What to Run Next: The Hidden Decision That Quietly Determines Your Shop's Throughput

Sol Solutions Consulting | May 2026

Every Monday morning at every job shop in the country, the same conversation happens. The owner, the foreman, and the scheduler stare at a board with twelve open orders and three machines, and somebody asks the question that has to be answered before anyone touches a tool: what runs first?

Usually the answer is some version of "whatever's oldest," "whatever's due soonest," or "whatever the customer just called about." The decision takes thirty seconds and the floor moves on. But that decision, repeated week after week across a year, quietly determines how much money the shop makes.

There's a whole field of math dedicated to this exact question. It's called scheduling theory, and it's been around since the 1950s. Computer scientists use it to decide which programs run on a processor. Hospitals use it to triage patients. Airlines use it to sequence flights through congested airspace. Same underlying problem in all of them: given a queue and limited capacity, what order should I do things in? People much smarter than whoever's running your Monday morning meeting have been studying it for seventy years.

Here's the interesting part. Scheduling theory doesn't give you one answer. It gives you a different answer depending on what you're trying to optimize for. And most shops have never explicitly said what they're trying to optimize for.

Three Strategies, Three Different Outcomes

The first is Earliest Due Date, or EDD. Sequence by delivery date, soonest first. This minimizes your maximum lateness. If a job is going to be late, it won't be as late as it would've been any other way. For shops where late penalties hurt or one blown delivery sinks a customer relationship, EDD is mathematically the right answer. Most shops already run this way without knowing the algorithm has a name.

The second is Shortest Processing Time, or SPT. Sequence by how long each job takes, shortest first. This minimizes the average time jobs sit in your queue. If your first job takes ten days, every other job is also waiting at least ten days before you start on it. Run the short stuff first and you clear customers off your floor faster. Average lead time across all customers drops. The trade-off is that some long, important job might wait longer than it would have. But if your priority is "more customers feel well-served per quarter," SPT beats EDD.

The third is Weighted Shortest Processing Time. Same idea as SPT, but you adjust for the fact that not all jobs are equally important. Divide each job's weight (revenue, strategic value, the customer behind it) by its processing time. Highest ratio runs first. This is the algorithm closest to what experienced operators are already doing in their head, except they're doing it imprecisely and often weighting the wrong things. The customer who called most recently tends to get prioritized over the customer who actually matters most.

Three algorithms, three sequences, three different outcomes. The "right" one depends entirely on what you're optimizing for, and most shops have never made that choice explicit. They have a sequencing habit inherited from whoever ran the floor before, and that habit chooses for them.

Ask ten manufacturers their sequencing priority and eight will say "we run by due date." Ask why and they'll tell you it's about keeping customers happy. But "keeping customers happy" isn't the same thing as "minimizing maximum lateness." It might mean "keep average lead time short," which is SPT. It might mean "keep our biggest customers happiest," which is weighted SPT. The English-language priority is fuzzy. The algorithm forces precision.

The Thrashing Problem

There's a real-world wrinkle the textbook version leaves out. Pure SPT assumes setup costs are zero. If you switch between five jobs and each switch costs an hour of changeover, you've just burned five hours of throughput on transitions. In a real shop, the right answer is rarely pure SPT. It's SPT within clusters of similar work. Group jobs that share tooling and setup, then sequence shortest first inside each group.

Computer scientists call this context switching overhead. A processor that constantly stops one task to start another wastes most of its time on transitions. So does a shop floor. Group, then sequence.

What This Actually Looks Like in Practice

None of this requires a Gantt chart, an OR PhD, or a six-figure manufacturing execution system. It requires three things, in order.

First, a written statement of what your shop is actually optimizing for. One sentence. "Minimize maximum lateness." "Minimize average lead time, weighted by customer revenue." "Maximize throughput within setup clusters." Whatever it is, it should be a deliberate answer to "what are we trying to do," and most shops don't have that written down anywhere.

Second, a simple sequencing rule that matches the priority. EDD if your answer is about lateness. Weighted SPT if it's about customer-weighted throughput. Cluster-then-SPT if changeover costs dominate. The rule should fit on a notecard.

Third, a weekly cadence where somebody (the floor manager, the scheduler, the owner) actually applies the rule instead of going by gut. That's the whole system. The rule does the thinking, the human follows it, and the decision that used to take thirty seconds of folklore now takes ten minutes of arithmetic. Across a year, the output is materially different.

We've seen this kind of change produce 15%-20% throughput gains without a single new machine or a single new hire. Same equipment, same crew, same customer mix. Different sequencing rule.

Why This Is Harder Than It Looks

The reason most shops don't do this isn't that the math is difficult. The math fits in a paragraph. The first step is what's uncomfortable: explicitly stating what your shop is optimizing for. It forces a strategic decision that's been hiding inside an operational habit for years, and it often exposes that the shop has been quietly optimizing for the wrong thing, or for nothing in particular.

That's a hard conversation to have internally. The answer that usually comes back is "we've always done it this way" or "the customers expect it." Both are answers about the past, not the future.

Find Out What Your Shop Is Actually Optimizing For

Most operations problems look like operations problems on the surface and turn out to be strategy problems underneath. Sequencing is one of the clearest examples. The throughput gain is real, but it only shows up after the strategic question gets answered honestly.

That's what our initial consultation is built for. We spend time with your team understanding your current sequencing rule, whether or not it's been written down, what the floor is actually optimizing for in practice, and where the gap is between that and what the business needs. You walk away with a written assessment of your highest-impact opportunities, whether that's sequencing, automation, workflow design, or something else entirely.