Technology within supply chains is taking on an increasingly important role. Supply chains must perform in an environment of previously unmatched complexity and uncertainty. Therefore, it is becoming increasingly difficult for humans to forecast and plan accordingly. Artificial Intelligence (AI) can take all these factors into account and still produce these forecasts. To quickly react to shifting trends in supply chains, companies should operate more proactively. Below are five trends that address the need for retailers to embrace more advanced planning solutions.

1. Dynamic pricing to cope with inflation

Global inflation is spreading uncertainty for both consumers and thereby retailers. In 2022, the New York Times reported the inflation rate for October to be 7.7% in the United States. CBS in the Netherlands reported an inflation rate of 10.7% for the same month for the Eurozone. In the Netherlands, this rate was even higher at 14.3%.

The typical retailer responds to inflation by setting higher consumer prices. However, simple price increases will lead to lower demand and sales. In contrast, dynamic pricing is a data-driven approach that enables retailers to consider what products should be in the scope of price changes, when prices should be adjusted, in which sales channel prices should be changed and what the new prices should be. This should be done while continuously taking into account the impact of price changes on customer demand, i.e the price elasticity of products.

One way of achieving this is through advanced planning solutions. Ideally, these solutions have an interlinked dynamic pricing module. Tools like these enable sales departments to add a layer of flexibility to their pricing and therefore account for inflation as the basis of product pricing. Dynamic pricing enables companies to remain flexible and has the potential to increase profitability, also by managing stock better. Products with a low price elasticity have more room to increase prices than those with high elasticity. To make the most of this approach, dynamic pricing should be executed in synergy with supply chain planning activities to ensure that revenue and margin are maximized, and the impact of inflation is minimized across the entire supply chain.

2. Proactive planning with AI and ML to (re)act faster

Thanks to globalization, the effects of events are felt beyond just one company, country or region. Take for example the war in Ukraine, the stranded container ship in Egypt's Suez Canal or the COVID-19 pandemic. With more connectivity comes greater complexity. Forecasting and replenishment in supply chains have never been this intricate. The human brain can only incorporate so many factors into the equation, and it hits its limits the more complex the equation gets.

However, AI can incorporate an infinite number of factors and, with the help of machine learning (ML), enables supply chain professionals to quickly react to trends by providing them with real-time data and insights. For example, by using predictive analytics and data mining techniques, supply chain professionals could identify potential issues or opportunities in the supply chain and take action. Possible negative impacts of such trends can therefore be dampened; the efficiency and responsiveness of the retailer improve as they can better identify changes in the market. These AI and ML-produced trend forecasts do not have 'the final say' but can serve as useful sparring partners that offer guidance in the decision-making process. Therefore, AI helps companies become more proactive as they can react quicker to developments along the supply chain.

3. Outgrowing Excel

As companies grow so does the number of factors that influence supply and demand for a product. Therefore, the usage of simple rule-based Excel sheets will not do the trick anymore as the data becomes too complex. Furthermore, combining large quantities of independent Excel files into a planning system is destined for trouble. This mosaic of spreadsheets is difficult to update and upgrade, error-prone and in danger of links being altered or broken. Additionally, Excel does not possess the modeling capabilities that allow for better, faster and more accurate calculations. Therefore, this conventional approach is outdated.

More advanced planning solutions can support organizational growth by establishing fit-for-purpose systems with dedicated and tailored planning activities that are scalable in size, incorporating ML to adjust to these large quantities of complex data sources. Especially small to medium-sized companies with a growth ambition should seize this opportunity and harness the full potential of more sophisticated forecasting tools. The best approach is to start small, explore and experiment based on preference and slowly develop an effective forecasting capability. This way, organizations avoid being rushed into these processes without a clear vision of how they should create value for the business.

4. A holistic view of end-to-end planning

Communication and cooperation are known cornerstones for success, but the reality is often quite different. Companies frequently consist of siloed departments. For example, demand forecasting, procurement and replenishment may be separate departments. Replenishment can often even be further divided into a store and distribution center level. Operating in silos leads to files not being shared between departments. This leads to teams having their departmental version of the truth based on their own data, information and insights. This leads to a lack of coordination and inaccurate forecasting due to unshared data files and therefore a fragmented overview, increased costs due to missed opportunities for cost-saving measures and ultimately reduced competitiveness because it is hard to keep up with competitors.

An advanced planning solution could help address the problem of silos in a company. This type of tooling can, for instance, act as the shared foundation for the demand planner and the store replenisher in a company. With a common source of data, information and insights, this tooling can facilitate more effective cooperation between not only company departments but also supply chain partners. Operating with one holistic view throughout the whole supply chain will increase efficiency on all levels in the supply chain.

5. Out with the old, in with the new features and products

Technology can help forecast how consumers respond to new product features or even entirely new products. Forecasting for existing products can be done based on sales data from the past year, adjusted for marketing campaigns that the company runs, creating demand curves. This historic data is very static and does not consider current trends. Looking at more volatile markets such as fashion or pharmaceuticals, it is difficult to forecast demand for new products, product features, or trend/event-influenced products based only on historic data. The true value for these markets lies in being able to forecast on a product feature level, such as colors, prints and materials.

These well-known challenges do not have a clear-cut solution just yet, but more and more businesses are exploring AI. According to ING, only 14% of Dutch retailers used AI in 2021, meaning there are a lot of gains to be made. Nonetheless, retailers can already take important steps towards innovating their supply chains in 2023. For instance, in fashion, such tooling can help create demand-driven collections that truly speak to one's consumers, optimize margins with dynamic pricing and decrease overstock with the power of AI-based forecasting and replenishment. With the power of an advanced planning and pricing solution, incorporating AI and ML, organizations can use technology to begin future-proofing their supply chains.

[1] 6 Trends in Demand Planning that Matter (johngalt.com)

[2] Top Supply Chain Trends Heading Into 2023 (Forbes.com)

[3] Trade outlook 2023: slow steaming in rough waters (think.ing.com)

[4] Kunstmatige intelligentie leidt online versnelling retailers in goede banen (ing.nl)