Forecasting is the science of predicting the future using past and present data. It is a technique based on statistical and machine learning algorithms that learns the patterns and relations from the data. How good a forecast will be depends on both the quality of the data and algorithms used. 

Forecasting: The backbone of retailer and consumer goods companies
But why does this matter for the retailers and manufacturers? For them, forecasting is the linchpin that holds together the complex machinery of supply and demand. It’s a meticulous orchestration of numbers and predictions, where a single misstep can lead to cascading effects – from stockpiles of unsold goods to the echo of empty shelves.

Retailers and consumer packaged goods (CPG) manufacturers don’t just care about forecasting; they live by it. It dictates their strategies, informs their decisions, and ultimately shapes their success in a landscape where a day’s delay, a misjudged trend, or an unanticipated demand can mean the difference between soaring profits and staggering losses.  

Forecasting in retail CPG is not a siloed function; it’s a collaborative effort involving multiple stakeholders. From the procurement team assessing raw material needs to the marketing department planning promotional campaigns, accurate forecasts ensure that every department’s efforts are synchronised and strategically aligned. This alignment is crucial in an era where consumers expect personalised experiences and rapid fulfilment.

Demand Planning: Accurate forecasting and effective supply chain management
Demand planning—the process of forecasting future customer demand—hinges on accurate forecasting serving as its foundation. At its core, forecasting is the key ingredient in demand planning, acting as the predictive mechanism that feeds into the broader demand planning process.

It is through forecasting that demand planners can anticipate future customer demand. This process enables businesses to meet customer needs more effectively but also allows them to optimise their inventory levels, reduce costs, and increase profitability. Additionally, accurate demand forecasting contributes to sustainability by preventing overproduction, which reduces waste and conserves resources.

The demand planning process starts with the collection and analysis of historical data, which includes historical sales, promotion data, event data, and any other relevant information that can impact demand.  

This data is then fed into sophisticated forecasting models which often utilise advanced statistical methods and machine learning, a type of artificial intelligence. These models are designed to identify patterns and correlations in the data to predict future demand with a high degree of accuracy. The forecasted demand then forms the basis for various strategic and operational decisions, such as production scheduling, inventory management, and procurement planning.  

Challenges of accurate forecasting in retail and CPG
Accurate forecasting in the retail and CPG sectors presents unique challenges that stem from the dynamic nature of consumer behaviour and market trends.

One of the primary hurdles is ever-shifting consumer preferences, which can change rapidly due to various factors such as emerging trends, socio-economic shifts, and global events. This volatility makes it difficult for retailers and CPG companies to predict future demand with precision, as past data might not always be a reliable indicator of future patterns. Advanced analytical and AI techniques can be used to tackle these challenges.

Another significant challenge is the complexity of managing a vast array of products, each with its own lifecycle and demand curve. Retailers and CPG companies often handle thousands of stock-keeping units (SKUs) and forecasting for each one requires a deep understanding of its unique demand pattern. Seasonality adds another layer of complexity, with certain products seeing spikes in demand during specific times of the year. This necessitates a highly nuanced approach to forecasting, which can be resource-intensive and difficult to scale. Advanced AI algorithms capable of scaling large volumes of data by using graphics processing units (GPUs) can be used to address this challenge. 

The increasing expectation for personalised shopping experiences and rapid fulfillment has raised the stakes for accurate forecasting. Consumers today expect products to be available when and where they want them, leading to the need for more localised and granular forecasting. This requires a sophisticated and world-class data infrastructure capable of processing and analysing large volumes of data at a granular level. It may take several months and engineering skills to setup a robust and reliable ‘home grown’ internal data infrastructure to enable machine learning forecasting. 

These challenges are compounded by external factors such as supply chain disruptions, changes in regulatory environments, and competition. The need for agility and flexibility in forecasting processes is paramount, but achieving this while maintaining accuracy and efficiency is a complex endeavor that remains a central challenge in the Retail and CPG sectors. 

