While it’s true Machine Learning and Artificial Intelligence have been used in SCP (Supply Chain Planning) processes before, we can’t really say they’ve reached their full potential when it comes to helping organizations reach their operational goals.
Today, there is an ever-growing need to implement new technologies into existing structures for a plethora of reasons: increasing profit margins and reducing wasteful tasks to a minimum, overcoming challenges posed by sudden market disruptions and shifts, and gaining granular control over huge amounts of useful data, among others.
This blog aims to outline the uses of AI and Machine Learning in Demand Planning processes. In a nutshell, Demand Planning refers to the use of advanced forecasts in estimating demand for different items at different points in the supply chain.
Demand Planning’s complexities, and the sometimes inexplicable factors surrounding it, have been giving CEOs, CFOs and COOs nightmares for several decades now. These forecasts need to adapt to constantly changing variables in order to make an impact – and AI and ML are just the tools to help them do it.
Gaining insights from cross-platform data
Processing information from massive, ever growing data sets and platforms such as ERP, IoT and CRM systems, AI is able to combine cross-platform data into a singular source of truth. This process improves Demand Planning by a significant margin. With more high-quality data, recommendations on how to improve operational excellence can become a reality. In addition, analyzing customers’ opinions, reviews and sentiments can bring even greater insights into how to act to improve your company’s profitability and plan for the future.
Reacting to problems in real-time
AI and ML powered tools use lightning-quick, real-time calculations to suggest solutions to supply chain disruptions, rather than relying solely on historical data. Accounting for factors such as the emergence of competition, new channels for sale or product placement, natural disasters, weather effects, social sentiment or a number of other challenges, they can significantly improve Demand Planning processes. By using this information, AI is able to evolve on its own and constantly fine tune your organization’s efforts.
Natural language processing
This used to sound like something out of a sci-fi movie, but it’s already happening – intelligent systems are able to perceive and process voice commands and respond to them in a fairly accurate manner. Not only that, but they’re now prepared to give information about how to improve business processes and what issues to focus on. This could prove especially useful in Demand Planning, as users could simply ask systems about real-time changes in different variables and react to them accordingly. Here’s an example of a Cortana-powered smart thermostat used by Johnson Controls. Natural language queries can drastically simplify the way business processes are conducted, speeding up forecasts and leaving users with more time for other tasks.
Automating supply chain-related tasks means one of your most precious resources will be freed up – we’re talking about time, of course. AI can autonomously suggest which pain points to focus on, inform users which raw materials, SKUs or production plants to pay close attention to, and recommend actions to alter and improve financial forecasts. As a result, companies can expend less time on time-consuming, unreliable calculations and make quicker decisions.
You can browse our website for more information on how to leverage Artificial Intelligence and Machine Learning in different aspects of Supply Chain Planning and modernize every business process in your organization.