This is the second of two articles based on a conversation with Donald Allan, CFO of Fortune 500 tool maker Stanley Black & Decker.
In the first part of our report, Don Allan talked about two types of transformation that he and his finance team are executing.
Both are technological in nature. One is a reimagining of the back office that includes consolidating the company’s more than 70 ERP instances into less than 10. The other is what Allan calls “Industry 4.0,” consisting of early efforts manufacturer to create “smart factories” by leveraging digital and robotic technologies.
Here, Allan adds a third high-tech transformation to the list, and the one that could stretch manufacturers the most: increased use of advanced analytics.
The changes you’ve told us about so far seem pretty ambitious. Where are you in terms of using today’s most advanced analytics capabilities?
Most industrial companies, and we are no different, have not dramatically evolved their business models to use advanced analytics to drive business models, business decisions, and even some customer decisions.
Today, we still perform traditional data analysis. For example, in our business intelligence warehouse, we have a lot of information about the profit margins of particular products. Why has this one dropped a point or two, and how can we begin to change this trajectory?
What is changing now is that making decisions about pricing and manufacturing costs requires far more input than traditional internal data.
This requires looking at competitors’ prices. This requires looking at competitors’ costs, where you can get them. And that requires looking at manufacturing cost alternatives from vendors and things like that.
With this data, using artificial intelligence and machine learning algorithms, you [can develop] information to assess your business model.
Do you use data scientists for this?
Yes, we brought these people on board. Sometimes I joke with the finance team that they’re all going to be replaced by data scientists.
But that’s only half a joke. I really think some of the finance functions today are going to become data science functions in the future.
So you need finance-oriented data scientists, but hopefully you also have data science-oriented FP&A people.
Exactly. Or, a handful of data scientists will do all the data analysis and provide insights, while the finance team figures out what to do with it.
We plan to grow the data science team to around 75 people by the middle of next year. Right now it’s about 15 to 20 people. I’m not sure I want 75 until they show me some value.
So we identify use cases. [For each one, the data scientists] we need to create value, whether it’s reducing inventory, generating revenue, reducing our costs or whatever. But I’m confident that we can create probably $200 million in value across the business over the next two to three years with this approach.
Can you provide an example use case?
They try to solve very common problems. For example, our tooling business is very complex. There are too many SKUs. We’ve discussed for years how to reduce this complexity, focusing on SKUs that turn very quickly.
It will be really interesting to see the result of the analysis on this subject.
But it’s one thing to have ideas. Then you need to take action to create value. If someone you bring in says you’re going to save $50 million by doing this or generate $100 million in revenue by doing that, it becomes hard to ignore that.
Given the growing reliance on analytics and robotics, is Six Sigma, which has been a core process methodology in manufacturing for decades, destined to become obsolete?
Six Sigma uses mathematical techniques to prove data before making changes based on the data. This actually tends to slow down the process change – it’s caught up in the math.
But I think Lean Sigma, the simplified version of Six Sigma, will always have value. It gets rid of the math and just looks at process maps of how we do things today to find and eliminate wasteful points.
I think data and digital could replace that. But I’m still not convinced. I don’t think Lean Sigma slows down what you’re trying to do.
Today, artificial intelligence could be considered the end game of advanced analytics. But it seems like every software company means something different when touting their AI capabilities. What does the term mean to you?
To me, it’s the same as machine learning. It simply means learning from data and information. Based on data, information and algorithms, a machine does something. Then it gets to a point where there is enough information and history for the machine to automatically change the algorithms, without human intervention.
It can be as simple as processing things in a back office, telling you if an invoice needs to be paid or if a journal entry needs to be recorded.
Are you worried that at some point you will lose control of what the machines are doing, so that you can no longer see or understand the components of the decision-making processes?
I do not know. What we and a lot of other companies do is have control checks around the process. People monitor and review the decisions made.
I’m not sure that will change drastically. We will always want to monitor it. As technology improves, one may wonder whether monitoring is part of the decision-making process or more of an afterthought.
So does it ever completely eliminate the human? I do not think so. Well, I shouldn’t say that. Not in my lifetime, let’s say.
Where are you on the AI adoption curve?
Oh, not very far. If it was a baseball game, we’d be in the second inning.