Read more about AI-driven demand forecasting here.

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Apptronik’s general purpose industrial humanoid robot – Apollo – stands 5’8” tall , can carry 55 pounds and operates on swappable batteries that are configured to give it the highest operational time of any humanoid robot.

Apollo is powered by linear actuators that mimic the mechanics of human muscles and provide a full range of mobility. Its unique force control architecture and flexible safety zone perimeter allow Apollo to work safely around and directly with people.

Together, the companies are evaluating the overall performance of the robot in a lab setting to further fine-tune Apptronik’s AI model before deploying the technology to a US distribution centre once ready.  

“We’re excited to partner with Apptronik to develop their AI-enabled humanoid robot,” said Adrian Stoch, chief automation officer, GXO.

“Apollo has great potential to add value throughout the distribution center, including the most labor-intensive operational processes. These kinds of robotics reduce repetitive work and improve safety while freeing associates to focus on higher-value-added activities. As we progress on our R&D journey with Apptronik, we’ll also be evaluating its capability for other critical use cases along the way.”

Discover more about warehouse automation, and digitisation, in the DeliveryX Warehousing 2024 report. Pre-register for it now, and get it straight to your inbox on release.

This report also includes three retailer interviews, with B&Q telling DeliveryX how it is utilising its store network to provide its customers with choice – not simply products but also how and when they are delivered. The report hears from Pets at Home on how its new distribution centre fits into its next decade of growth. The founders of Furniturebox explain why its facility is key to an 8pm next-day delivery cut off.

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The new store will allow fans to tap their card or mobile wallet, grab their chosen drinks and snacks, and get them back to their seats so they don’t miss a moment of the game or show. To enable secure payment processing at The O2, the collaboration also worked with global financial technology platform Adyen.

According to the partnership, the Just Walk Out offering not only increases throughput but can also help extend opening hours and reduce theft. Furthermore, it can assist with staff optimisation. For example, The Market Express at ExCel London, which has 400 events and more than four million visitors a year, is now able to serve 300% more customers on their busiest days and has increased annual revenue by 56%.

As The O2 becomes one of more than a dozen third-party locations in the UK to offer Amazon’s Just Walk Out technology this year, the retailer is looking to bring this tech to customers in places such as sports stadiums, entertainment venues, airports, hospitals, universities, and more.

The O2 adds to a growing list of prominent UK venues that have opened stores powered by Just Walk Out technology, including The SSE Arena, Belfast. There are stores in corporate headquarters like Bentley Motors and Santander Bank, as well as Amazon’s own headquarters in London.

The technology has also been rolled out at Telford Hospital and Sussex University, and recently at a store at Gridserve’s EV Station at Gatwick Airport, with more Gridserve stores coming later this year. By the end of 2024, Amazon expect to be in more than a dozen third-party stores in the UK, as well as in all 20 UK Amazon Fresh stores.

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The technology, from MRI Software, will also provide visibility relative to the broader UK market.

“Accurate footfall analytics will help us to understand and optimise store performance in each region and country as we continue to use analytics and technology to enhance our consumers’ experiences in innovative ways,” a Holland & Barrett spokesperson said.

This latest technology investment follows Holland & Barrett’s acquisition of fitness technology startup Avie last year, as it looked to expand its offering beyond retail. The health retailer planned to use Avie’s technology to provide customers with customised wellness solutions.

“We want to become a trusted partner to help people achieve their health and wellness goals – and to do that we want to offer simple ways to test, track and improve your health, built on our 150 years’ experience in health and nutrition,” Holland & Barrett chief business and science officer Tamar Rajah said at the time.

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The courier believes this strategic investment could even see couriers testing the use of augmented reality headsets in the future.

Evri’s AI strategy focuses on three areas. First, it will work to improve customer service as AI will simplify, speed up and improve the consistency of its parcel delivery services by automating simple data collection and identification of issues. This will in turn free up Evri’s customer service team to focus on resolving more complex issues, leading to faster resolutions and a consistently higher quality of service.

