It used to be that taking advantage of optimization techniques, whether mathematical or otherwise, was difficult. It required significant resources to properly integrate and take advantage of business data.
The data integration in top logistics companies, including those leading the commercial aviation space, during the 90s, paved the way for implementation of new optimization technology.
Yet with new cloud technology, it’s easier than ever to start gathering data, monitoring your activities, and analyzing your data.
This is the first step towards taking advantage of optimization technology.
Why Manage Data Well?
Intuitively, we know we should have well-managed data. But what does that really mean?
Often, to convince managers and higher-ups, you need to be able to make a business case for dedicating resources to managing data better.
Business Analytics & Management
The most obvious reason for managing data well is that you need to know what’s going on in your business. You wouldn’t think about operating a business without a balance sheet and a budget, right? Well, that’s data management.
But what actually contributes to that budget and balance sheet? The operating parameters of the business are what ultimately determine the business success, and tracking these parameters is important to ensure your business is healthy.
A basic example is a logistics business delivering cargo. If they aren’t tracking how much cargo they’re transferring, how do they know if it’s enough to be profitable? They need to know what percentage of capacity they’re operating at to make sure it’s enough, to know when they need to start looking at purchasing excess capacity, or when they need to spend more marketing dollars to fill their capacity.
It’s impossible to know how well your business is doing without tracking and managing data well.
Systematic Decision Making (and Managing Expectations)
How do you make decisions in your business? By feel? By intuition?
You’d be surprised how many do. Often, the decisions aren’t bad. But opaque decision making like this has consequences.
First, it’s impossible to implement processes and standardization - necessary for scaling - when there is no consistent basis for making decisions.
One manager may make a different decision than another manager when faced with the exact same situation. When this process isn’t standard, and these decisions aren’t tracked, it becomes impossible to improve.
Making decisions based on data solves this problem.
Making decisions based on data makes it easy to manage expectations, and modify processes when they don’t work.
An common example we encounter is employees who are dissatisfied with their working schedule. They feel their schedule is unfair, and worse than others.
In reality, their schedule is often better than average - the problem was, none knew what ‘average’ was.
Tracking key metrics around the ‘quality’ of schedules in this situation allows the manager to show the employee what an ‘average’ schedule is, and then how theirs compares. It allows the manager to manage the expectations of that employee, and it gives the employee peace of mind.
Better yet, if their schedule is determined using a standardized process - for example, if you have a worse schedule than average this month, you’ll have one better than average next month - they know they are being treated fairly.
Reduction of Team Conflict
Related to the above example, when there is a standardized process in place, the decision-making process is transparent. There’s no one to blame except those who developed the process. Employees and scheduling teams face less friction when there are clear data points and processes surrounding decision making.
It also gives a starting point for future negotiations or improvements. If there are still large issues with employee satisfaction, the process can be modified to improve that aspect of the process. But good data and processes are the starting point.
Reducing Vulnerability to Change
What happens when your favourite scheduler or manager quits? Or retires? We’ve referred to them as the “Magicians” in logistics businesses.
In short, they’re the experts everyone relies on in the business. They have a lot of accumulated knowledge and experience, and they are who everyone turns to when they have problems.
However, if data and processes aren’t in place for systematic decision making and planning in a logistics business, when someone like this leaves, it causes big problems.
All of a sudden there’s no real basis for decisions, and those who step into the void are merely trying to replicate what they *perceived* the previous person did, and not actually working from first principles to make decisions.
If you have proper data management practices in place, and processes for systematic decision-making based on that data, business disruptions like the departure of key personnel cause far fewer problems.
Convinced you need to manage your data well? In our next post, we talk a bit about some common data management issues we see with our logistics customers.