How did the MLB team with only $44 M go on a 20-game win streak and compete against teams like the Yankees who spent over $125 M? Data…or Moneyball.
If you’re not familiar with this story, here is a quick summary of what changed the fundamental way of playing baseball: In 2002, the Oakland Athletics finished their season with a 103-59 record based on the premise that the collective wisdom of baseball insiders over the past century is subjective and often flawed. Instead of relying on collective wisdom of the insiders including players, managers, coaches, and scouts, general manager Billy Beane used numbers and statistics (sabermetrics) to compete in the MLB. Although they did not win the World Series, they fundamentally changed how the game of baseball is played.
So how does this relate to you and your supply chain?
Most people agree that change management is one of the most challenging areas of new system implementations because there is such a strong pushback when you try to change how veterans have been running the operation. People are wired to believe that the “refined process” that they’ve developed is the most optimal way of operating. To challenge and break these subjective beliefs, businesses need to adopt data science that not only measures and supports their current operations but improves operations by creating simulations and forecasts.
Imagine that you opened a small sandwich shop. After a few months of running the business, you have a good understanding of how much bread and meat to keep in stock. However, since you can’t accurately predict demand every time, you tend to overstock to avoid shortage. At the end of the day, it’s only a few loaves of bread and pounds of meat that you’re throwing away every week. To give you some perspective, within just 5 years, you have expanded your business to 100 locations in the state. Now you’re wasting hundreds of loaves of bread and pounds of meat. But, at this point, you’re thinking: “why fix something that’s not broken?”
One day, a young store manager approaches you with some numbers. Based on his calculations, his store is throwing away $100 worth of food each week due to overstocking. Across 52 weeks in a given year, that’s only $5,200. Then he goes on by multiplying that number across all 100 stores – $520,000. Suddenly, he has your attention. According to a study done by Genpact, collaborative planning and an integrated approach for data collection can improve forecasting accuracy by 15% and reduce held inventory by 25%.
Let’s look at another example that I’ve personally experienced in a warehouse – one of my clients was considering time and attendance system integration with their new labor management system so that the employees did not have to clock in/out into two different systems. Initially, the management did not approve the proposal, so I helped the superuser put together supporting numbers that shows the ROI. When the proposal was presented again with supporting data, it was a no-brainer to invest in the integration.
Examples like these exist in so many areas of supply chain; however, you cannot quantify these opportunities without leveraging data.
Despite a large capacity for value, companies can be reticent to fully embrace all of the opportunities for effective data use. Especially in companies with a more complex supply chain network to handle, an overabundance of data can make it difficult to discern the best course of action. Different divisions of a single company may have several different strategies towards data management that make extracting key insights difficult. For example, one large retailer was extracting the number of units shipped from both LM and WM systems, but since the systems were not in sync, there was constant discrepancy.
Additionally, without proper oversight, it is easy to make false assumptions based on correlations in the data that may not accurately reflect real-world events. While storing data is relatively inexpensive due to recent developments in cloud technology, investing the requisite time to analyze and understand data can be a process that requires heavy investment that may not seem worthwhile.
Given all of these areas for failure, is it worth it to invest the time and resources into data-driven supply chain decision making? Absolutely, provided that the approach is methodical with a clear, centralized focus. Your best bet is to start small; trying to jump into a sophisticated data strategy without first building up a strong case of support is a recipe for disaster.
First of all, start with clean data. In a survey done of 1500 midsize companies, only 14% of data on average was labelled as “business-critical”. This means that companies are paying to store and analyze data that isn’t being used. It’d mindboggling to think that leveraging data can actually hurt the operations when executed incorrectly, but any modeling or analysis based on inaccurate data will result in a major headache with no real benefits.
Secondly, remember that patience is a virtue. Business strategy is often complex and using data-driven analysis to inform strategy necessitates a high level of precision and thought. While data can be incredibly valuable in predictive analysis, this is often a long-term solution that needs time and patience in order to yield valuable results. With patience and careful planning, strategic planning can be vastly improved by a data-driven analysis system.
At Bricz, we are passionate about providing our clients with the information and tools necessary to make well-informed strategic decisions about the future of their companies. If you are interested in learning more about how we think about leveraging data and strategic planning, please reach out to us at info@bricz.com!
Contributors: Jimmy Kwon & Katherine Bell