machine learning Archives

Navigating the Christmas Waste Conundrum with AI

Posted on 9 December, 202429 July, 2025 by Keith McKinley

Artificial intelligence is transforming how retailers prepare for Christmas, turning data into actionable insights for a sustainable celebration, writes Svante Gothe (pictured below) Head of Sustainability at Relex Solutions.

As retailers dive into the bustling Christmas season, a pressing challenge looms – spoilage and waste, particularly for fresh and short shelf-life products like Brussels sprouts. While it may be challenging to address spoilage and waste, there is a critical opportunity for retailers to apply Artificial Intelligence (AI) and Machine Learning (ML) to reduce waste and align with the increasing consumer demand for sustainability.

Retailers, grappling with inflation and economic uncertainty, are finding it increasingly difficult to accurately plan for the seasonal demand. The complexity is exacerbated during occasions like Christmas, where seasonal items such as sprouts have a finite shelf life and an even shorter consumer interest span.

According to the UK Environmental Agency, the UK generates 30% more waste around Christmas time, and this includes waste from Christmas food shopping and dinners. Infact, Business Waste estimates that 17 million Brussel sprouts go to waste each Christmas. However, there may be a shift as consumer consciousness rises — highlighting the potential for AI to help retailers predict the tipping point between consumer wastage and consumer consciousness.

Predictive power in supply chains

Beyond consumer habits, it is the retailers who are under pressure to curtail this waste at its origin — the supply chain. The integration of real-time analytics into the demand forecasting process can make businesses more agile in reacting to unexpected changes, making the system robust against sudden shifts in market dynamics.

The key to achieving this lies in harnessing the predictive prowess of AI and ML. By implementing AI-driven demand forecasting, retailers can capture the nuances of hundreds of demand drivers, translating complex consumer data into actionable insights. This means businesses have visibility into future demand, allowing for improved planning processes across merchandising, supply chain, and operations, ultimately leading to reduced waste.

The challenge, however, is not just in forecasting demand but also in ensuring that this information propels a collaborative effort across the entire supply chain. The decisions on how much to produce for items like Brussels sprouts happen months before Christmas, and so the coordination between retailers and producers is therefore vital. By sharing forecasts and planned orders well in advance, the entire supply network can adjust accordingly, reducing the risk of overproduction and subsequent waste.

Inventory planning

Another critical application of AI in this endeavour is inventory planning. By automating replenishment and allocation tasks in all nodes of the supply chain, AI ensures that the flow of goods is synchronised with real-time demand, reducing the chances of overstocking and the need for deep markdowns that often fail to clear excess inventory. Markdown and clearance optimisation also play a pivotal role in this sustainable orchestration. AI systems can dynamically adjust prices throughout the season, ensuring that products reach consumers before they lose their relevance, thus avoiding the post-Christmas slump that turns potential sales into waste.

Cutting waste for all retailers

The strength of an AI system in retail lies in its ability to be tailored to the specific needs of different business models and scales of operation, ensuring that every retailer, regardless of size, can reduce waste and improve sustainability. With Christmas spending expected to reach £88.3bn this year, and more people participating in the festivities, the opportunity to optimise the supply chain with AI is more significant than ever.

Simply put, the use of AI in retail planning is a strategic imperative in the fight against waste. As we move through the holiday season, the onus is on retailers to adopt these advanced tools and practices to optimise their supply chains and develop a more sustainable retail strategy. As retailers embrace this technology, we edge closer to a future where the holiday waste issue becomes a thing of the past, replaced by smart and data-driven approaches that balance consumer demand with environmental responsibility.

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Digital Twin Logistics Market Projected to Boom

Posted on 18 October, 202429 July, 2025 by Keith McKinley

The digital twin in logistics market is set to grow from its current market value of more than $1.2 Billion to over $9.4 Billion by 2032′ as reported in the latest study by Global Market Insights, Inc.
By creating a virtual replica of their physical logistics network, companies can monitor and analyze every facet of their operations, from warehouse management to route optimization, significantly boosting operational efficiency through real-time insights.

End-users are increasingly integrating digital twins with artificial intelligence (AI) and machine learning (ML) technologies. This fusion amplifies the predictive prowess of digital twins, leading to sharper forecasting and optimization. AI and ML algorithms sift through vast data from digital twins, discerning patterns and making instantaneous decisions. For example, in route optimization, AI-enhanced digital twins can modify delivery routes in real-time, factoring in traffic, weather, and historical data.

The market is segmented by component into software and services. In 2023, the software segment accounted for roughly $893 million. The capabilities of digital twin software have been significantly bolstered by the integration of Internet of Things (IoT) devices and sensors. These enhancements facilitate real-time data gathering from assets, vehicles, and infrastructure within the logistics network. Such detailed data is vital for crafting precise digital replicas of tangible systems. For instance, in March 2024, DHL harnessed digital twin technology to craft virtual models of its warehouses.

