Using Prescriptive Analytics for Supply Chain Planning

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According to Gartner, 85 percent of supply chains use prescriptive analytics. To help your business improve decision-making and quickly sort through huge amounts of data, following are a few advantages of using prescriptive analytics to optimize and improve supply chain planning.

Descriptive, predictive, and prescriptive analytics

Supply chains have a lot of data to sort through for their supply chain planning process. For analytic solutions, businesses use predictive, descriptive, and prescriptive analytics, which can encompass statistics, mathematics, machine learning, and predictive models for effective decision making.

  • Descriptive analytics: Descriptive analytics provides hindsight into past performance. It reviews activities based on data mining techniques to review raw data. Statistical data can come from a few minutes ago or a few years ago and then generated into reports. An example is a business that wants to review line graphs, bar charts, pie charts, or tables.
  • Predictive analyticsPredictive analytics addresses what will happen in the future if specific actions are taken. It determines what future directions might look like. An example is a business that wants insight through “what if” scenarios based on explanatory variables to make future predictions.
  • Prescriptive analytics: For supply chain planning processes that need recommendations for more efficient and data-based decision-making, prescriptive analytics is recommended. It can employ techniques like AI and machine learning. An example is a business that wants advice on a possible outcome and what suggested actions they should take.

Why prescriptive analytics

To understand why prescriptive analytics is recommended for supply chain planning, it helps to understand how businesses use analytics for processing data.

Cummins: Improving the customer experience

One way to use prescriptive analytics is with mobile diagnostics to improve the customer experience and determine future spending patterns. Cummins is a Fortune 500 company that designs and manufactures alternative fuel engines, diesel fuel engines, and power generation products.

Cummins uses automation techniques gathered from data points to up-sell and cross-sell services. Technicians alert customers remotely if they are due for oil changes or need filters replaced and get their responses in real-time.

The data gathered from consumer spending habits and personalization opportunities gives the business clear insight into targeted sales approaches for influencing customer purchasing decisions and future profitability.

CPG and retail chains: Leveraging prescriptive analytics

Some retail and CPG chains use prescriptive analytics to manage millions of data points and help them increase sales and profit margins:

  • Walmart has about $36 million in sales per hour.
  • Walgreens has over 8,100 stores.
  • Fashion retailers sold 34 million products in 2016.
  • Unilever has over 400 brands and 50,000 SKUs.

With prescriptive analytics, store-level supply chain plans can recommend which items to reorder and when. Analytics target customer demand, determine the best products to showcase, and determine how much inventory to add to meet seasonal customer demand in-stores and online.

Businesses that use demand forecasting and demand planning software can better allocate resources, improve financial planning, and achieve forecasting accuracy. Inventory optimization software can also help with logistics, inventory levels, and shipments.

UPS: Leveraging advanced technology

UPS analyzes data from hundreds of sources and can push route optimizations to its entire fleet, saving millions of dollars annually on fuel.

UPS spends about $1 billion annually on on-road integrated optimization and navigation (ORION). Offering advanced algorithms, its benefits include:

  • Processes 250 million data points and route recommendations, saving 100 million miles annually and 10 million gallons of gas
  • Monitors driver habits, and can tell when drivers are delayed in traffic
  • Recommends best routes, determines if drivers need further training, and predicts delivery fleet maintenance to reduce breakdowns that can delay package deliveries
  • Lets customers interact in real-time with UPS My Choice service for delivery updates

To reduce negative sentiment with customers and improve the customer experience, predictive maintenance helps UPS stay on track and on schedule.

In Conclusion

With huge amounts of data points, prescriptive analytics provides insights to mitigate future risks and explore future opportunities. Supply chains that leverage new technologies are better able to sort through data for accurate decision-making.