Today’s business world is inundated with data. Transactional tools such as ERP, CRM and POS systems have amassed large volumes of historic data, which if mined correctly and timely, can aid in better supply chain decision making. In addition, today’s interconnected and hyper-competitive business environment demands that supply chain managers take a dynamic and an end-to-end view of their digital supply chain when making crucial planning decisions. This has substantially increased the streams of data that supply chain managers must constantly pay attention to forecast demand and manage supply. These crucial data streams could each easily generate terabytes of data each day, and  includes structured data from plants, warehouses, vendors, stores and even customers collected through means such as EDIs, or RFIDs; and unstructured data such as web logs, social media mentions or even weather data.

Traditional supply chain management (SCM) systems are not equipped to support the data heavy digital supply chains of today. Traditional SCM systems do not look for data in an automated fashion, nor can they quickly analyze vast volumes of data to generate the necessary planning insights. This has forced supply chain managers to rely on a combination of isolated planning tools that make analysis based on static viewpoints of the supply chain with a heavy amount of intervention from spreadsheets and tribal knowledge to fill in the missing gaps.  

Integrating Big Data Analytics to Supply Chain

Supply chain analytics at its essence is about transforming all the gathered historic data and incoming flow of current supply chain data into insights for making better planning decisions.  Using big-data analytic tools that employ sophisticated algorithms to aggregate and dissect data, supply chain analytics produces (i) Descriptive, (ii) Diagnostic, (iii) Predictive, and (iv) Prescriptive insights to empower supply chain managers to make better supply chain decisions.

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Each analytic component serves a larger purpose. Descriptive analytics, which include KPI tracking and internal reporting metrics based on historical data such as what sold where and how many, provides the baseline analytics foundation. Diagnostic analytics, takes the data a step further by pointing to the root-cause of issues based on patterns in the historical data, enabling directional guidance for faster reactions to fix problems.

Predictive analytics uses statistical techniques to estimate the likelihood of future events such as stock outs or movements in your product's demand curve. It provides the foresight for focused decision making that avoids likely problems in the future. Finally, Prescriptive analytics closes the loop by tying all the analytics components into actions and automated decisions (with exceptions based planning) to improve the bottom line supply chain results.

A supply chain analytics platform not only includes advanced science for predictive modeling, scenario analysis and optimization in the back end, but also includes interface tools such as monitoring and visual dashboard capabilities with advanced query, reporting, alerts and drill down capabilities on the front end. It is not a black box but rather a platform to provide views and insights on your end to end supply chain to drive timely management decision making.

Value of Supply Chain Analytics 

It is ultimately the better decision making that reveals the true value of supply chain analytics. Supply chain analytics has the power to elevate your entire supply chain’s performance: from higher revenues due to reduced missed sales because of timely predictive alerts of impending stock outs, higher margins because of prescriptive analytics that recommended timely promotions before the inventory goes stale, faster inventory turns, to an all-around improvement in the productivity of your supply chain team.        

Solvoyo Supply Chain Analytics Platform

Solvoyo combines the best of supply chain planning and supply chain analytics into one integrated platform. The Solvoyo Elevation Platform harnesses the power of big data analytics to drive insights to make smarter supply chain planning decisions with a comprehensive view of your complex supply chain.