Sciera | The Big Impact of Big Data and Analytics on the Supply Chain
1290
post-template-default,single,single-post,postid-1290,single-format-standard,ajax_fade,page_not_loaded,,qode-title-hidden,qode_grid_1300,footer_responsive_adv,qode-theme-ver-17.2,qode-theme-bridge,disabled_footer_top,wpb-js-composer js-comp-ver-5.6,vc_responsive

The Big Impact of Big Data and Analytics on the Supply Chain

Data has been growing at an exponential rate and is being looked upon as a company asset that can be mined for important insights. The data from the supply chain is no exception. To add to that, in many ways, the timely availability of goods has become as important as the quality of the product in delivering customer satisfaction.

The supply chain has for long been undergoing a digital transformation. IoT enabled RFID chips havegiven companies the ability to get a real-time view into the location of each consignment. This transparency has allowed companies to become more nimble in how they stock, deliver and fulfill demand in an age of volatile consumer preferences. But this optimization of time and the resultant reduction of cost through increased efficiency can only be achieved by insights provided by supply chain analytics.

A digitized supply chain feeds invaluable data into complex Analytical algorithms that help organizations predict future demand and help organizations in reducing cost & lead time. By being more agile, organizations can make the most of the available market opportunity, achieve greater customer satisfaction, and deliver better outcomes.

Analytics was already playing a role in the supply chain but it was not playing as much of a role. This isbecause earlierAnalyticscould be applied only tostructured data and not on unstructured data. These days all manner of unstructured data, including fromCCTV camerasis also being used in analyticsacross the supply chain.

To understand the importance of analytics in addressing these challenges, let’s take a look into the value chain of a typical SCM process. It starts with operations planning, then sourcing of raw materials, followed by production, and then storing the finished product in a warehouse. Then, of course, comes the shipping part –i.e. how the products reach the point of sale and ultimately end up with the customer.

The operation planning team pulls in data from the ERP and CRM systems to predict raw material order volumes. This helps in making quicker delivery a reality. The sourcing team for its part carries out an analysis of vendor pooling, cost-modeling, and demand-supply balancing to reduce the procurement cost and improve just-in-time stocking of materials.

Analytics in production is a vast field. The efficient scheduling of production for energy-intensive processes along with statistical analysis to reduce wastage has now become proven.  Visual Analytics is playing a pivotal role in allocating storage locations as well as in scheduling pick-up personnel for the goods in a warehouse. Options like the Monte Carlo Analysis to provide real-time routing of delivery have been exploited by e-commerce companies to great effect.

On the POS front, the detection of minimal reorder points, shelf-space optimization, scheduling of outlet employees, and store allocations can also be streamlined using supply chain data.

Lastly, there is a key role for customer analytics for sales forecasting to ensure timely delivery of goods.Big data can positively impact each of these levers. By exploiting the key levers, marketing, procurement, warehouse management, manufacturing, and logistics a greater degree of operational efficiency can be brought in.

Opinion-makers such as Gartner say that it is becoming impossible for companies to operate their supply chain without data-driven decision-making. Let us look at examplesof a couple of areas where Analytics can play a huge role in the Supply Chain.

  • Demand forecasting

By implementing advanced technologies like Machine Learning and Data Science organizations can predict the demand for a product or service and can make it available in the stores when it is needed. For example, Amazon factors in external factors like movie releases and pop-culture references to define when demand will shoot up for specific merchandise in particular locations. They can then go ahead and stock their fulfillmentcenters, to reduce the time gap between order and delivery.

  • Optimization tools

The use of traffic data obtained from different sources is key to good fleet planning, as it helps to reduce the number of vehicles used and providedelivery locations with an accurate time of delivery. Good fleet management systems, through optimization tools, can transform an otherwise inconclusivemass of information into trends and indicators which can help in decision-making, both for strategic planning and for day-to-day issues.

  • Visualizing delivery routes

With the use of geo-analysis companies can dynamically reviewmillions of data points and model hundreds of scenarios to plan optimalroutes. Consider the value of being able to define the fastest route when the delivery has to factor in multiple stops.

Big data is becoming mandatory for most organizations to take the guesswork out of their supply chain operations. The aim is to make the supply chain more efficient. And this is critical to business. Like Supply Chain industry leader and opinion-maker Wael Safwat says, “It’s not the organizations that are competing. It’s the supply chains that are competing.”