Predictive Analytics in Procurement

Table of Contents

Introduction
Why is Predictive Analytics Important to Procurement?
Use Cases of Predictive Analytics in Procurement

Value from the sourcing process
Knowing changing customer demands
Best quality and best price
Accurate spending data
Stock transit

Metrics Used by Predictive Analytics in Procurement
Examples of Predictive Analytics in Procurement

Apple’s supply chain
Amazon

Conclusion
Topics in Predictive Analytics

Introduction

Predictive analytics in procurement could be described as the use of available historical and current data; this includes material, supplier, spend and catalogue data analysis to do an analysis that will predict future trends and outcomes.
To understand the purchasing department, predictive analytics analyzes the activities to keep track and learn the patterns, behaviour and strategies to keep the records of an organization in an ideal condition for a better tomorrow.

Why is Predictive Analytics Important to Procurement?

Even though predictive analysis is relatively new to the procurement department and other growing departments, its importance cannot be ignored, especially for those organizations that have been doing consolidation, segmentation, supplier leveraging and spend analytics.

For laser-sharp prediction, understanding and analyzing the commercial and technical aspects of the supply chain ecosystem married to statistical modelling capabilities is necessary.

Prediction analytics in procurement is needed to project revenue, mitigate disruption and identification of potential market opportunities.

Use Cases of Predictive Analytics in Procurement

Value from the sourcing process

When awarding contracts, procurement can use a data-driven approach by use of predictive analytics to assess historical and current supplier’s performance data. This will help in risk assessment and current market pricing as well. Sourcing teams on the other hand can extract more value from logistics by examining carrier data and shipping routes.
Getting the true value for labour is the reason procurement can source better. when sourcing variables such as cost, labour output and freight, for instance, predictive analytics tools help to analyze the composite conditions of sourcing and incorporates the multiple components involved in the best cost for sourcing assuring better operational profitability.

By quantifying the value of labour cost, resource professionals can compare it with outsourced resources to quantify the value of work done to achieve optimized cost for utilized service.

Knowing changing customer demands

Predictive analytics can help predict fluctuations in demand and allow the procurement department to make necessary adjustments. With historical data and current data at hand, procurement can point out changing customer needs and predict changes in supply and demand for sales automation and component purchasing. This will help improve customer loyalty, service delivery and make logistics more efficient.

Best quality and best price

The electronic component purchasing team can always assure they receive the best quality of products by using predictive analytics to get products with the best price. By comparing historical data and current data to check trends in pricing and quality, procurement can continuously assure they are controlling their spending by getting the best prices and can ascertain quality as well.

Accurate spending data

Predictive analytics plays important role in matters spend. It detects spend leakage and explore spend saving opportunities by checking the total spend and choosing the best buyer. Spend analysis on the other hand identifies critical improvement areas and create pathways for gaining operational benefits. Through vendor consolidation, cost reduction has helped companies minimize costs through control of price variance.

Stock transit

By monitoring the status of products and materials as they transit the supply chain. Predictive analytics will identify unforeseen challenges beforehand; these will greatly alert the procurement professionals to be proactive in responding to potential chain interruptions. Mitigate actions can help save cost and time for product and production lines.

Metrics Used by Predictive Analytics in Procurement

Procurements metrics can be divided into three categories; those that ensure quality, deliver savings and improve delivery.

Metric Category	Description
Superior quality rating	Quality rating is an important metric in understanding the quality of your supplier’s performance. This can analyze the present and future performance and relationship with suppliers. Corrective measures may be placed after a warning alert that a supplier will perform poorly and necessary changes will be made. The whole point of this metric is to monitor suppliers to predict future outcomes and take necessary actions in an attempt to evaluate the defect rate, negotiate future contracts and reach the best quality score.
Procurement cost reduction	Cost reduction is important among procurement KPIs of cost management. By analyzing historical data of saving done and comparing the current to the new ones, companies can use predictive analytics to predict a future expenditure and make necessary adjustments in procurement cost thereby saving a great deal. This is an important metric because it has a direct impact on the cost statement.
Lead time	This KPI measures the time taken from initiation of a procurement action to the time of delivery. It is a composition of two key things; administration leads time and production lead time. If the data indicates that the lead time is taking more than anticipated or analysis predicts a failure over time, measures can be taken to prevent anomalies and delays.

Examples of Predictive Analytics in Procurement

Predictive analytics can provide more assurance with regards to shipment information, ETA et cetera, thereby protecting profit margins, reduction of network latency and reduced cycled times.

Apple’s supply chain

To understand real-time visibility and demand patterns, Apple is using predictive analytics to establish and anticipate online orders for products.

Amazon

Amazon’s move to acquire Whole Foods is indeed a jackpot. It uses whole Foods’ data to gain access to physical stores and customer data. This data has allowed them to anticipate shipping and stocking using real-time data.

Conclusion

Predictive analytics is the future. Procurement departments are fast embracing the use of artificial intelligence, big data and machine learning to enhance processes, minimize loss and expenditure and increase profitability.

Effective implementation and informed application of predictive analytics in procurement can help organizations make major improvements and wise decisions in future. It also paves way for deeper understanding and research for cognitive technologies that will forever change the procurement landscape.

Topics in Predictive Analytics