Second, it will enhance parcel security through Evri implementing AI-powered tools. Using AI Evri will be able to more quickly identify, on the doorstep, if a parcel is being left in a highly safe place – as the tech will analyse data against previous delivery photos.

The enhancements will also analyse claims data, and automatically check geo-location and delivery photos to identify potentially false claims. This builds on Evri’s existing ParcelVision technology, which checks every photo couriers take, in real time, to ensure the quality of delivery photos.

Finally, Evri will use AI to optimise workforce planning by analysing data and predicting future demands. This will ensure Evri will have the right people in the right place at the right time, improving efficiency and delivery success rates.

Working in collaboration with AI consultancy Robiquity, Evri has created a ‘AI Centre of Excellence’ team.

Marcus Hunter, chief technology officer at Evri, said: “This strategy will empower our people and optimise our operations, ultimately enabling us to deliver a best-in-class experience for everyone we serve. It demonstrates that Evri is a tech-forward business and a really exciting place for new talent to work – we want to be the number one parcel delivery company globally.”

“We put a real focus on intelligent automation in the middle of 2023, and we have been supported by Robiquity for the start of this exciting journey. The team at Robiquity have been crucial in building the foundations – allowing us to move at pace with implementing the early stages of our strategy. The significant benefits achieved through Intelligent Process Automation in the first six months have given us the platform to explore other AI, with a view to implementing later technologies to help shape the future of our business.”

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HelloDone’s conversational AI solution answers post-purchase customer queries on digital messaging channels, including WhatsApp, Instagram and Facebook Messenger. This enables retailers to automate responses to customer queries via AI-powered chat, offering effective and immediate resolutions of customer needs at scale to deliver enhanced delivery experiences and customer service.

The acquisition will see the HelloDone team join Scurri, adding rich, specialist AI expertise to Scurri’s existing product and engineering functions to accelerate product innovation. Scurri will also acquire all technology, trademarks, intellectual property and clients. This acquisition is being achieved via a restructuring by transfer arrangement.

“Scurri’s acquisition of HelloDone demonstrates our commitment and ambition to accelerate growth for our customers by empowering them to be at the forefront of the AI revolution that is rapidly changing the retail industry,” said Rory O’Connor, founder & CEO of Scurri.

“HelloDone’s technology offering is highly complementary to ours and will enable us to unleash new capabilities and give our customers even greater control over the most fundamental pillars of their business – delivery.”

“HelloDone’s services are highly synergistic with Scurri’s and we are excited by this opportunity to blend our collective knowledge, expertise and technology to provide a distinct competitive advantages as a fully integrated solution-driven delivery and post-purchase service provider,” commented Ed Hodges, founder & CEO at HelloDone.

Learn more about conversational AI in an exclusive feature in the newly published DigitalX 2024 report. The feature looks at what is AI good at within retail and conversely, which areas still need the human touch.

The full report through a mix of analysis, expert comment and case studies, illuminate key themes including how do retailers and brands best build customer engagement and loyalty? And how do retailers and brands put digital at the heart of marketing initiatives?

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Its results for the year ending 29 December 2023, Gousto has also reported ‘robust’ revenue, increasing 1% year-on-year to £308mn. The recipe box operator credited its use of technology and AI, combined with a continued focus on cost discipline despite inflationary pressures, for these margins.

Timo Boldt, CEO and founder of Gousto, commented: “We have made good progress in 2023, improving our customer proposition with more recipe choice for more households, whilst also beating our financial expectations.

“But we are still early in the journey and as a pioneer of recipe kits, we will continue to leverage AI, automation and our technology capability to lead innovation in the category as we extend our reach deeper into the mainstream market.” 

Gousto’s production facility at Warrington, which opened in 2022, is powered by three in-house developed algorithms – auto replenish, auto routing and pick face optimisation. Warrington’s maximum pack speed is 140% faster than its first generation facilities with a pick accuracy of 99.97%.

