Using A Consistent Forecasting Methodology
How do you create your forecasts should be a straight forward question for most but the answer is arguably more complicated than we would think.
Typically a standard forecasting methodology would be implemented and can be adhered to.
The problem with a standard methodology is that in the real world it is difficult to find a standard set of circumstances between products, locations, or even teams.
Each of these things can result in a standard methodology being more or less accurate depending on the mix of variables that we are presented with. As the accuracy changes, there are impacts that the business must manage.
Reduced accuracy can result in a damaged reputation with customers, higher costs, and reduced profits to name just three consequences.
We must also consider that when individuals from multiple locations will play a part in the creation (or local level changes) to a methodology.
Individual reps, store managers, or any individual has their own ideas about what is most important and what is less so when it comes to forecasting. These views often find themselves being included.
The result is that the idea of a standard methodology quickly becomes a fairytale. The problem here is that humans are bad at spotting patterns in data. Being far more likely to find patterns that support their hypothesis rather than refute it.
We touch on this more in improving forecast accuracy
How To Fix It
The way to fix this is to accept that there is no single methodology that can solve for each of the innumerable variations in the real world. While also removing the human aspect from forecast calculations. As we have seen humans are notoriously bad at spotting patterns.
It is far better to calculate forecasts leverage the power of artificial intelligence and machine learning to accept all of the data available to you and implement a process of analysis, testing, and refinement.
By doing this we can remove the weaknesses of traditional methodologies whilst keeping the benefits.
Working in this way you gain the additional benefit of keeping knowledge within the company rather than individuals. Should an individual leave the company the secret to accurate forecasting does not leave with them.
The impact of switching to an AI-powered platform that is leveraging machine learning like iERP is dramatic.
Here are some of the results we have achieved at iERP.
99% (it has been every customer but we can’t claim 100%) have seen an improvement in the accuracy of their forecasting simply by adding their historical data into iERP
98% is the average forecast accuracy
8% the percentage increase in profitability
Over £1million in capital released from unnecessary stock.