In our second blog we considered planning theory as a unique problem solving domain within contexts like supply chain planning, so now let’s compare and contrast planning and scheduling, to better understand how they work together.
In most every plan, like a manufacturing plan, we’re following a series of steps, tasks following one after another, where the sequence is driven by interdependencies. But when we have some flexibility and certain sequences are much better than others, then we have a separate scheduling problem within our planning activity that deserves special attention.
The scheduling process considers relatively small subsets of tasks within the initial part of our overall plan, like the first few shifts of our manufacturing plan, considering much more detail and using different measurements (KPIs) to generate efficient task sequences. It requires a completely different skill set and supporting capability than the planning process.
For example, say we have some tasks lined up on the production floor and hourly employees staffed to do them; each job has multiple steps that can be done in any order by anyone, but our staff has varying pay grades and skill sets, each being much more efficient at certain kinds of work, and we need to pay time and a half for any hours over 40 per week. The boss wants all jobs done on time in the least expensive way.
We start by planning, sequencing jobs by due date and estimating how many staff we need each week. In the first week we plan four people to do five jobs, where each job has three steps. Then our amazing scheduling team starts evaluating a billion schedules per second, trying to find the least expensive schedule for us. How long will this take? Two days? A week? How about … 45 million years!* Not gonna happen!
Of course, every type of scheduling scenario has its own unique challenges, but we know in general that scheduling problems are notoriously difficult, in an altogether different space than planning. Scheduling difficulty is often independent of planning complexity, and varies enormously by problem type and size. We need good answers fast, not perfect answers. This can usually be accomplished using common-sense rules of thumb to simplify problems and very specialized optimization applications to do the calculations for us.
And we can’t forget that every scheduling problem is embedded within some kind of planning problem, and that the inputs to our scheduling problems come from our plans. As Sanjiv Sidhu, co-founder of i2 Technologies, once said: “A bad schedule from a good plan is better than a great schedule from a bad plan.”