The high level of pick accuracy elevates the customer experience whilst also improving the bottom line through a material reduction in customer refunds, explained Gousto. At the same time, its operational food waste is limited to less than 1%. 

Learn how Boldt saw a gap in the market for healthy, convenient and sustainable home cooking in an exclusive company profile in the newly published SubscriptionX 2024 Report. The case study also looks at Gousto’s reaction to there being no off-the-shelf subscription technology and the team’s work to build its own.

The report also looks at real-world examples through in-depth case studies of Hotel Chocolat, Graze, Cocoon and Netflix.

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The buzz around this reached a fever pitch in 2023 and this year, we’ve seen shippers, carriers and LSPs transition from the thrill of its potential to the pragmatism of implementation. This requires a robust AI strategy with guardrails around data bias, transparency, compliance, ethics and more, notes Jonah McIntire, chief platform officer at Transporeon.

 Where can generative AI bring value?
AI’s impact on logistics and transportation won’t be a sudden revolution but a steady evolution. As a starting point, it can help to think of AI as additional ‘talent’. A good way to map out potential implementations is to identify which teams are being held back because of a lack of knowledge or high labour costs and use AI to help solve this problem.

So far, AI implementations in the logistics industry have focused on process automation and data-driven prediction. These generally don’t involve newer generative AI models, but symbolic AI (which focuses on explicit knowledge representation and logical reasoning) and statistical AI (which uses data patterns and probabilities to learn and make predictions).

Classic procurement tools, many of which predate the current generative AI boom, are a great example. In particular, spot buying is an excellent AI use case. Why? Firstly, it involves set budgets and carrier/lead time requirements, so the algorithm has explicit constraints within which to work. Secondly, negotiations follow a simple structure – participants can make an offer, wait for a response, make a counteroffer, accept an offer, or end a negotiation. AI can also help LSPs on the other side of spot procurement, allowing them to automatically offer instant, accurate quotes based on predicted market rates. This automation means they can quote for – and win – more opportunities.

Another AI use case is real-time ETA prediction. A decade ago, accurately predicting arrival times for loads seemed impossible, as strikes, traffic jams and adverse weather appeared utterly random to the human eye. However, today, AI models can uncover hidden patterns by analysing years of data. This enables shippers and carriers to accurately predict ETAs, ensuring they’re always prepared to receive loads.

Looking towards future AI implementations: The best UI is no UI
So, where does generative AI fit into this picture? Though it will undoubtedly unlock efficiencies and new capabilities across the logistics value chain, many changes will only be obvious within organisations. The transformation of logistics software user interfaces (UI) is an obvious exception. 

Most software currently uses a graphical user experience. Users engage by moving their mouse to an object, clicking it, and keying in data or re-clicking. This means users must ‘train’ themselves on each platform’s symbolism (such as having navigation at the top versus on the side) and terminology (one platform might say ‘orders’ and another ‘shipments’). We forget that this form of interaction is ‘unnatural’ because we all grew up doing it! However, it often creates friction between intention and action from users, who must constantly micro-plan how to get what they want from systems. 

Generative AI promises to change this by working in natural language with zero friction. Currently, most generative AI models use text boxes, but there’s no reason for progress to stop there. Humans talk faster than they write, so voice input could be a natural evolution, creating a perpetually available assistant who can ‘do’ any action within a specific platform. This would unlock myriad possibilities. For instance, a freight forwarder might verbally describe a complex shipment scenario and have the software generate the necessary forms.

What’s next?
One thing is certain – AI is poised to drive a new era of intelligent transportation. However, shippers, carriers, and LSPs can implement this technology in multiple ways. Some firms are choosing to invest in building their own AI tools in-house, which is demanding from a resource perspective. 

