How GenAI Changed the Supply Chain and Retail Space Forever

Laura Ritchey admits taking a somewhat unusual path into the supply chain sphere. 

Having graduated with a law degree and an MBA, her career started quite naturally in the finance and consulting world.

It was only after a mentor told Ritchey her skillset would be well suited to operations, particularly retail supply chain, that she began to shift her focus. 

Fast forward to today and she is enjoying her 25th year in operations and supply chain as CEO at Radial, a leading B2C e-commerce fulfilment solution provider powering some of the world’s best customer experiences.

“My passion for leading Radial is fueled by the fast pace of retail e-commerce and the opportunities for our team,” says Laura. 

“For Radial to be a strategic client partner, we are constantly innovating and evolving our business strategy. Our past successes are stepping stones to even greater achievements and that motivates me and the team to push the limits.”

Here, Supply Chain Digital speaks to Laura in depth about generative AI’s dramatic impact on all things supply chain. 

For those unfamiliar with Radial, what are the company's main activities? 

Radial is a leading 3PL provider that offers integrated fulfilment, payment, fraud detection and omnichannel solutions to mid-market and enterprise brands. 

Leveraging more than 30 years of industry expertise, Radial tailors its services and solutions to align strategically with each brand's unique needs. 

Our team supports brands in tackling common e-commerce challenges like ensuring safe and secure online experiences, scaling to meet consumer demands and increasing brand loyalty.

With a commitment to fulfilling promises from click to delivery, Radial empowers brands to navigate the dynamic digital landscape with the confidence and capability to deliver a seamless, secure and superior e-commerce experience.  

What does your role as CEO entail?  

As CEO, a big part of my job is setting our business strategy.

Our focus is on ensuring the customer experience is seamless from the initial click to purchase on the webstore to the delivery of the products to the customer.

To prepare for the future, we anticipate how customer expectations are evolving over the next three years. We often look at logistics leaders like Amazon and Walmart as benchmarks for those changing expectations. We also understand other industries such as healthcare and travel provide great insights about customer behaviour.

Our goal is to retain our dominant market position in e-commerce logistics by remaining agile. 

How are AI and automation driving supply chain efficiencies?  

Over the past few years, retailers have found that operating their businesses has become much more complex. From today’s inflation-cautious consumer to labour shortages and supply chain challenges, consumer expectations are constantly juxtaposed against the reality of the macroenvironment.  

Radial’s latest research found that, following the 2023 holiday season, consumer expectations are still on the rise. The data found that out-of-stock items were the top holiday challenge for 68% of consumers. And if their order wasn’t going to arrive on time, 44% wouldn’t purchase.

Amid this backdrop, AI-powered and automated retail technologies are helping to streamline and optimise operations, forecast demand, speed up e-commerce fulfilment and delivery, and reduce human error – among other advantages.  

Notably, while automation technologies are optimising many areas of the supply chain, they don’t eliminate the need for humans. Instead, they unlock the ability to focus on more valuable work by automating repetitive tasks and removing rote, transactional labour. 

Broadly, AI and automation are already playing an important role in order processing, order fulfilment, inventory management, fraud prevention and more. 

How have Radial's own offerings changed amid the relatively recent rise of GenAI?  

Most retailers that have implemented AI for inventory, customer service, risk management/fraud protection, predictive analytics and customised shipping intend to enhance their AI for improved performance.

Radial, in partnership with Retail Dive, surveyed brands and consumers on what’s changing in retail in 2024. We found that 59% of retailers currently use AI for inventory management (e.g. forecasting demand, managing inventory levels, optimising the allocation of stock across different locations) and plan to enhance these initiatives. 

The same survey showed that AI in customer service – like chatbots and virtual assistants providing real-time support to customers, tracking shipments and answering questions – is a focus for 57% of brands who are currently using AI and plan to take that further.

Additionally, AI in custom shipping options (e.g. tailoring shipping solutions based on customer preferences) is currently used by 45% of retailers who also plan to enhance these capabilities. 

Radial is using AI to enhance its demand forecasting and labour management to ensure we meet our delivery promises. Our customer care business has implemented GenAI to enhance the effectiveness of our chatbots and virtual assistants.

Radial is also beginning an AI pilot to drive more employee engagement. The pilot will allow us to get real-time feedback to help us improve our associates’ experience in our fulfilment centres, enabling us to remain an employer of choice.  

Why is GenAI already so important in the supply chain and retail space?  

One of the reasons the supply chain and retail space are so primed for automation and GenAI is that retail generates massive amounts of data that contain important signals on customer preferences, intent and behaviour, as well as data on every connected touchpoint across the business.

However, this data is often siloed in various systems.By automating analytics and business intelligence, data can instantly be detected, sifted, sorted and analysed in real time for information that can serve data-backed decision-making and provide predictive analytics.

A powerful example of this automation is in forecasting labour. Here, customer data can be used to analyse demand fluctuations, which translates directly into the staffing needs for any given day. This not only eliminates situations where employees come in with no work to do, but also empowers them to plan their work schedules and finances. 

GenAI has also been important in opening avenues for precise personalisation, offering tailored product recommendations, related purchase suggestions and forecasts for replenishment based on actual usage. 

These personalised journeys foster deeper loyalty, keeping customers engaged and spending more. As retail leaders step into the future, they must understand that GenAI can be adopted safely now – even while we await the unknowns.  

What risks come with rolling out GenAI capabilities too quickly?  

As I mentioned previously, rolling out GenAI quickly often means the organisation hasn’t programmed the model specifically to the solution desired or confined it to operational areas. 

This is an inherent risk as GenAI has been trained on large language models, drawing on a much larger volume of data for its responses. This data is unknown in origin and unvetted to the retailer, which is why parameters must be established to outline how GenAI is to perform.   

We’ve seen that unprogrammed GenAI can act very much like a child who doesn’t have the adult context to know appropriate behaviour or the implications of their actions.This can cause models to share data without restraint. For example, throughout our testing, we found that GenAI bots – in their eagerness to help – would readily reveal data about customer identity and information that is restricted under privacy law. 

What considerations should retailers take before implementing GenAI?  

There are so many benefits and applications for GenAI within retail and the supply chain.

However, as retail leaders look to implement GenAI, they must evaluate the various forms of GenAI available, the level of human intervention needed, and how the technology will fit into their frameworks and complement the brand voice.   

When deciding whether to use open-source or closed-source GenAI, technical considerations such as data privacy and training requirements become even more crucial.

Each option has its own advantages and disadvantages. While open-source GenAI, like Meta’s LLaMA series of models, offers significant cost savings by eliminating development expenses, the open nature of the code can introduce potential privacy and security risks.

Conversely, closed-source GenAI requires substantial investment in development by the owning company. This controlled environment can be beneficial for security, but it's important to remember that closed-source doesn't guarantee complete security – it all depends on the company's development practices. 

Regardless of the choice, as the demographic of retail customers begins to skew towards younger customers, retailers should be investing to understand their customers’ expectations and begin piloting solutions to keep pace while keeping data privacy and security at the top of their minds.

They need to trace back ROI to their customers and seek to partner with internal teams or vendors to map out a strategy that benefits them.  

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