Leveraging Analytics to enhance procurement’s value

Analytics is increasingly playing a prominent role in the the world of Supply Chain, bridging the gap between the physical, information and financial flows, and increasing functional integration within an organization’s intra and inter Supply Chain operation by adopting data analytics that processes data into information.

Considering the volume of transactions that Supply Chain generate, and hence the corresponding data, there is no dearth of  data that can be leveraged to draw insights. Being a sub function within Supply Chain, this applies to procurement as well, meaning that procurement also has and will generate considerable value by leveraging data.

To provide an overview of what analytical techniques are leveraged within procurement, I will use the three widely known categories of procurement:

  • Descriptive
  • Predictive
  • Prescriptive

Descriptive

Descriptive analyticas processes historical data to provides information that highlights past events, and highlights issues where an intervention is required. Examples from procurement perspective include:

  • Product availability issues and stock outs
  • Customer returns by stock keeping units (SKUs)
  • Inventory mark downs or write-offs by SKUs
  • Products returned back to suppliers
  • Quality issues by SKU and Suppliers
  • Product lead times
  • Customer complaints

Note that the real enhancement of the information comes from the way it is generated and shared within the organization. That is where procurement visibility tools play a majo role. Sharing this information electronicaly with the organization’s supplier base, who can then introduce corrective actions based on the feedback generated by descriptive analytics, can help greate an agile procurement process.

Predictive Analytics

This form of analytics uses historical data combined with statistical tools and techniques to analyse the data searching for patterns, relationships and trends within the data that can be used to help predict future outcomes. This information can be extremely useful for procurement decisions.

Examples

  • If there was a positive relationship between the weather and product volumes, information captured from weather forecasts can be used to place orders with suppliers.
  • Analyzing Point of Sales data can reveal an unexpected relationship between different product categories within a retail outlet, and provide opportunities to develop promotions or relocate them together to improve customer experience
  • The relationship between the time of the day and the type of product purchased is very important for procurement professionals to ensure product availability and replenishments, for example a lunchtime meal deal where three different items are bundled together.

Prescriptive Analytics

Prescriptive analytics can be used to enhance procurement decisions by using data to develop simulation models, which can be then used to optimize future scenarios.

Examples

A procurement decision can be taken to change the mode of transport for the primary inbound distribution between a supplier based in Europe and the buyer in the UK from road freight to one based on intermodal using rail. The impact on cost, lead time, service levels, safety stock calculations, customer service and environmental factors would all need to be derived before making the decision to proceed with the change.

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