On the other hand, many shippers, carriers and LSPs are opting to implement the best generative AI software from external providers. This strategy relies on the expertise of specialised firms and offers a faster time to implementation. It’s also worth noting that external providers (neutral third parties) typically work with multiple industry players. This collaborative, ‘platform’ approach means they can access a broader pool of data to train AI models, accelerating progress towards improving the industry’s safety, efficiency and service levels. 

Whichever approach companies take, the time is now to embrace AI strategically in a rapidly changing landscape.

Jonah McIntire, chief platform officer at Transporeon, A Trimble Company

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The launch of ChatGPT in November 2022 changed the public perception of artificial intelligence (AI) almost overnight. Discussion of technology hitherto associated with sci-fi stories, and Skynet’s declaration of war against humanity in The Terminator was mentioned often, was suddenly everywhere. 

In 2024, it’s a measure of how high awareness of AI has grown that a recent survey by MSi-ACI found that 70% of 1,000 UK consumers were aware of how generative AI is used to promote goods and services online.Some 45% of respondents thought the technology had influenced their purchasing choices, with Londoners (59%) most likely to think so. Less positively for a retail sector intent on using AI, the survey found concerns over information being less reliable. Half of over-55s surveyed worried about not being able to trust online sources due to AI-generated fake reviews, although that figure dropped to 28% of 18 to 34-year-olds. 

The survey is, of course, measuring attitudes rather than the actual degree to which AI is actually being used within ecommerce, but nevertheless its results lead to other questions: If that’s what consumers think? Are they right to think so? Is AI really that influential in our purchase decisions? 

To which an accurate answer is yes, probably and sometimes, although that’s not uniformly true and, anyway, some retailers, brands and sectors are using AI more effectively than others. More seriously, long before the public realised that AI was going to become important, retailers and brands were already using the technology within such facets of the business as personalisation and communications via chatbots. However, implementation has been, to adapt the famous William Gibson adage about the future, unevenly distributed. 

A further complication is that it is difficult to imagine any retailer or brand downplaying their use of AI or documenting or detailing their problems with implementing the technology. Nevertheless, even accepting that it can be difficult to glean exactly what’s happening on the ground, it’s probably safe to suggest bigger retailers and brands have long been employing AI and that there’s a huge demand among smaller retailers to use the technology.

In this context, it’s revealing that, when Amazon recently released its first quarter report for 2024[1], CEO Andy Jassy attributed a jump in revenue at its cloud computing division, Amazon Web Services (AWS), to its focus on AI. Revenues at AWS for Q1 were $25bn, a year-on-year increase of 17%. It’s reasonable to suppose that many of the businesses employing AI, whether accessed via AWS or different stripes of competitors, do so within marketing and personalisation. That’s not to say these are the only uses – retailers such as M&S have been trialling machines that spot gaps on shelves – but they are areas of digital retail where AI seems to be a natural fit. 

The data conundrum
As to why that’s the case, it’s in part because personalisation relies on data. However, there’s a complication here. In so many respects, such data has become far more difficult to manage over recent years. Firstly, there are regulatory factors to consider. Even if the EU’s General Data Protection Regulation (GDPR) legislation didn’t cause the ecommerce bonfire that some envisioned as a worst-case scenario, it has created a world where businesses need to be far more careful about how they treat personal data. 

This has happened at the same time as many consumers have become more careful about how they share data. One emerging theme in consumer behaviour is to use a secondary email address in dealings with all but the most trusted businesses. Or to use a service such as Hide My Email and iCloud+ to avoid being identified at all. 

At the same time, the business imperative to use data and the insights it yields have rarely been stronger. Paradoxically, this need is being driven in part by consumers who have grown to expect personalised services and who will be critical if, for example, they are offered products they’re not interested in or, worse, seem downright inappropriate. Effectively, this means retailers and brands are caught in a kind of pincer movement between a reluctance to share information and increased customer expectations. 

