May 17, 2020

How Digitalization – through automation and AI – is transforming demand planning

Jörg Junghanns
5 min
The process of demand planning is undergoing enormous transformation. While it has historically been a reactive process involving responding to changing...

The process of demand planning is undergoing enormous transformation. While it has historically been a reactive process involving responding to changing market conditions, the advent of technology is allowing – and at the same time forcing – demand planning to become much more strategic. Digitalising demand planning is becoming imperative for organizations that want to stay ahead of competitors, impress customers and drive company profits. Demand planning is no longer a case of simply reacting - instead, it requires continuous proactivity to successfully predict demand. In line with this, artificial intelligence (AI) is becoming an intrinsic part of the demand planning function, further boosting planning accuracy through sensing the markets’ desires.

A recent Capgemini report found that, when it comes to supply chain digitalization, organizations work on too many projects simultaneously, with close to 30 projects at pre-deployment stages. This high volume inevitably leads to some initiatives failing to take off, and places the most critical projects at risk. The digitalization of demand planning - and subsequent implementation of AI - is one example of a critical initiative which businesses must prioritize, and that has tangible and quick benefits, including:

Strategic decision-making

AI drives automation of the more traditional and labour-intensive tasks within demand planning to the next level – most notably, analyzing and interpreting batches of data. Not only is AI able to do this more accurately and quickly, but – by automating these critical but complex tasks – the team’s time is freed up so that they can focus on more strategic business endeavours.

Additionally, demand planners no longer need to dedicate large amounts of time to creating short-term demand plans or triggering stock replenishment – AI can do this for them. The team can then concentrate on progressing higher-value business objectives that will have a greater impact on the organization. Demand planners will need to interpret their role more strategically, e.g. dedicate more time to investigate how to improve operational efficiency, identify new ways to increase profits and become more involved in the business as a whole.

Improved forecasting

With so much data readily available, it has become more difficult to detect customer purchasing patterns. Artificial intelligence can work to cut through this noise, processing the data to uncover subtle patterns that humans would have missed. By aggregating datasets from Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and Internet of Things (IoT) systems – and combining this with external variables and contextual data such as a calendar of events, seasonality and the weather – AI works to provide more accurate demand planning forecasts.

If this holistic approach is taken, AI forecasts can then be linked through supply and inventory planning to automate replenishment triggers, so that organizations consistently have the correct amount of products in stock. This results in increased sales by improving order fill rates and shelf availability.


For example, a global organization for personal care products built a demand-driven supply chain using data analytics to increase visibility into real-time demand trends. This enabled the company to produce and store the exact amount of inventory required to replace what consumers actually purchased, instead of manufacturing based on forecasts from historical data. The company also utilized point-of-sales (POS) data from retailers such as Walmart to generate forecasts that triggered shipments to stores and informed internal deployment decisions and tactical planning.

This approach helped the company to effectively track stock keeping units and shipping locations. As a result, it saw up to a 35% reduction in forecast errors for a one-week planning horizon and 20% for a two-week horizon.

More responsive   

Supply chain channels are undoubtedly vulnerable to a variety of external factors – for example natural disasters or availability of raw materials– that can impact demand forecasting. Rather than relying on historical data, AI and machine learning tools use real-time calculations to respond to and find resolutions for supply chain disruptions. As well as this, automation allows for rapid responses to changing consumer demand, improving sales and profits, and boosting consumer loyalty. This added responsivity boosts the accuracy of demand planning and limits monetary losses.

An office products retailer, for example, had disparate systems working autonomously with different SKUs, forecasting and planning processes. Management recognized that, without a “synchronized view of demand” of its supply chain, the company could not respond rapidly enough to market changes. Capgemini and a software solutions provider were brought in to implement an innovative solution designed to empower the retailer with synchronized decision-making and, ultimately, a unique competitive advantage. The solution is allowing the company to proactively meet fluctuations by tightly integrating a range of core business processes, starting at merchandise planning through to the replenishment process. The company expects this to increase top-line revenue by delivering real strategic value and strong demand chain results.

As with any significant organizational change, an agile approach – involving small steps, small failures, and fast recovery – can deliver the quick results that clearly demonstrate the value of cutting-edge demand planning approaches, such as the implementation of AI.

With this in mind, A proof of concept approach (POC) is highly recommended. This allows enterprises to gain a better understanding of the costs and returns of automation, as well as understand the skills and alterations that will be needed to accommodate it. Ultimately, the sooner an organization begins to adjust the way it goes about demand planning, the sooner the benefits will become apparent.


By Jörg Junghanns, Head of Europe – Digital Supply Chain for Business Services at Capgemini

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Jun 9, 2021

Upgrading RFID and Automated Track and Trace Solutions

Elise Leise
5 min
Why do decades-old tech like RFID remain relevant despite digital disruption - and which recent innovations can accelerate traceability and SCM?

During the COVID-19 pandemic, global supply chains faced the challenge of rapidly adjusting their business priorities to new customer preferences. Local supplier backlogs, winter storms, and the Suez Canal backup in March underscored the need for efficiency and visibility across the supply chain. 

According to Christof Backhaus, Digital Lead Product Supply and Smart Label Project Lead at Bayer, companies must now place critical importance on tracking and tracing their products. “All large enterprises in the world dealing with finished goods,” he said, “seek functional and technical solutions to real-time channel inventory.” 

