Why You Should be using AI for Demand Planning and what the process is.
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When considering the adoption of new technologies, businesses often ask themselves ‘What problem will this solve?’ and ‘How will it help us achieve our business objectives?’ Depending on the company, these questions may not be easily answered. However, when it comes to implementing Artificial Intelligence for demand planning and supply chain management, the answers become simple. That’s because AI can improve visibility into future demand, reduce forecasting errors, decrease lead times on new products, and cut back on excess inventory that is frequently caused by inaccurate forecasting.
What is AI-powered demand planning?
As the name suggests, AI-powered demand planning is the process of incorporating machine learning, artificial neural networks and insights from past demand data to forecast future demand. Traditional demand planning primarily relies on forecasted sales by product and region, coupled with inventory projections to create a demand forecast that denotes the expected quantity of goods to be bought or sold. AI-powered demand planning, on the other hand, uses historical data to create a predictive model that not only forecasts demand, but identifies any potential issues within the supply chain that might result in future supply chain issues.
Identifying the root cause of supply chain problems
Data analytics has become an indispensable tool for demand forecasting and supply chain optimization in many industries. But without the proper implementation, it can also be a waste of resources. With so many factors that can cause supply chain issues, it can be difficult for business leaders to know where to start when addressing supply chain problems. That’s why it’s important to identify the root cause of supply chain issues. First, it allows business leaders to focus on the right problems. Second, it helps them prioritise those problems. And finally, it guides them in the right direction when implementing AI for demand planning and supply chain management.
Steps to implement AI for Demand Planning
There are several steps involved in implementing AI for demand planning and supply chain management. Here’s a quick overview of the process.
First off, you will need historical order data. Order data is different from sales data. Order data tells you what each customer wanted, which is their demand. If you couldn’t fulfill that order, or the order was repeated because it was fulfilled in the previous sale, this should be in your data too. If you have a reasonably high OTIF across your ranges then this may not be significant. If you do, you need some help.
Ideally, this order data is daily and you have 5-6 years’ worth of data. The reason for this is that the global pandemic will probably have interrupted and accelerated orders at different periods over 2020, 2021 and maybe some of 2022. Therefore, if we take 3 years of data, you will find that each April you will have non-typical data. There is little point in feeding the model overrides and data that isn’t real, this will break your process and take you back to manually inputting the data you are expecting.
One issue that is always identified is contextual data. The reason Demand Planners revert to manual, excel-based forecasts is that their human collaboration with other departments is based on human anecdotes and bias. Sales teams do what sales teams do. We have solved a lot of this problem by already having a range of publicly available datasets ready to be used with your data.
Say you’re in DIY or hardware. We have, for example, data on housing starts, planning applications, housing transactions, consumer price indexes and Purchasing Manager’s reports so we can see which external factors best fit with your market, and over what time frame. This means that housing transactions may be really important but is a 6-month lag, whose importance can be trumped by shorter-term impacts of consumer confidence or inflationary pressures.
Secondly, it’s about how to choose the models that will give you accurate and precise predictive analysis. This is where there is art with science. The quality of the output is really important obviously and should be your number 1 priority. Once you have the quality you then need the explainability. This is also coupled with the simplicity or complexity consideration. An overly complicated model will possibly be quite brittle, while an overly simple one will not be convincing. Having assured business insights and analysis here is key as how you can weight and assign different attributes becomes part of the explainability.
Finally, the output needs to be usable. Utilisation is one of our key pillars. The model may be fabulous, and the output may be high quality, but until it is used, it’s worthless. How is this work going to be used, how is it followed up and how do you start to utilise and maintain it? This is where having a way of accessing the results, feeding back
We have found that by simply helping to supply some contextual data on promotions, OTIF and range changes, we can deliver an AOP within 1% of the internal estimate within 2 weeks.
The process is scaleable meaning that this can then be refreshed as a rolling forecast, seen through customer or product lenses, scenario planned with sales teams and utilised to accommodate further price increases or supply disruptions.
Conclusion
Business leaders can improve visibility into future demand, reduce forecasting errors, decrease lead times on new products, and cut back on excess inventory that is frequently caused by inaccurate forecasting by implementing AI for demand planning and supply chain management. Now that you know why you should be using AI for demand planning and what the process is, it’s time to get started. Get in touch with the team at Palm AI (https://palmai.io/contact)to discuss how AI for demand planning and supply chain management can benefit your company.