The ability to understand and meet customer demand could make or break a business and a robust demand forecasting strategy that includes Artificial Intelligence (AI) forecasting will give your business an edge.
Demand forecasting falls under the umbrella of demand planning and allows companies to better determine and predict demand, maintain the right levels of supply to meet demand, plan product lifecycles and measure market size and share. The demand forecasting process is essential to supply chain management and informs a business’s procurement, logistics, and distribution processes.
Over the years, the process that planners use to forecast demand has evolved. Traditional forecasting involved a planner collecting internal transactional data such as historical shipments and using simple calculations to manually build the demand forecasting spreadsheets.
In the 1990’s Enterprise Resource Systems arrived with ‘black box’ forecasting methods using time-series models. These integrated solutions improved structure, access, and process and reduced the effort of planning in spreadsheets, but they have not coped well with the expansion of data. Information saturation from the weather to competitor promotion and from covid infection rates to social media sentiment abound and single data source forecasting is now outdated.
The exponential rise of available data that we have seen in recent years has been matched by system capability and availability. AI/ML forecasting has reached mainstream forecasting and planners in all types of businesses are now using cloud-based, next-generation solutions using AI/ML technology to improve forecast accuracy and reduce forecast cycles.
What is AI forecasting
Next-generation forecasting relies on AI capabilities, such as Machine Learning (ML) forecasting algorithms, to streamline and optimize demand forecasting processes. Planners can take vast amounts of structured and unstructured data and let AI/ML algorithms connect the data nodes and edges to discover patterns and relationships in ways that a traditional forecasting system could never do. This automation helps planners to make faster, better decisions.
How does AI forecasting work?
AI forecasting improves the demand forecasting process and forecast results because the algorithms create more accurate pictures of demand causality compared to the more traditional demand forecasting methods. AI/ML solutions enable the transition from legacy forecasting to true demand sensing and shaping. Demand Sensing means using the power of automation and machine learning to analyze all the data that you can collect from demographics to the weather, and from price changes to consumer sentiment and making sense of it against your history. Demand Shaping uses that insight to not just create a superior forecast but create better new products, promotions, marketing campaigns thus increasing market size and share.
An AI forecasting process can take the heavy lifting involved in forecast analysis and delivering forecast accuracy off of a planner’s shoulders by automating analysis and suggesting courses of action. These actions can also be automated or authorized by planners triggering workflows or widgets.
At a summary level, a typical AI/ML forecasting process looks like this:
- Data collection and harmonization
- Data analysis and feature engineering
a. exploratory analysis
c. feature creation and selection
- Model creation and iteration
a. algorithm creation
b. model levels and slicing
a. training and validation
c. predictions and guardrails
- Forecast generation and cycle start
What are the benefits of AI forecasting?
AI forecasting offers many benefits to the demand forecasting process, most notably improving forecast accuracy, bias removal, and forecast transparency along with reduced cycle time and efficient reactions to demand fluctuations. An industry or organization using traditional forecasting methods can experience demand shocks that can throw a business’s supply chain into a tailspin.
AI/ML delivers accuracy, efficiency, and agility touching every aspect of a forecast cycle from driver data selection to the optimal blend of the consensus forecast. Here is a short selection of key processes that can be performed faster and more accurately with AI/ML:
- Driver selection and suitability
- Outlier correction
- Exception management
- Price and promotion effectivity
- Marketing event optimization
- Product lifecycle management
- Consensus forecast blending
- Market share exploitation
Of course, with next-generation cloud capability, these activities and functions will be collaborative and visualized in dashboards with grids, charts, widgets, and pop-ups providing the planning team with all the insights enabling them to make better decisions.
The ability for planners to build stronger demand forecasts with the help of AI algorithms can ultimately create a more agile and resilient supply chain. With a more accurate forecast, companies can work more proactively with suppliers to ensure the proper amount of products and materials are ordered on a timeline that meets customer demand and reduces delivery interruptions. The granularity and speed at which disruption impacts and growth opportunities can be identified and automatically resolved or applied means that AI/ML forecasting is no longer a ‘nice to have’ but an essential business toolkit.
Common problems in AI forecasting
Of course, AI/ML activities and automation don’t come without challenges. A next-generation planning solution implementation will typically have to contend with:
Evolving roles and responsibilities
Where previously forecasting required data administrators to manage data collections and cycle preparation, AI/ML forecasting requires data scientists and data analysts to deliver successful results. The opportunity here is to create career path roadmaps for planners. Offer them the opportunity to become the next in-demand specialists.
Python and R Skills
AI/ML methods are increasingly created and managed in open-source programming languages like R and Python. There are libraries of algorithms available, or they can be scripted from scratch in-house or using system integrators. Having resources who can use these languages is likely to be key to efficient implementations and ongoing solution optimization.
External data may become outdated, superseded, or no longer available. An AI/ML solution will need regular data assessments updates. A neat solution to the analysis is to use AI/ML to assess the data quality and advise when data should be changed. The future of planning is outward-looking, get ahead of the curve by developing a team to search and manage this data.
Data privacy and security
The importance of external data and the move to outside-in planning using open-source algorithms on cloud-based solutions brings a heightened security risk. Planning data is extremely sensitive. Don’t let plans become stolen and exploited—ensure that your planning solutions and processes are completely secure.
A common consequence of a proliferation of source data usage is that system performance becomes impacted. Engine run times and slow user interface access need to be measured carefully. Be sure to make structural efficiency of your dataset a core implementation strategy.
Overfitting is a common problem with planning solutions that focus more on the source data than on the predictive results. Put simply, overfitting means that the statistical forecast resembles the past data too closely. The future will not be exactly like the past but approximately like the past. Consider moving away from ‘Best Fit’ forecasting to blended forecasting where the results of multiple forecasts are merged to create a composite prediction.
In conclusion, demand forecasting, that essential aspect of supply chain management, is evolving. AI/ML solutions can process and evaluate large volumes of internal and external data from price and promotions to competitor web scraping and Twitter hashtags and create superior predictions. The time of a single historical source methodology for planning has passed. Ultimately, an AI/ML-based solution can significantly improve business forecasts, build stronger supplier relationships, and create a more resilient supply chain. Deal with disruption and leverage the opportunities for market growth by implementing AI/ML forecasting.