Business Analytics Assessment

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Added on 2020-10-23

Business Analytics Assessment

Added on 2020-10-23

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BUSINESS ANALYTICSASSESSMENT TASK 1: DATA ANALYTICS

Applications of Data Analytics in Supply Chain and Logistics ManagementThe application of analytics in the Supply chain and logistics industry allows theidentification of trends and patterns from synthesizing past data, to predict the future, furtherto better manage risks and anticipation (Alicke et al. 2016). The complexity of supply chainprocess increases as activities within are often globalized, especially by multinationalenterprises. On the other hand, the high demand in such a rapidly changing environmentmakes it a challenge to provide the best quality of goods and services, with minimum cost,and the optimum time period; the objective is to achieve lean supply chain management. Incontext of achieving optimization in the process, this is where analytics are advantageousmore than ever.Analytical tool that are proven to provide big impact on supply chain industry are Big dataand Artificial Intelligence (AI). It generates extensive statistics visualization on past andcurrent trends to project future (IBM,2020). Therefore, more accurate insights to improvedecision making process. In this case, the improvement can happen in not limited to one partof the supply chain channel, but can improve decisions throughout all supply chain channel;sourcing, production, warehousing, transport, POS and end consumer (Alicke et al. 2016). Taking the pandemic situation as an Instance, the demand for efficient supply chainperformance significantly increases. As coronavirus spread through humans, contactlessoperation and social distancing are persuaded as preventive method. In relation to analytics,big data allows the drone delivery to be used as part of AI approach, to provide contactlessdelivery service to your door (Cozzens, 2020). However, although there are numerous promising opportunities in data-driven approaches forsupply chain and logistics management in the market, only few that has actually maximizedanalytics application in the process (Alicke et al. 2016); for instance, acquiring big data. Costand Capabilities presence in supply chain workers are what threatens the missed opportunitiesof big data implementation.

In the upcoming years, the development of analytics can also digitalize the supply chainprocess, replicating warehouse managers with machinery performing their tasks. It allows a24/7 operation, systematic mass production, increase production rate; thus, efficiency (IBM,2020). Additionally, by the high demand in social media use, AI can map customerpreference through their interactions and behavior without any direct consumer contactneeded. Productivity will definitely improve. Overall, the objective of supply chain is to achieve maximum efficiency, in effort ofreceiving high Return on Investment (ROI) (Alicke et al. 2016). The application of businessanalytics, specifically on Big data and AI has proven to favor the industry’s objective inoptimization. Nonetheless, labor issues and financial capabilities should be taken into accountas applying analytics software into companies required a significant amount of upfrontcapital. The expectation moving forward is to be able to minimize operational errors and risksby anticipating in advance.

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