The market categorizes the digital twin in logistics by deployment model into cloud-based and on-premises. The cloud-based segment is projected to surpass $7.5 billion by 2032. These cloud solutions offer unparalleled scalability, allowing logistics firms to modulate computing resources in response to demand shifts. During peak times or unforeseen surges, businesses can swiftly upscale their infrastructure without hefty capital outlays. This adaptability not only ensures peak performance but also bolsters efficiency and customer satisfaction.

In 2023, North America led the digital twin in logistics market, capturing about 31% of the revenue share. Spearheaded by the U.S., this region stands at the vanguard of technological advancements. The swift evolution and adoption of IoT, AI, and big data analytics are pivotal in driving the uptake of digital twins in logistics. Companies in this region harness these technologies to boost operational efficiency, refine decision-making, and secure a competitive edge.

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AI Demand Forecasting Works

Posted on 9 July, 202431 July, 2025 by David Priestman

Despite the widely reported benefits of AI, particularly adaptive AI, some businesses may still be hesitant to adopt AI or machine learning (ML) technologies due to perceived concerns, such as fear of job loss, privacy concerns, and uncertainty about the reliability of AI predictions, write Dr. Nicholas Wegman, Senior Director – AI Scientist, and Alex Barnes, Senior Director of Product Management, Zebra Technologies.

One common fear heard among employees is that AI will replace jobs. However, research shows that while AI may automate certain tasks, it is unlikely to replace entire jobs, especially when it comes to supply chain planning, inventory management and more.

According to a study by the World Economic Forum (WEF), while AI is expected to displace some jobs, it is also expected to create new jobs and transform existing ones. The WEF’s Future of Jobs report states that by 2025, AI and automation will lead to a net increase of 12 million jobs globally.

Rather than replacing humans, AI is expected to augment human capabilities and improve productivity, allowing employees to focus on higher-level tasks that require creativity and critical thinking which are absolutely required when actioning the demand forecasts and inventory plans.

Business leaders can assuage these fears by providing employees with the necessary training and support to effectively integrate AI into their workflows. By involving employees in the AI adoption process and demonstrating the benefits of these technologies, businesses can help employees feel more comfortable with AI and view it as a tool that can enhance their work rather than a threat to their job security.

Another common concern with AI adoption is privacy. As AI systems analyse vast amounts of data, businesses must ensure that they are protecting customer and employee privacy – as they would with the use of any other technology. This may involve developing strong data security policies and protocols and obtaining the necessary consent from customers and employees for data collection and use – which, again, shouldn’t be much different than what they’re already doing today.

Finally, we know businesses may be hesitant to adopt AI due to uncertainty about the reliability of AI predictions. However, as noted earlier, AI has been shown to provide more accurate predictions than traditional methods, particularly when analysing large datasets. By carefully selecting AI models and continuously monitoring their performance, businesses can ensure the accuracy and reliability of their AI predictions.

To illustrate the benefits of AI in retail and consumer packaged goods (CPG) companies, let’s look at a few real-world examples of how retailers and CPG companies are already leveraging AI for stronger business outcomes.

PacSun, a leading retailer of lifestyle clothing, used AI-powered demand forecasting for allocation and fulfillment, to improve inventory accuracy and reduce stockouts. The system helped the company double its ship completes, forecast and allocate omnichannel demand, and balance inventory between stores, distribution centre (DC), and web-depot locations for in-store and online sales.

In another example, Bimbo Bakeries worked with an AI demand forecasting team to collaboratively tailor an AI-powered demand forecasting and predictive ordering platform to support different front-line workers via custom user interfaces (UIs). Everyone from operations managers to DSD drivers can now open their respective UI to right-size production and localised delivery plans down to a SKU/store/week level factoring seasonality, local events, promotions, and other outside influences that may not be considered with human-led demand forecasting and inventory planning models.

While so many CPG companies – and competing bakery companies – struggled with supply chains and logistics for months on end during the pandemic, the Bimbo team was able to adapt its forecasting and production in less than a month to meet the heightened demand for their baked goods as more consumers started to eat at home amid restaurant closures. In just a few weeks, AI enabled them to right-size their production volumes, adjust delivery routes to avoid out-of-stocks, and properly staff production lines, loading docks and trucks to meet the skyrocketing demand.

Another consumer packaged goods company was required to react and adjust more quickly to inventory planning and order fulfillment to counteract skyrocketing consumer demand for food and consumable products during the pandemic. To optimise business performance in those market conditions, this multi-billion-dollar global company launched a strategy focusing on a competitive advantage by investing in data and analytics. One critical area that promised significant business benefits was order processing and available-to-promise (ATP). The system helped them achieve a 4-5% improvement in case fill rate for strategic customers, and 10x return on investment (ROI) from increased revenue and reduced on-time in-full (OTIF) penalties.

Ready to Improve Margins?

AI offers the potential to revolutionise the retail and CPG industries by optimising demand forecasting, inventory planning, and pricing and promotions. However, businesses must ensure they have the right integration, adoption, and execution strategies in place to fully realise the benefits of AI. By addressing perceived concerns, involving employees in the adoption process, and highlighting various successes, they can overcome barriers and unlock the full potential of AI like Bimbo and PacSun have to optimise margins and gain a competitive edge in the market.

Supply Chain 2024 Predictions