The Times’ Raconteur Special Report stated that 56% of businesses are using Big Data to improve Supply Chain visibility. Results of a Gartner report, published in the same article, claim that 57% of chief executives ranked supply chain optimization and traceability as a first priority for technology investment.

With data visualizations from technologies companies can gain full visibility of their end-to-end Supply Chain, tracing products by mapping geolocation and even temperature. These technologies are transforming how Supply Chain management decisions are being made.

Streamlining the supply chain means managing multiple complex factors all at once, in real time: optimizing inventory, streamlining transportation, and delivering just in time, every time. 

At Pivot BI Analytics, we bring everything together in a visual environment that’s accessible to our clients at the push of a button using advanced modeling and forecasting reporting tools for top management needs. Pivot BI Analytics opens an elegant and powerful view into your existing metrics and allows you to create new, customized business intelligence reports to enhance your supply chain analytics.

Supply chain is a function of planning and forecasting

Physically, a supply chain network is about the movement of goods and services. But in today’s fast-paced world, where an hour of lost time is not an option, idle manufacturing facilities are a waste of resources, where losses are not just measured in actual numbers, but potential numbers as well (opportunity cost). Supply chains have become much bigger than just logistics, warehouse stock movements andpositioning of resources.

Supply chains have become complex, convoluted, and highly integrated systems. Such is the complexity that you just can’t work on them efficiently anymore without a lot of help from advanced computing techniques and intricate data visualization systems. The numbers involved are too complex. ERP systems have helped to make the data generally available but converting or morphing unstructured data into actionable insights remains a challenge.

Real time Inventory- Data-Driven Stock and Ordering

Big Data solution can help retailers optimize supply chains to reduce cost, improve service, and gain vital insight. Big Data analytics are used to predict inventory positions in stores and distribution channels. This is achieved by utilizing demand plans and forecasts, sales history, external predictors of future performance such as category trends, weather patterns, local events and so on, enabling retailers to reduce both out-of-stocks and over-stocks.

Big Data analytics can also deliver full supply chain visibility by capturing real-time inventory positions across the enterprise and through the extended supply chain. Data to be leveraged include open purchase orders, in-transit inventory, or vendor and distributor inventory. This information is critical for retailers looking to deliver an omni-channel shopping experience to their customers. 

Improving the supply chain operations with a data-driven approach requires bringing together different skillsets to 1) obtain access to the relevant data, 2) bring the data together from the various siloes it currently resides in while ensuring data quality, 3) defining how the data should be used and how it will guide decision-making, and 4) running the analytics and delivering insights to the business decision makers in a timely manner and in a usable format.

You can’t manage what you don’t measure, and you can’t measure if you can’t make sense of what data you have. The best way to make sense of data is to tap into the highly developed visual sense of the human mind. This article introduced you to some useful tools for visualizing data.

 However, you don’t need to reinvent the wheel and start everything from scratch or redeploy installations. Web-based utilities are a great starting point to use available extracted data and build quick demonstration visualizations. If their use catches on and users find real value in it, more complex solutions can be built by using advanced packages. But as a starting point, you can use the methodology and tools that are mentioned in this article to build simple and straightforward proof of concept demonstrations.

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