a need for more studies newlineidentifying and diagnosing the challenges in data analytics