5 Small Variables That Cause Big Losses in Operations

A production line just stopped, and it won’t start back up for 11 minutes. Luckily, the issue isn’t too big; it’s just a sticky valve that was supposed to be oiled last Tuesday, but someone forgot to do it. But those 11 minutes translate into USD 2,200 in lost labor and late fees.

Turns out the issue is big after all.

Nobody's going to put that sticky valve in the report because it's just a valve, plus it's too much of a hassle. And nobody will DEFINITELY take blame for it. Things just happen; it's part of the business.

Let's move on. Right?

Well, the problem here is that a couple of days later, a worker will put the wrong label on a pallet, and then the next day, a handoff will happen 5 minutes later than it should. But while all that's happening, yesterday's inventory is still showing up in the system.

These are all small mishaps on their own, but when you add them together?

It’s a ton of wasted time and money.

Things That Seem Small, But End Up Costing a Lot

One small operation miss doesn't hurt that badly, but come on.

When have you ever heard of just a single one happening? They tend to pile up over time, and they cost a lot of money.

The Timing Being Slightly Off

It doesn’t matter that you made the right decision if it happened too early or too late.

If the peak of demand has passed and your stock just arrived, you’ll have full shelves and no customers. If you schedule deliveries during insane traffic, what was supposed to be a 20-minute route will turn into an hour.

Timing is something you need to adjust all the time, and you have to base the decisions on what’s happening right now, not what the schedule said 6 weeks ago.

Same Area, Different Results

There could be two stores in the same city, and their performance could be different as night and day. It’s a matter of neighborhood; a store close to a school that closes at 3 P.M. can’t have the same amount of traffic as one that’s close to an office park that can be full until the evening.

The point is that you can’t treat every area the same because, if you do, your inventory will end up in the wrong places, and the logistics will waste fuel.

Even a small difference in location can change the outcome way more than you’d expect.

Weather Turns Small Issues into Huge Problems

It’s not that the weather itself will cause a disaster, unless it’s extreme.

But for the most part, the weather takes small problems that were already there and makes them a whole lot worse. If the day is nice and sunny and you make an error in timing that costs you 10 minutes, it’s not the end of the world. But that same error would cost you 2 hours during a storm, and that would most likely result in some serious damage.

In order to lower this risk, it helps to use tools that retrieve current conditions with forecast data.

That way, it’ll be easier to make tweaks before things escalate.

People Don’t Behave as the Model Expects

Predictive models are extremely useful, and they do a great job at spotting patterns.

But that’s about it. People are quick to change those patterns without any warning, so it’s not unusual for a forecast to drastically change on a weekly basis. And the model can’t see it coming because it doesn’t have a crystal ball to predict the future.

Systems can’t react as fast as behavior can change, and that can end up being really expensive.

Data is Good, But It isn’t Fresh

If you have accurate data from last week, that’s good, but it doesn’t necessarily mean it’ll help you make better decisions about today or next week. What sense would there be in using last week’s sales numbers to decide how much product you’ll put on the floor this weekend? Those numbers might be correct, but they’re stale.

That’s not to say that past data can’t help to a certain extent, because it definitely can, but you have to consider how old it is. One stale number could cause you to make a wrong move, so now you’re stuck with more problems than you had when you started.

Accuracy is important, but speed matters just as much (if not more sometimes).

Conclusion

Most of the time, those big, dramatic losses that sink a company in a minute happen in movies.

Yes, there have been some cases of that happening in the real world, but it’s not that common. The bigger issue is small mistakes that stack up until you can feel the consequences. And by the time you can feel it, you’re already in deep trouble.

So, how do you fix this? Easy. Pay attention.

Check if the timing is off by a few minutes, and if it is, see why, and make sure it doesn’t happen too often. Use data that’s as fresh as possible when making decisions, and treat past data as exactly what it is – a thing of the past.

It can still be useful, of course, but it’s not a be-all end-all type of information.