5 Ways Big Data Analytics is Becoming a Disruptive Force in Supply Chain Management

Mar 01,2019 by Parul Singh
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Supply chains across the globe are becoming increasingly complex, thanks to globalization and the ever-changing dynamics of demand and supply. Companies are harnessing the power of information technology, or more precisely: big data analytics, to bring in disruptive changes at all levels of Supply Chain Management (SCM). Big data powered platforms benefit supply chain players in several ways:

  • Improved demand forecasting: Demand forecasting is one of the key steps in devising a successful supply chain strategy. Automated demand forecasting empowers companies to respond to the fluctuations in the marketplace and maintain optimal stock levels at all times.

Though traditional forecasting software continued to be used by many small and medium-sized enterprises, they are time-consuming and involve a lot of manual work. With the marketplace becoming more volatile and competition more fierce, it has become imperative for businesses to respond to demand-supply dynamics in real-time and get the right product delivered at the right place and right time. Automated demand forecasting has, therefore, assumed a pivotal role in the supply chain industry.

New generation platforms use historical sales and inventory figures as inputs to study past trends. By integrating these trends with seasonal fluctuations and other factors, companies can forecast demand for each product, warehouse and location in real-time. More advanced AI enabled Big Data Analytics tools even trigger automated inventory alerts and recommend a reorder quantity based on the forecasted sales figure.

Through a judicious use of these platforms, companies can maintain optimum inventory, manage distribution networks effectively and improve warehouse cost efficiency. They can plan out their sales and distribution strategy well in advance to avoid any last minute glitches.

  • Better sourcing and supplier management: Advancement in technology has allowed companies to collate data on different suppliers. Using appropriate big data tools, companies can leverage this data to gain valuable information about the historical record of any supplier. Companies can assess these suppliers against several key performance metrics such as customer reviews, profitability, location, quality of service, level of compliance, etc. and select the most appropriate supplier for any particular commodity and location.

A company can map its supply chain and integrate it with real-time information on weather changes, strikes and traffic to track disruptions in the supply chain and prepare themselves well in advance for any deviation from normal delivery patterns.

Prices of raw materials vary with the changing marketplace dynamics. Factors like seasonal fluctuations, new product launches and changes in marketing strategy can significantly impact the price of any commodity. Big data helps companies examine the simultaneous impact of all these variables on commodity pricing and prepares them for any possible price hike.

  • Enhanced production efficiency: Data coming from IoT devices fitted along production lines provides an enormous opportunity for optimizing production quality and quantity. In this era of industrial IoT, manufacturers use powerful big data tools to pinpoint inefficiencies and opportunities in every segment of the production process. This helps them track underperforming components and processes. Data emanating from sensors in machineries and cameras can be combined with financial and operational data for a more holistic picture of the production process.

With contractual permission, factories can monitor worker productivity by using IoT enabled employee badges which exchange data with sensors installed in production line units.

Predictive analytics in big data has significant applications in product testing. To maintain product quality, manufacturers usually need to perform hundreds of tests on a single product. Using predictive analytics, companies can reduce the number of tests needed for a product and perform only those tests which are absolutely necessary.

Chip manufacturer Intel is a noteworthy example in this regard. The company has leveraged the power of analytics to optimize quality check process for chip manufacturing. Intel has now slashed the number of tests involved in chip manufacturing, resulting in a whopping 25% reduction in quality processing time.

Predictive analytics also finds applications in product customization. Till recently, manufacturers focused solely on large-scale production to save costs. Customization was left in the hands of players serving the niche market. Changing technology has made it possible for the manufactures to predict the market for customized goods and produce them in-house without overspending resources. Since manufacturers can now closely and accurately track the entire production cycle, it is possible for them to identify points in the production line where they can conveniently and profitably insert a new custom process. They can also delay the production process and allow a vendor to carry out customization before the manufacturing process is complete. Manufacturers can thus execute large-scale customization and take made-to-order requests profitably.

Companies rely heavily on their machines and focus on optimizing their performance. Smart sensors installed in machines capture and transmit performance data in real-time and help pinpoint defects, if any. By leveraging analytical tools, manufacturers can analyze tons of such data to predict and schedule preventive maintenance for all the machines. Keeping abreast of wear and tear of machinery helps them to avoid asset breakdowns and prevent any unscheduled downtime. Preventive maintenance also prolongs the life span of machines by preventing irreversible failures.

  • Better warehouse management: Of late, warehouses have started installing sensors to collect data on the flow of inventory. This data helps them pinpoint bottlenecks that hinder the flow of inventory. Sensors can also be used to track the performance of workers in different areas of the warehouse and modify staff allocation for better ergonomics.          

Warehouses need to scan and weigh the items entering and leaving it to collect relevant information on these items. Big data fueled systems can be used to build and manage an exhaustive data base containing all the information on the weight and dimensions of any product. This can improve warehouse management efficiency at an item to item basis.

Warehouses are trying to achieve near-automation of their operations like sorting and handling of items by using robotic pickers and forklifts. Big data platforms can process data gleaned from robotic sensors which can take warehouse robotics to a new level. By analyzing this data, warehouses can optimize stock management, accelerate stock movement and improve warehouse safety.

Amazon, for instance, has started using small KIVA robots to grab products from shelves. They also have drones that can deliver items to a customer living within thirty minutes of an Amazon center. Other ecommerce companies are also expected to take a big leap in this direction.

  • Improved distribution and logistics: Fleet managers use big data solutions to optimize delivery routes. These solutions integrate data from several different sources in real-time such as GPS, weather reports, traffic data, road maintenance data, personnel schedule and vehicle maintenance schedule into a system that advises the vehicle to take the best possible route and reroute whenever needed.

For example, UPS, the American SCM giant has leveraged big data to their advantage in a big way. By examining their data, UPS realized that their trucks turning left was costing them a lot. The vehicle turning into incoming traffic wasted fuel, caused delays in delivery and increased risks. UPS trucks now turn left only when absolutely necessary, not more than 10% of the time. This single change has helped them reduce their fleet by 1100 and deliver 350,000 more packages every year.

Analytics can also help immensely in capacity planning. Companies can compare the demand at different locations with the availability of vehicles and personnel and allocate assets where they are needed the most. This prevents over congestion of vehicles in a particular location and helps them match demand with supply.

Vehicles equipped with visual and acoustic sensors enable real-time tracking of the inventory from the time it is dispatched to the time it reaches the customer. Enhanced traceability brings in more transparency in operations. Companies can track the performance of their drivers and create more accountability for them. This improved access to real-time data on dispatch vehicles can help companies improve service and build long-term associations with customers.

The Last Word on Big Data in SCM Supply chain players have already begun implementing big data solutions to modernize their operations and make more informed decisions. The coming years will witness a disruptive transformation in the supply chain space and create happier, more satisfied customers!

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