We hear a lot these days about how analytics can accelerate a calculation that used to take ten hours and do it in six minutes. For me, this is a very poor argument for analytics. After all, is an analyst going to stare at a screen for ten hours waiting for a response? Of course not, she will be busy doing other things, so the time lag, or latency as it’s referred to these days, is not really an issue. However, if that bit of analysis is so timely that it needs to be done in six minutes, then that is probably a good case, but I doubt that it is that timely. A ten hour query was developed to solve a problem that did not need to be solved in six minutes.
There are many problems begging to be addressed with extremely fast response times, but my suspicion is that these are new solutions that couldn’t be done before. Hence, the time-saving pitch for existing analytics seems a little shallow.
There are great reasons for applying today’s amazing tools, however. A few years ago, I was engaged to solve a supply chain problem for a company that manufactured their products in Asia and shipped them via container to the US. The main warehouse was near Seattle and there were satellite warehouses across the US. My client was a Senior Vice President of Logistics with a nasty problem. Clients were unhappy because they were frequently out of stock. The existing solution was called (incorrectly) )the On the Water Report.
The report was a three-inch thick greenbar report that detailed all products ready for shipment at the plants in Thailand and Malaysia, product on ships (hence, on the water) in containers and inventory at the warehouses. My client would get the report once per week, scan the entire thing and combine, in his head, what he knew about orders, and highlight every instance where he felt a problem could occur. This took almost a full day of his time. When he was finished, the report would go to an analyst who would build an “issues” spreadsheet and from there, various people in the organization were alerted to potential problems.
The key word here is “alerted.” No solution was devised. The only response was damage control.
In my naïveté, I thought I would be able to design a system to skip the greenbar by eliciting his explicit and tacit knowledge, and generate the spreadsheet automatically, saving him a day a week, and another day of work for an analyst. Based on this, the project had a very positive ROI and nice corollary benefits, such as integrated data for many other uses.
His response when I presented the solution to him was, “Neil, you don’t get it, do you?” I have to admit that this wasn’t the first time in my consulting career I heard this. I was about to get a lesson about how things really work (For me, this is the best part of consulting, learning from experts how things really work and what is really important).
He said, “You can save a day a week of my time and day a week of an analyst’s time, but that isn’t going to mean a damned thing in the greater scheme of things. Here is where you can really do some good. Save this company the expense of sending a helicopter to a ship at sea to break open a container to satisfy a major client. Save our sales force the time they spend on the phone apologizing for missed shipments and for putting clients on allocation because we can’t get the products to the right place at the right time, and repurpose that time getting them in front of customers in a good mood and selling them things.”
That’s what we set out to do, to build an optimizing system linking sales forecasts, contract compliance, manufacturing and transportation. In uncharacteristically candid disclosure for a consultant, I regret to say the project wasn’t a success. Senior management got involved in a scandal, business deteriorated and a very injured company was sold and largely disappeared. But the lesson for me was clear.
The moral of this story is that informing people with analytics isn’t worth a bucket of spit (as they say in Texas) if you can’t take it all the way to presenting a solution. Making things go faster, or “saving time” of professional staff is not a very compelling proposition. Changing a process to provide better service to customers, making an entire sales force more productive or fine-tuning manufacturing forecasts are.