Cash & Carry adoption of data science is growing momentum

The first book on wholesaling — Wholesaling Principles and Practice (1937) by Beckman and Engle states that “During the era of rapid change in the field of wholesaling which began in the middle of the twenties, the cash and carry wholesale house was ushered in.”

Wholesalers buy primarily from manufacturers and sell mainly to retailers, industrial users and other wholesalers. They also perform many value-added functions.

The main traits of cash and carry are summarised best by the following definitions:

• Cash and carry is a trade in which goods are put on sale by a wholesale warehouse and utilised either on self-service grounds or using samples.
• Customers include retailers, caterers, institutional buyers, etc, who settle the invoice on-site and carry the goods away.
• The main difference between “classical” wholesale sales and cash and carry is that their customers transport goods themselves and pay on the spot, rather than on credit.
• Access to cash and carry is normally restricted to operators of businesses, and the general public is not admitted.

The problem

FMCGs and consumer goods evolution in the B2C segment towards consumer understanding, improved brand experiences using data, automation and process improvement has left the wholesale lagging behind.

The opportunity

Using Data Science, Artificial (Augmented) Intelligence and advanced Analytics these data-driven technologies will generate improvements across the whole supply chain, from procurement, throughout inventory control, to cart check-out. AI can also create more efficient lines of communication and interoperability between systems, functions, processes and people (staff, providers and customers alike.

How to seize this opportunity

Wholesale companies must always collect their customers’ purchase history as a B2B operator.

Therefore these companies have a significant advantage regarding B2C (who often struggle to obtain this kind of information/data.

Many already have data sets of great value stored and underused in one or more data warehouses and should start to make better use of this information.

Most are not fully aware of the actual value of the data as a strategic asset and its economic value to their growth potential.

We will highlight a few different systems that can work autonomously or combined. Their application in the wholesale sector not only focuses on customer experience and revenue generation but also enables a rational use of resources, improving the management of stocks and perishables. In turn, this creates a positive impact on the sustainability of the wholesale distribution sector.

• Recommendation engines can help to improve the purchase process and help clients to remember what they usually purchase by anticipating their needs. It can also encourage these customers to buy more of the same or purchase new products that engage them. Algorithms found in the back-end of recommendation engines can suggest products based on purchasing habits of every customer while considering key variables of product supply, margin per unit and availability.
• Optimisation of purchase times and movement flow of customers through the store by using AI applied to digital floor plans. Additionally, insights distilled from these applications can help with a more efficient design of new floor plans and redesign of underperforming stores based on the shopping behaviours of existing end customers.
• Stock management and forecasting: Prediction models applied to stock management and storage forecasting, aligned with future purchase recommendations. Forecasting using Data Science for precision is critical for sustainability and competitiveness, especially when dealing with perishable goods. Market players in this sector need to be conscious that forecasting using spreadsheets is no longer an option to stay competitive.
• Loyalty systems evolve and enable individualised, tailor-made programs and promotions in line with the products suggested by the recommendation engine. Supported by automated alerts including product offers, suggestions and ideas using SMS or APP notifications increase average shopping basket spend significantly, improving shopper experience and satisfaction at the same time.
• Smart interconnected stock systems to know the accurate inventory of each store in real-time while feeding the recommendation engine to send up-to-date suggestions.

But the role of AI isn’t only to forecast and predict. Access to richer customer insights of higher value will help get other stakeholders and business units more involved, reduce silos and improve communication throughout the operation because Data Science and AI are cross-functional and interdisciplinary tools.

What is in it for early adopters?

AI can help to liven and transform the B2B wholesale market, add value to the final consumer, increase revenues, boost sustainability and generate efficiency leading to growth and profit.

Those who act quickly will see how algorithms can help with resource allocation, stock management, customer engagement, and brand experience all at once.

However, it is still hard to decide how to take the first steps, and there are dozens of possible solutions and approaches depending on the current digital mindset and situation of the organisation.

At Bedrock we are working with partners and clients in the sector, defining custom roadmaps and implementing immediate wins first whilst identifying mid-long term high growth opportunities.

Our pioneering solutions are backed by the Spanish Ministry of Science and Innovation since 2021, which awarded us with a NEOTEC* non-refundable grant to help clients finance the implementation of our ground-breaking solutions.

We are open to including further partners in the project, so don’t hesitate to get in touch if you want to know more!

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*About NEOTEC:

NEOTEC aids are non-refundable and destined for business projects in any technological or sectorial field. Aimed at business models based primarily on services to third parties, only projects developing their own — new technology are eligible.

On average, about 1 in 8 of such projects are approved, and those granted are widely regarded as the most innovative and pioneering applications of all reviewed across industries on a National scale.