Leveraging AI Forecasting Models in a Data-Light Environment — a Brief Guide
Demand forecasting is an integral component of several business functions within an organizational setup; from risk assessment to workforce planning all necessitate an accurate understanding of demand. And with advances in technology, businesses that relied primarily on spreadsheet-based forecasting techniques are transitioning to AI-based algorithms in a bid to improve their forecasting accuracy.
AI models that have become increasingly sophisticated in learning from past patterns help organizations realize tangible benefits. For instance, by applying AI models to supply chain management, businesses can reduce product unavailability by 65%, warehousing costs by 5 -10% and administration costs by 25–40%. Likewise, companies operating in healthcare or telecommunication can leverage AI-driven forecasting engines to slash operational expenses by up to 10–15%.
Traditional demand forecasting methods require users to manually update data and adjust forecast outputs on a constant basis. This not only consumes a lot of time but also prevents businesses from responding fast to immediate changes in demand. Automated AI-powered models overcome these challenges by ingesting real-time data and continually identifying new patterns, allowing for agile responses to changes.
Notwithstanding its advantages, the adoption of AI has been limited, thanks to the challenges faced by organizations. As per a recent survey, around 56% of the organizations have adopted AI in one or more business functions, compared to 47% in 2018. So, while AI is being increasingly adopted across the industry, the rate of adoption indicates that organizations still face barriers, especially when trying to scale AI beyond a single function.
Limited availability of data (or limited utility of the available data) remains a primary barrier for many businesses. While it’s true that AI typically yields better results with more data, empirical evidence suggests that most organizations have sufficient data to derive insights from AI-based models. All they need are actionable strategies to successfully apply these models to their environment.
4 Strategies to Forecast Reliably in Data-Light Environment
Organizations can use the following four strategies-either individually or in combination-to obtain reliable forecast outputs when limited data is available:
- Choosing a suitable AI model
- Utilizing data-smoothing techniques
- Scenario planning to tackle uncertainties
- Leveraging external data APIs
Now let’s discuss these strategies one by one.
- Choosing a Suitable AI Model
First of all, organizations need to identify the most suitable AI algorithm, on the basis of the quality and quantity of the data available to them. Machine learning models can be employed to test multiple models and choose the most optimal one.
In several instances where limited historical data is available, organizations have been found to effectively apply a range of forecasting models with different complexity levels to disparate data sets. Their algorithm automatically chooses simpler models when historical data yields smaller sample sizes and complex models when large amounts of data and large sample sizes are available.
2. Utilizing Data-Smoothing Techniques
There may be scenarios where the time-series data gets affected by events not likely to recur regularly in the future. Such events may create anomalous periods that aren’t representative of the remaining data . These periods have patterns of seasonality and trend different from the rest of the time series. The machine-learning model doesn’t automatically treat such a period as aberrant and tries to learn from it. If this happens, it becomes difficult for the model to make reliable predictions.
The impact of such events can be mitigated through smoothing. Smoothing reduces ‘noise’ or random variations between time steps in order to create a more representative data set to learn from. This allows AI models to forecast with a higher degree of accuracy.
3. Scenario Planning to Tackle Uncertainties
When forecasting for the long term, it may be difficult to rely on historical patterns, as unexpected events are likely to disrupt trends and seasonality. In such cases, it is highly recommended to use what-if scenarios.
What-if scenarios are especially useful when sample sizes are small, making it difficult to forecast accurately, or in cases where demand and supply patterns are volatile, and models dependent on past data may fall short.
Scenario planning tools enable users to test a wide range of parameters and define new scenarios to account for any unexpected trends in forecasting. e.g. impact of COVID-19 on demand.
But in order to achieve desired outcomes from these tools, it is essential that the stakeholders clearly define the parameters that could impact the target variables, and set up reasonable ranges for these parameters. This way, they can avoid getting forecasts that are unrealistic and can lead to poor business decisions.
4. Leveraging External Data APIs
In addition to internal data, organizations can rely on external data sources (e.g. social media activity, location data, weather forecast, financial transactions, etc.) to inform the forecast values, particularly when little historic data is available. The rising popularity of these external data sources can be gauged from the whopping 58% market growth for external data.
Entities that provide external data typically offer their services through APIs, making it easy for users to access data and integrate it into their prediction models. For instance, a company can utilize weather APIs and access weather data to anticipate the demand for maintenance work with a higher level of accuracy.
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Source: https://mck.co/3SHtSko
