When explaining demand forecasting to people with non-technical backgrounds, the handiest comparison drawn in with weather forecasting. The instant consequence is the understanding that it is a prediction of how consumer demand may shape up.
Although the understanding thus obtained is fairly correct, it lacks accuracy; one thing that demand forecast cannot afford to lack, unlike the weather. Because while an error on the part of the meteorological department in forecasting can cause people to perhaps get stuck in rain, minute miscalculations in demand forecasting can lead to mismanagement of production planning, inventory management, supply, and the entire product lifecycle.
Demand forecasting is, therefore, more so a science, that is both, executed by and effective to the decisions by corporate supply chain and business management.
A common error that people make while perceiving this science is confusing it with another technological concept -- predictive analytics. What’s important to note here is that predictive analytics is a greater umbrella employing data analytics to obtain valuable insights in different domains that are helpful for better and more accurate demand forecasting.
How AI and Advanced Analytics Are Streamlining Demand Forecasting
It is important to weigh the risks of mismanaged demand forecasting. What starts off as a compensation to the risk of running out of stock, filling shelves more than the actual demand can lead to magnanimous losses and so can the converse cases.
That’s where technologies like AI and advanced analytics come in to leverage greater accuracy, precision, and efficiency to obtain better results.
- Improved Efficiency: AI Tools used for inventory and supply chain management pick up the minutest changes happening in these domains in the marketplaces. While this can be an aid to traditional retailing, digital portals that offer e-retail services through AI inventory management further help in gaining customer shopping insights and improving efficiency.
- Improved Accuracy: Right from demographics to customer behavior, analytics have a directly proportional relationship with accuracy. The greater the internal, external, and contextual data, the better the data analytics, the better the accuracy of demand forecasting.
- Reducing Errors: According to a report by McKinsey, errors in supply chain networks can be reduced by a drastic 30 to 50% using AI-powered forecasting. The most fundamental logic can be traced back to the highly precise connection that AI makes from the analyzed internal and external data.
- Minimizing Cost and Losses: As a cascading effect of the above benefits, a 65% reduction in lost sales is estimated due to poor inventory management and accuracy. This also means a significant 10-40% reduction in warehousing costs.
Approaches That Companies Can Implement To Get Demand Forecasting Right
While technologies like AI and Advanced Analytics can greatly improve the forecasting results in the above ways, its implementation is in the hands of the company and the approach is crucial. Here are the 5 crucial approaches you need to have when steering demand forecasting towards betterment.
While the most logical approach would be focusing on the AI-strategy, the very point of implementing AI analytics is to get results. The only right approach here is to have an iterative process that is centered around an objective, quantifiable business result. If that’s what you work around, that’s what you obtain. The most tried and tested way to make this approach successful is working top-down in not only the product but also market hierarchies. Identifying the point of diminishing returns when the graph of improvements in forecasting starts flattening, can have your strategy go a long way.
Focusing On Relative Improvement
Getting your goals straight really helps, and as fundamental as it may sound for a functional company, many fail to understand that focusing on outranking alternatives is more important than just improving forecasting performance.
Consulting Multiple Models
A common mistake that most businesses make while implementing AI-powered demand forecasting models is sticking to one simple model. On the contrary, when the plan is to obtain more power through forecasting, having two or more models is always the right approach. It is essentially about accommodating an ensemble of simple models like moving averages and complex ones like ARIMA and others, to average the answers.
Trusting Only Real-World Testing
Establishing your forecasting model’s accuracy using backtesting sounds pretty reliable on paper, but evaluating its performance in real markets is the real deal. That’s because the results in testing will always be specific to a data set. By testing in the real-word, you can not only identify the new developments in old data but also gain an idea as to how each dollar impacts the process changes.
Maintain A Buying and Building Balance
Know when your business needs an in-house built forecasting and when you should pass over the responsibility to a vendor or professional forecasting tool. The key is to ensure that when your requirement for forecasting is strategic, you leverage your resources to build customized data pipelines as opposed to the basic, inflexible, and straightforward ones using the external tools.
Transformed Demand Forecasting: What You Need To Look Out For
We can all agree that optimized demand forecasting can help companies manage not only better profits but also customer satisfaction, brand reputation, and marketing. That being said, there are some common pitfalls that companies end up falling prey to despite following all the above approaches.
Challenges may arise due to a lack of flexibility in supply chain networks and the purpose of demand forecasting is sure to get lost. Companies should first make sure their networks have enough room for optimization.
The second most important aspect of forecasting is data, real-time changes and the efficiency in reflecting those cannot be compromised. It should also be noted that the forecasting for new products works on insufficient historical data. In such a case, relying too much on the accuracy of a new product’s forecast would be a risky thing to do.
All in all, the power that AI and advanced analytics can impart to demand forecasting cannot be paralleled, however, the results are nothing without the process and execution will always be of paramount importance.