Battling Customer Penalties with AI
By: Opex Analytics
Tags: Artificial Intelligence, Customer Service, Business Value, Machine Learning

by Kristen Daihes

Customer penalty costs are impossible to avoid altogether. Historically, they were often attributed to the cost of everyday operations. Recent years have seen a steady increase in this expense category however. As a result, many companies are now starting to take notice and are searching for a solution that will bring these costs back under control.

I recently read an interesting Customer Deductions Benchmark Survey with this in mind. One of the points that stood out for me was the importance in differentiating between deductions that are considered the normal cost of doing business (such as trade promotions and allowances), and those that are true vendor violations and compliance errors. The survey then highlighted the latter as being self-inflicted, and reinforced the importance of understanding real root causes in order to prevent re-occurrence and therefore take action to limit or even eliminate these ongoing charges.

Early/late delivery was reported by more than 35% of respondents as the most significant non-trade compliance deduction (in dollars)

The survey also highlighted that early/late deliveries was the category of penalty affecting companies most this past fiscal year. This is no surprise in the CPG space as more and more retailers are shortening their delivery windows with suppliers and incurring more significant penalties for missing these windows. But that realization on its own won’t help companies predict and prevent future occurrences.

Only 29% of respondents had performed root cause analysis to understand the underlying reasons for deductions.

Interestingly enough, despite the pain of these ever increasing costs only a small percentage of companies are looking into the ‘why’. This statistic took me back to my days as a reliability engineer, driven every day to solve the puzzle of identifying the real root cause of failures. “You cannot eradicate that which you are not aware.” My company did see the value in dedicating tremendous time and effort into this but many other companies then, and even now, are simply reporting on the costs without taking any action.

So why aren’t more companies investing in root cause analysis to prevent these rising costs? Bottom line: It’s really hard. When I was a planner, about 40% of my time was spent doing manual research, pulling data from several sources to try to determine why a customer delivery missed its window. What made this particularly challenging was that so much transactional data was sitting in different functional silos. It was difficult and time-consuming to unite these disparate data sources to answer my questions. To make matters even worse, critical information, like carrier confirmation of delivery, was often completely missing.

There is now no excuse for inaction….

While I understand the difficulty of these endeavors through my previous work, I have come to find that artificial intelligence based solutions can really transform what is possible in this area. There are countless ways to go after linear and non-linear root cause analytics. Technological innovation also makes it easier to stitch together these previously disconnected data sources, as well as increase end-to-end visibility.

Beyond the obvious improvement in OTIF (on time, in full) compliance, I have seen the numerous benefits of investing in these improved root cause analytics solutions:

· Increased efficiency and speed in automating the assignment of the root cause of losses. Improving in these areas enables your organization to pivot resources toward proactive intervention and eradication of potential future losses.

· Reduction in the impact of human bias on the decision-making process by using machine learning to assign root causes to losses.

· Identification of structural improvements and the business value they represent.

I have also found that investing in root cause analytics can benefit a broader ecosystem. Target, for example, employs continuous improvement leaders to work with their suppliers to help them determine where their delivery processes break down. If the supplier can improve OTIF reliability for Target, they are likely to see a similar improvement for other retail customers as well.

The reliability engineer in me always gets excited by the focus on eradication of failures. To make a significant impact, you have to have the ability to identify where and why failures occur, as well as understand the full impact they have on the business.

BOTTOM LINE: If root cause analytics is not on your road map of opportunity, it should be.

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