By Jörg Junghanns, Head of Europe – Digital Supply Chain for Business Services at Capgemini
In today’s volatile commercial environment, where customer expectations are at an all-time high, supply chain flexibility has become paramount for any business looking to edge ahead. Recognised as the key enabler of any truly adjustable supply chain, this race for agility is placing increased pressure on businesses to strengthen their demand management capabilities.
Whilst previously viewed as a reactive process involved in responding to changing market conditions, advances in technology are forcing demand planning to take on a strategic role. No longer seen as just a fulfilment function, demand planning is now expected to be a growth enabler that underscores a business’s ability to drive profits, impress customers and stay ahead of competition.
So, as the demands of the digital era drives heightened expectations around demand planning, just how can they keep up? Here are three ways demand planners can harness the latest technologies in order to seize their new strategic opportunity…
Dedicating time to decision-making
Through the adoption of AI, traditional and labour-intensive tasks within demand planning can be automated. This is of immense value when it comes to analysing and interpreting data. Not only is AI able to do this more accurately and quickly, but this allows human teams to redirect time previously spent on this task into more strategic business efforts.
What’s more, AI can give demand planners back precious time previously spent creating short-term demand plans or triggering stock replenishment. With these tasks firmly in the hands of AI, teams can then concentrate on progressing higher-value business objectives that will have a greater impact on the organisation. With AI taking on menial and time-consuming tasks, demand planners can dedicate more hours on investigating how to improve operational efficiency, identifying new ways to increase profits and take on a more holistic role within the business.
Improving forecasts
By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth. As this wealth of data grows exponentially, it is becoming increasingly more difficult to detect customer purchasing patterns. Here, AI can be deployed to process this data and excavate subtle patterns that a human might not be able to detect. By aggregating datasets from Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) and IoT systems – and analysing this against external variables and contextual data such as a calendar of events, seasonality and the weather – AI can provide an extremely accurate forecast.
To take a truly holistic approach, AI forecasts should then be linked through supply and inventory planning in order to automate replenishment triggers, so that organizations consistently have the correct number of products in stock. In turn, this drives sales by improving order fill rates and shelf availability.
Rapidly responding
Historically, external factors such as natural disasters or shortages of raw materials have seriously impacted demand forecasting, leaving supply chains vulnerable. Instead of using legacy data, AI and machine learning tools use real-time calculations to react to supply chain disruptions. On top of this, automation enables rapid responses to changing consumer demands, leading to boosting profits and enhancing consumer loyalty.
When it comes to retail, for example, weather can trigger significant fluctuations in consumer demand. Creating rules based on the relationship between demand for a product and elements such as rain, hours of sunshine and temperate can be a laborious task. When these processes are automated, retailers can proactively plan for increases or decreases in local demand. Quicker reactions to external factors such as these can boost the accuracy of demand planning and limit monetary losses.
Conclusion
As a result of growing demands and evolving technologies, the role of the demand planner is rapidly transforming. With many of the operational functions they were once responsible for now automated, their remit has broadened outside of their historically transactional role.
By leveraging AI and automation, demand planners can now be seen as partners to their business, working in real-time and collaboratively with other disciplines in the organization such as production and planning systems, and of course ultimately with customers.
