7 November 2017
Using the past to understand the future
Today’s supply chains are more multi-tiered, geographically far-reaching and complex than ever before. This can have major implications for supply chain transparency and the ability to identify opportunities, mitigate risks and ensure that a supply chain is operating efficiently and cost-effectively.
Among the technologies businesses are looking at to help increase transparency is predictive analytics using algorithms or artificial intelligence.
Predictive analytics uses historical data and market information to forecast future spend by providing greater visibility across a supplier network in a single location. Capturing, managing and analysing this data enables businesses to make cost savings and mitigate supply risks when determining future demand.
Creating a golden source of data
A single, comparable dataset is fundamental to forecast future demand but it isn’t always easy to gather this information.
James Jenkinson, Vice President at Efficio, says: “Different organisational entities may work on different systems using different supplier names, agreements and units of measure, meaning reference data can be inconsistent across an organisation. The structure of goods and services cost data is also likely to be different.”
Add to this the fact that suppliers – rather than the client organisation – typically hold a large proportion of this data, and the challenge of gathering inconsistent input data into a single location to provide comparable output data is clearly evident.
Without a single, uniform dataset however, an organisation will probably not have, or even understand, demand-forecast processes. Moreover, stakeholders are likely to have a lack of confidence in procurement plan figures.
Identifying cost savings
Predictive analytics provides a detailed and long-term view across different business units of the goods and services required by an organisation based on historical data. It puts businesses on the front foot and improves the bargaining power of procurement teams by giving them longer lead times to plan and source what the business requires. Goods and services can be procured in the most efficient way and deliver maximum value for money, for example, by reducing inventory costs and through more efficient resource allocation.
Jenkinson says: “Having this type of information at your fingertips is invaluable. It allows you to order the right things, at the right time and for the right price – reducing your capital requirements substantially. At the same time, you can monitor budget against demand forecast to ensure you continue to meet targets.”
Mitigating supply risks
Businesses can also use predictive analytics to review the dependencies of their supply chain and proactively identify supply risks.
They can monitor whether, for example, supplier framework agreements are being complied with. Are they still fit for purpose? When do they expire? Should they be renegotiated? They can also determine if they are overly dependent on a supplier or a group of suppliers and whether a strategic review might be necessary. Equally, if there are goods or services that a business needs but which have yet to be assigned to a supplier, they can identify where these gaps are and run a tender process to fill them.
Jenkinson says: “Businesses have access to a wealth of historical data that can give them greater visibility over planned spend across the organisation, but many lack the means to use it to predict future requirements. For those businesses willing to tap into predictive analytics, there are significant opportunities to improve the bottom line and reduce supplier risks – crucial if businesses are to survive and thrive in today’s uncertain world.
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