Predictive Analytics for Business Forecasting

Here we’re joined by the host of IBF’s On Demand Podcast, Eric Wilson, to discuss the role of analytics in modern day organisations and in particular, what we can learn from Eric’s new book titled ‘Predictive Analytics for Business Forecasting’.

Eric has 30 years of plannning and analytics experience and is a Certified Professional Businesses Forecaster (CPF), as well as being Certified in Production and Inventory Management (CPIM). At the Institute of Business Forecasting & Planning (IBF), he helps to drive their strategy, as well as conducting, researching and creating content in addition to hosting the bi-weekly “On Demand” podcast. He is currently in the process of publishing a book entitled “Predictive Analytics for Business Forecasting”, which goes into more detail on the key concepts that he has used throughout his career.

Nowadays, it’s extremely easy to create thousands of numbers with machines, but how do you produce something that is actually worth the attention of a human being? This is the main challenge. We can say that data should be considered as building blocks that need to be transformed into insights. Unfortunately, many companies often struggle to access these precious insights.

With analytics, there are several ways that you can arrive at non-productive lines of thought. To avoid this, it’s important to focus on something that has a mundane, physical or tangible impact on your supply chain. For example, a purchasing decision, a stock movement, or a price change. Often, it’s easy to get lost in “vanity metrics”, KPIs where nothing is “key” and instead they’re simply vast oceans of numbers that lack focus, or any built-in intent, and serve only to distract.

We talk in more detail about the roles of Demand Planners and Supply Chain Scientists and how they can be positions that are challenging to recruit for. We compare these two roles and the various skill sets that are required. We also discuss the peaking interest in supply chain, which for many years now has remained a somewhat niche topic that is now however gaining in interest due to the impacts of COVID.

To conclude, we reflect upon how predictive analytics may evolve in the upcoming years, with different data types, such as microtargeting (using customer loyalty data for example) likely to grow in importance. We also discuss how, when technology evolves and gets things “right”, it actually sort of blends into the background without making much of a fuss - antispam filters on emails are a great example of this. Future supply chain tech may also follow a similar trajectory, with tooling that smooths warehouse planning in a subtle way and only requires a few specialists to pay special attention to it daily. We could say that the ideal supply chain of the future is a “silent” supply chain.

Timestamps

00:08 Introduction

00:22 Eric, perhaps you could tell us a little more about your background and your role at IBF?

01:19 Joannes, what is your initial overview on the role of predictive analytics in modern day organisations?

02:22 Are we producing too many numbers without looking at what is really important?

03:45 In which case can business decisions change the way a company operates?

05:29 Would you say that there is a lack of capable demand planners in the job market?

07:15 Why is it so hard to recruit well qualified Supply Chain Scientists?

10:56 Would you say that this kind of grounding is more difficult to achieve?

14:22 Would you say that the supply chain industry is intimidating for someone starting out?

15:47 Is the sheer number of buzzwords intimidating?

18:12 How do you see the role of demand planning changing over the next couple of decades?

19:44 How do you see the future for the tech landscape?

24:54 Eric, what are the skills you would like a demand planner to gain from reading your book?