SIOPS and the Gulf Oil spill

I have been lucky in more than one of my previous lives to be involved in the creation or overhaul of several Sales inventory Operations Planning Systems (SIOPS) I love being involved in this activity as it is what I like to do best; work with complex puzzles involving both processes and people.

Recently I saw a discussion posted on LinkedIn from a consultant asking if anyone has used SIOPS type tools to forecast the ability to meet a month end target. Clearly this guy had never been in a line management role in his life. After making a mental note never to engage this company's services, I pondered how many real deliverables are expected from this process.

Sr. Management expects this process to both protect the company and to optimize both revenue and inventory. However they are generally not close to the real compromises and risks taken in the process. The detail required to really understand the process is really not available to them, usually by design. They really have to take the recommendations they are given in most cases.

The real operating managers, people in Sales, Manufacturing and Supply Chain, have grown up in the pressure cooker of competing goals and objectives, insufficient resources and crazy time constraints. They are used to working in this environment and usually do an amazing job of keeping the race car in the lead of the race while overhauling it. SIOPS to them is the dashboard, Ouija board and a place to find whatever wiggle room is left after everything has been "optimized".

Folks at the working level expect this process to provide them with a workable plan. Given the dynamic nature of most operations, the plan agreed to does not generally survive intact too long, and they find themselves reacting to what is coming up in their e-mail in boxes, making decisions sometimes at odds with the plan.

This (finally) brings me to the Gulf spill. I keep imagining this same sort of a dynamic. Senior managers looking at data at a macro level, do not consider the crazy statistical outlier that is what happened in this case, and push on a macro level to reduce costs putting processes and incentives in place to accomplish this

From here, operating managers to try to save money on inspections, with all their data showing that this type of spill had never happened, and the models show risks are low (though consequences catastrophic) They find a way to keep hitting their targets, more with less, what they get paid to do…

Our lesson? Let remember that the models are just that and that they are still really just tools to aid human judgment.