Indeed, RFID’s real-time tracking data allows executives to make quick, well-informed decisions in moments of supply chain crisis - and rather than unfolding across days or weeks, it only takes a matter of minutes. 

Why does RFID remain relevant despite digital disruption? 


Essentially, RFID uses radio frequency waves to transfer data wirelessly between a scanner and a tag. In contrast to barcode technology, which requires a stationary scanner, RFID tags can be pinged from anywhere in the world, allowing companies to track real-time movement through the supply chain. RFID tags can also scan unique SKU numbers and distinguish between varying product sizes, colours, and styles: a critical feature for increasingly personalised end-user products. 


Though the first patent for RFID tags appeared in 1973, higher accuracy rates, lower costs, and advances in sensor and data technology have made it newly accessible to a wide range of companies. Today, the technology is used in logistics networks, manufacturing and delivery networks in the pharmaceutical industry, and any business where efficiently tracking and monitoring product location is critical: raw materials, consumer products, cars, electronics, retail, and agriculture. 

What are the key benefits? 


Overall, automated track and trace solutions keep labour costs low, optimise operating costs, mitigate security risks, use capital effectively, and assist companies in adhering to regulatory requirements. 

Below are three in-depth dives into how RFID benefits major industries: 


  • Pharmaceuticals: RFID tags help manufacturers safeguard sensitive products such as vaccines, tracking where they are and when they will arrive in real-time. Sensors closely monitor temperatures to ensure regulatory compliance. If anyone tampers with a shipment, the sensors alert the company. 
  • Logistics: RFID identifies process gaps and frequent anomalies by monitoring a product’s lifecycle from shipment to delivery. This data helps decision-makers predict the most efficient routes and therefore optimise their distribution schedules. 
  • Retail: Sensors help guard shipments against theft and provide critical intelligence when shipments go missing. Before adopting RFID technology in 2203, UK retailer Marks and Spencer relied on barcodes to scan inventory. When they made the switch, their productivity increased from a maximum of 400-600 items scanned per hour to up to 15,000 items scanned per hour. Building on their initial success, the retailer expanded the use of the technology and is still using it today. 

Regardless of the industry, RFID promotes accuracy, immediacy, and efficiency. Companies reduce human error by automatically scanning products, keep track of inventory even in geographic locations with poor connectivity, and help streamline warehouse operations by identifying exact product locations. 

Which recent innovations have changed the game? 


With recent developments in cloud technology and IoT, a multitude of cloud-based alternatives have emerged to challenge traditional RFID technology. One of these cutting-edge solutions is Sony’s Smart Label - an intelligent shipping label that runs on AT&T’s global cellular network. 

As with any good innovation, Sony’s proprietary technology started with a customer need ready to be solved: the Bayer Crop Science Division lacked an international IoT solution that could track seed products from start to finish throughout its distribution channel. Millions of dollars of revenue stood at stake, so Bayer turned to Sony to develop a smart label that would set the organisation up to manage its supply chain with end-to-end visibility. 

Sony’s printable and disposable adhesive label allows companies to track the condition and location of their products worldwide and act upon the vast amounts of data it collects. The process is simple: the label activates when attached to the package, connects to AT&T’s secure LTE-M network, and sends data to the Smart Label Cloud in real time. 

In sharp contrast to other smart label solutions that place trust in a patchwork combination of Wi-Fi, radio-frequency identification, and other limited coverage connections, the Sony Smart Label connects solely through a secure and universally-available cellular network. “Working with Sony,” says Robert Boyanovsky, the vice president of Mobility, IoT and 5G at AT&T, “we provide full visibility of every item shipped.” 

Most importantly for companies on the edge, the Smart Label integrates with existing enterprise systems to achieve full visibility, thus adding value without disrupting supply chain process flow. 

Why is this important now? 


Companies that previously delayed introducing RFID and other automated track-and-trace technologies can capitalise on recent developments that lower costs, improve accuracy, and supercharge traceability. 

Clearly the technology has value in today’s uncertain global marketplace, and can help decrease the costs of tracking goods. To quote Christof Backhaus, the Project Lead at Bayer, “the Smart Label indicates how much product is in the market, from the packaging line to the end customer.” Companies no longer have to spend a small fortune to take advantage of recent IoT developments. “Due to the technical composition [of the label],” Backhaus explains, “we don’t require additional infrastructure, manual scanning, or other expensive tools.” 

Over the decades since RFID was first introduced, support for introducing it to company supply chains has also improved. AT&T’s IoT Professional Services Organisation, for example, supports companies through the end-to-end design and integration process--from installation to deployment and project management. 

Companies that invest in traceable and visible supply chain solutions stand the best chance of survival, adjusting in real-time to natural disasters, shipping backups, and slowed-down supplier turnarounds as a result of the global pandemic. “Smart Label promises to help businesses like Bayer realise the full potential of the IoT,” says AT&T’s Boyanovsky. “[We can] deliver improvements in revenue and cost savings and make supply chains more efficient.” 

Certainly, company executives will be hard-pressed to ignore recent innovations. In an age of uncertainty, RFID and its challengers herald a welcome sense of supply chain security. The next step? “Our sales team,” Boyanovsky adds, “is prepared to engage with prospective customers now.” 


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