How Advanced Analytics & AI Can Transform Procurement in Retail

Buying the right quantity of products, at the best price, is key for retailers looking to reduce the tendency to overstock items and optimize their margins.

Continue and discover how Advanced Analytics and AI can help a retailer make more data-driven decisions & automate actions in the fiedl of Procurement.

The growth of research in the domains of data science, machine learning and AI enables data-driven organisations to take things even further. These new technologies and techniques enable a new way of extracting patterns from data, of deriving insights from data.

Insights that help data-driven organisations to better understand what is happening and why it happened. Insights that permit a retailer to predict what will happen, anticipate and prescribe actions, applying ever more advanced analytics.

This article focuses on 3 jobs-to-be-done in Procurement Management where Advanced Analytics & AI can create the most impact with your data:

1. Improve your price negotiation tactics using Advanced Analytics & AI

Get a better view on facts, as you prepare negotiations

Negotiation on pricing, lead times and terms is crucial to each retail organisation. Getting a better deal, given a certain set of constraints, can have a significant impact on profitability. In the past, procurement officers have often been confronted with the need to oversimplify the growing amount of data to keep their work manageable.

However, decision-makers in procurement tasked with purchasing items at the best price, at the most convenient moment, can no longer afford the luxury of making decisions based on inferior insights or mere gut feeling. Fact-based preparation of negotiations with vendors based on verified events and new insights has become essential.

As a result of digital transformations in both vendor and purchasing organisations, the amount of available data has expanded substantially. Access to such data remains, however, an issue. So does having the technology and people with the right skill set present in the organisation to define what data is important, for what purpose and how it can be made readily available to be used.

Tools making use of machine learning algorithms can be applied to classify and categorize data, enabling the use of advanced analytics that describes, explains, predicts or prescribes what purchasing decisions should be taken. Through visualisation, the results can be made available in multi-level dashboards that empower a decision-maker with newly acquired insights to steer negotiations, even with real-time reports, ensuring he or she gets the best deal possible.

Find insights that improve how items are procured

By taking advantage of known data sources (such as time-series pricing datasets) and identifying new ones ( such as available competitor data) companies can bring together a wide array of datasets fueling a decision-making process improved by better insights.

Traditional use of data has often overlooked such insights. Certain events can have a tremendous impact on pricing but can occur delayed in time, the reason for which they might go unnoticed. Such can, for instance, arise when prices of raw materials such as oil or crops affect the price of basic materials such as plastics or fabrics.

Find price drivers, spend outliers, hidden costs and many more. Enable early warning systems for price deviations between purchase price and ‘should price’ or create dynamic vendor scorecards in terms of pricing. By applying advanced analytics and AI, a decision-maker can significantly increase his or her negotiating power and ensure better purchasing decisions are made. Such can additionally lay the groundwork for increased automation of the purchasing function, where the potential return on investment is sizable.

2. Use Advanced Analytics & AI to predict your order quantities

Access more data to enable predictions

New emerging technologies used enthusiastically by consumers have made procurement in retail more challenging than ever. The demand for ever-lower prices, vast assortment and swift delivery, accompanied by an appetite for convenience and frictionless ordering & returning of goods, prompts a data-driven procurement strategy that enables proper planning. Especially, with regard to order quantities.

The rise of e-commerce and digitization of other retail business functions & their processes certainly significantly multiplied the ability to gather relevant data, enabling much better planning.

The use of demand forecasting in retail

By deploying machine learning algorithms and other data science techniques, patterns can be found that permit a better prediction of materials required or forecast of sales. The latter is often referred to as demand forecasting with the aim to reduce resource planning errors that have a considerable effect on ROI, revenue and profitability.

It is common good that retailers put 5 to 15% more items on shelves than they are about to sell, an act to overcompensate for the risk of running out of stock. Additionally, overordering is often conceived as the less expensive choice over underordering. However, making such a decision entails that significant working capital resources are tied up in non-moving goods, underutilizing shelf space in shops with expensive square meter pricing.

The use of advanced analytics and AI enables retailers to walk that fine line between over- and underordering. By training various forecasting models depending on product properties and promotional strategies, retailers can develop an overall strategy that focuses on accuracy where needed or allow for more flexibility were such can be sustained.

Simplified, a retailer can be prompted with an accurate order size, upon request. Such can be shown in a dashboard view, flavoured with insights on how it impacts the entire supply chain. Bringing substantial amounts of in-house and third-party data together enables retailers to intelligently mitigate risk.

By understanding patterns in demand and the impact of factors such as product life cycles, competition, and changing market dynamics impact, retailers can continue to reduce the bullwhip effect that is caused by the distorted flows of information up and down the supply chain. That is the true promise of advanced analytics in retail.

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

3. Create better vendor relationships, driven by Advanced Analytics & AI

Putting a wealth of data to work

Improving performance is a responsibility in the hands of every part of the retail organisation. Given the fact that procurement is often rich with data, it is the ideal business function to spearhead the transformation towards more evidence-based decision-making driven by data and algorithms.

The relationship between vendor and retailer is one where the ‘human aspect of doing business’ remains highly relevant. Vendors have been chosen a while ago and as a result of multiple purchasing cycles, trust between people was established. Such should however not mean that there is no room for improvement of performance.

Better insight into the vendor-retailer relationship can increase transparency, eradicate bias and even increase the aforementioned trust between parties. The exchange and analysis of parties’ data, applying advanced analytics and AI, can significantly augment the decision-making that is central to the relationship. It enables the retailer to better understand, monitor and follow-up the relationship with each of its vendors.

Measure performance, benchmark and assess the value

How? Advanced Analytics and AI can be used to measure the individual performance of a single vendor. Such can entail measuring real-time performance, providing info on quality, lead times, compliance to payment terms or various types of contract compliance.

When performed with multiple vendors, retail organisations can develop new insights through benchmarking of individual results against clusters of similar vendors. Retail organisations can, as a result, be provided with the opportunity to target specific vendors with favourable contracts. Bringing both internal, partner and third-party data together, retailers can even take risk mitigation one step further.

Tracking invoice compliance could lead to the detection of fraudulent or deceitful transactions. Tracking external data could provide insights with regard to the impact of political instability, cybersecurity breaches or hazardous situations stemming from environmental issues or lack of corporate social responsibility.

The use of advanced analytics and AI enable retail organisations to assess whether an established vendor relationship is still bringing enough value to the company for it to remain sustained. By capturing and analysing these untapped sources of value that used to be hidden in data silo’s, a retail organisation can be empowered by comprehensive dashboards to make better purchasing decisions. It can also take a leap forward, automating a significant amount of these purchasing decisions.