The second factor that plays in here is the need to deal with different kinds of data. In order to craft effective marketing campaigns and to personalise offerings, retailers and brands don’t just need data about specific customers, an address and so on, they need to understand, for example, what kinds of products different segments of the customer base are likely to buy if nudged. 

This is before we have begun to talk about the data sets around product descriptions and around where those products are located, supply chain data and so on. Or, more complex still, starting to make the most of contextual data, so that a consumer idly browsing on their phone at 8pm is treated very differently to a consumer standing at the checkout of a bricks-and-mortar store. 

The sheer number of variables goes some way to explaining why it can be such a challenge using AI within digital services. While the problem of data silos producing inconsistencies that cascade through the business is nowhere near as prevalent as it was a decade ago, that issue still exists. If a company cannot even trust its own data, how can it hope that setting AI to work on this data will produce coherent results and yield actionable insights? 

That said, as our case studies in this report show, retailers and brands are grappling with the issues here. One of the points that came through doing background interviews for the report is that it’s no good thinking of AI as a quick fix. Rather, you need to work with teams and show them how using AI can make them more effective. Businesses need an AI strategy that includes people in the equation. 

Take the relatively straightforward example of copywriting. If you can automate product descriptions so that 80% of the work is done by AI, the copywriter can better focus on those areas where refining messages needs the human touch by putting the zing in the sell, adding excitement and expectation and refining key messages. 

Or, to look at that another way, providing a company’s data is robust and its use of both that data and technology is underpinned by a clear strategy, AI can help human beings better personalise marketing. A decade on from now, we would guess, using AI in these kinds of ways within ecommerce will seem routine – just a part of how the business works. 

This feature, authored by Jonathan Wright, originally appeared in the Digital Marketing Evolution 2024 report, which was launched at the DigitalX conference – part of the RetailX Event’s Spring Festival.

Inside the report we discuss how marketers need to deal with the challenges here at the same time as grappling with advances in fields such as deep tech, AI, data science, social trends, platforms and behavioural analysis.

Case Studies includes Shein, Ugg, Deckers Brands, John Lewis, Sweatty Betty, Nike, HP, Sainsburys, L’Oreal, Pepsico and Kingfisher.

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The supermarket has announced a new five-year strategic partnership with Microsoft to harness its AI and machine learning tools.

Sainsbury’s said it will utilise Microsoft’s services to transform operations across three areas:

• Enhanced customer experiences: Sainsbury’s will use generative AI to create a more interactive online shopping experience and improve customers’ search experience, making shopping more efficient and engaging.
  
• Empowered store colleagues: Store colleagues will have real-time data and insights for key processes, like smarter shelf replenishment processes. Using AI to pull together multiple data inputs, such as shelf edge cameras, colleagues will be guided to the shelves that need replenishing; saving valuable time as well as ensuring sales opportunities aren’t missed. Sainsbury’s store colleagues will have more time to focus on the things that matter to customers, with access to AI-guided support to address customer and colleague queries effectively in the moment, improving efficiency and enabling the delivery of leading service for Sainsbury’s customers.
  
• Data powering the Sainsbury’s business, built on smart cloud foundations: Sainsbury’s data assets combined with Microsoft 365 collaboration tools, generative AI and machine learning capabilities will enable Sainsbury’s to continue driving returns through its Save and invest to win programme: transforming operations, driving better decision making and running a more efficient business. This will be delivered through Microsoft Azure as part of Sainsbury’s cloud ecosystem, reducing time to market for new services and innovations.

Clodagh Moriarty, Sainsbury’s chief retail and technology officer, said: “Our collaboration with Microsoft will accelerate our ambition to become the UK’s leading AI-enabled grocer.

“It’s one of the key ways we’re investing in transforming our capabilities over the next three years, enabling us to take another big leap forward in efficiency and productivity, continue to provide leading customer service and deliver returns for our shareholders.”

Earlier this month, Currys announced that it has chosen Microsoft, alongside Accenture, to deliver its core cloud technology infrastructure.

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