When should we perform maintenance on our equipment? How much stock should we purchase of this item? Will this project stay within budget? These are a few of the many questions which can be addressed with predictive analytics. However, the question of when to get started with predictive analytics might be difficult to answer, as it has many possible applications and it could be unknown territory for your organisation. The intention of this blog post is to give some support in answering this question.
In order to get started with predictive analytics, a couple of preconditions have to be met. Roughly, the following two preconditions apply:
The ‘required data’ means that sufficient observations are available and that the relevant variables are available as well. For example, if an ice cream truck business wishes to predict the ice cream sales for the next day, the ice cream sales of many days are required (in this case an observation is 1 day) and the temperature should also be recorded (the temperature is a relevant variable).
There should be willingness in the organisation to use the predictions as made by the predictive model. In case decision making is currently mostly done based on experience and gut feeling, this would be quite a change. Not everyone in the organisation has to fully embrace predictive analytics, but there should be some people who recognise its potential and are willing to approach a certain business problem with predictive analytics. Decisions do not have to be fully based on a prediction, but the prediction can be seen as an advice which is based on the available data. This way, the relations as extracted from the data are combined with the human experience of the decision maker. By combining the available resources (data + experience), decisions can optimally be substantiated.
If all preconditions are met, a business case can be created to determine whether predictive analytics would be a good investment. Based on some rough assumptions, the costs and benefits are estimated. Benefits mostly come from a higher efficiency and preventing wrong decisions. Costs mainly consist of the implementation project and software licenses.
Consider for example that 3% of the stock is thrown out each year because the items are expired or obsolete. If adopting predictive analytics results in a 50% more accurate estimate of demand, only 1,5% of the stock would have to be disregarded. In case the stock value is €10 million on average, this would mean savings of €150k for each year.
Getting started with predictive analytics does not mean that a large scale project has to be initiated right away. It is recommended to start on a smaller scale, with a pilot. This has several advantages:
During the pilot all required data will be collected and some first data exploration is performed. Among others, it is tested whether the expected business logic is found in the data. For the ice cream truck example, this would mean that sales are higher when it’s 25 degrees celsius compared to 15 degrees celsius. If this is not the case, the cause should be found and solved, before the actual predictive analytics are performed. It could be that there are insufficient observations or that relevant variables are missing.
Applications of predictive analytics are available in practically every organisation. Before getting started, it should be checked whether the preconditions are met. Thereafter a business case is made to consider the return on investing in predictive analytics. This is followed by a pilot, which might lead to an implementation. Are you curious to find out whether predictive analytics can be of value for your organisation? Then feel free to reach out to us, and we can set up a call to discuss your specific situation.
Jelle co-founded Cadran Analytics. He spends most of his time on sales and project management. In addition, he can often be found coding in R.
