Monday, September 29, 2014

The Role of Data Analytics in Supply Chain Management

Research shows that today, nine out of ten companies face problems in improving supply chain performance. There are two major issues: the understanding of the supply chain as a complex system and the effective use of data.
The supply chain provides opportunities for leveraging data analytics, partly because of its complex nature and partly because of the role supply chain plays in a company’s cost structure and profits. However supply chains can seem to be very simple and the traditional approach of managing supply chains can guise the opportunities to perform better by analyzing data and by adopting a predictive rather than retrospective orientation to the data.
Data Analytics in supply chain has been used by the US military in World War II by applying logistic models. UPS was the forerunner in applying analytical approaches to distribution networks, inventory optimization, forecasting, demand planning, risk management, and other applications. Large retailers, such as Wal-Mart Stores and Target, have had considerable success with supply chain analytics, often working in collaboration with suppliers. And carriers like UPS, FedEx, and Schneider National wouldn't dream of managing their operations without a variety of analytical models.
However the use of supply chain analytics has declined in recent years. The companies have failed to take the full advantage of analytics and even when supply chain analytical tools are available, the tools are often unused because of lack of skills and understanding.
Supply chain analytics can help achieve higher levels of performance. This day to day usage of supply chain analytics can be used to address a large number of issues:

1)      Help Connect Demand and Supply in Real Time
Linking supply chain analytics with metrics and data on the demand side will help to optimize operations. For example, price changes or promotion will change demand and hence the necessary adjustments must be made to the supply of the product. Similarly changes in availability of products should be reflected in the marketing and sales process.
Dell has pioneered the integration of supply and demand by suggesting to the call center customers ways to shorten delivery times or take benefit of the excess inventory. This was done by human decision making and tracking supplies. However the need for today’s real time online business environment companies will have to apply analytical models that will continuously integrate supply and demand without human intervention. Such models would automatically offer promotions to customers to push the products based on the availability of inventory. However, the primary obstacle to implementation generally is a lack of collaboration among multiple transaction systems, in a way that allows companies to make informed decisions in real time.

2)      Analyze supplier risk
Success of a large number of organizations is highly dependent on their suppliers. However supplier’s use of technologyhas been limited to simple metrics and reporting in organizations.
The creation of supplier resiliency scores will help companies decide to pursue secondary sourcing or work with existing suppliers to identify alternate locations. Analytical tools that incorporate public and third party data can help organizations determine whether the critical suppliers can meet increased demand during an upturn. Nowadays, predictive statistical models have been used to accurately forecast the supplier failures and identify attributes associated with the failure.

3)      Use of Sensors:
 One of the primary drivers of analytics in organizations is the availability of extensive data. As their use expands, new sensors, radio frequency identification (RFID)—will make dramatic amounts of data increasingly available for the next generation of supply chains.
The data that is generated through the use of sensors like RFID can be used in a variety of ways to optimize the efficiency and effectiveness of their operations. The data can be transactional and can be used to benchmark with competitors. For example, Daisy Brand uses RFID analytics to determine the time it takes for products to reach shelves and replenishment rates. This data is particularly useful for promotions. Daisy Brand also makes extensive use of Wal- Mart Stores' Retail Link data, which provides suppliers with weekly point-of-sale and inventory information, in its analyses.
UPS, Schneider are using GPS-based telematics devices in trucks and trains. These devices provide a wide variety of data about driving behavior, speeds under various conditions, traffic, and fuel consumption. UPS is using telematics data to redesign and optimize its entire delivery network for only the third time in its more than 100-year history.
The ILC sensors (identification, location, condition) can monitor variables such as light, temperature, tilt angle, gravitational forces, and whether a package has been opened. They can transfer data in real time via cellular networks. Thus, the potential to identify supply chain problems in real time and take immediate corrective action is greatly enhanced with this technology.

4)      Improving Analytical Literacy
 Better decision making in supply chain management is often hindered by the inability of managers and front-line personnel to understand and apply analytical models.
The motor carrier Schneider National, for example, has developed a simulation- based game to communicate the importance of analytical thinking in dispatching trucks and trailers. The goal of the game is to minimize variable costs for a given amount of revenue while maximizing the driver's time on the road. Schneider uses the game to help its own personnel understand the value of analytical decision aids, to communicate the dynamics of the business, and to change the mindset of employees from "order takers" to "profit makers." 
“Analytical Apps” are being introduced by several business intelligence and software vendors. Analytical apps that have been developed for supply chain functions include tools for supplier evaluation, inventory performance analysis, transportation analytics, and transportation contract compliance.

The future of supply chain analytics
The use of analytical tools such as ERP systems, the Internet, RFID, and telematics is becoming common, and more organizations are generating considerable amounts of high-quality data. Now that companies have more and better data than ever before, it is only natural that they would begin to use it to analyze, optimize, and make predictions about their supply chains.
However the increased use of analytics affords the organization a large number of advantages, are the organizations ready to move towards an analytics driven supply chain management?
Every company with a supply chain devotes a fair amount of energy to making sure it adds value. But new tools and disciplines now make it possible to drill deeper into supply chain data in search of savings. However can advanced analytics extract additional value from your supply chain, or do approaches based on traditional metrics deliver the best ROI?



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