'How This Newcomer Turned a 20-Year Bottleneck into a Streamlined Success'

Yeah, absolutely, so one of the first projects that I started with er straight out of university, so in, in my, in my um corporate role was um kind of a, so it was beyond my scope in the sense that I was, I eventually ended up managing people who were in the company, had worked at the company for almost 20 years. So that was in that way, beyond my scope. Now how did I get there? Well, the idea of this project was to reduce the, the bottleneck that was currently going on in the creative side. And what this was, was the lab, so in our lab, all, all process flows through the lab eventually. So any creations that go in, go on with the company, eventually, flow through to the lab, and the lab was the bottleneck, where we had essentially, Um, We, we were basically told that we were not allowed to um add another resource, so that was out of, out of the options, so we were left with just basically changing up the, the resources that we had. And reworking and and changing up the flows. So essentially what I did is I broke down a whole bunch of data, qualitative and quantitative, um, took the brief from the manager to understand what the problem was, and that was the problem I just explained. And then I started to kind of see the problem for myself, went into inspector mode. Uh, very quickly I found that actually we can do what we're doing with the resources we have, so in fact it's good, it's good news, we don't need a new resource. Uh, however, what this meant was managing 55 operators who were working at the company for 5 years and their manager who had been at the company for 20 years. So all of them to some degree averse, uh, averse to change. Um, and more, more importantly, averse to change from an individual who's literally just started at the company. However, of course, numbers don't lie, statistics, data, um, the data sets, even qualitative data from stakeholders outside of that team, um, were feeding me information, and it was that information that I used to make the decisions, uh, and the changes that I made. So that was certainly outside of kind of my comfort zone to start with. Um, but eventually we managed to make the changes, the, the, the manager was happy to do the changes once he saw the numbers that can happen, and the bottleneck that was no longer existed.

OK, so, so I was working at that point in, in the lab. So just to give you context, I, I worked across different functions in my first job. So, uh, almost like a, a trainee scheme. So 4 different functions, 6 month stints. Uh, one of those functions was to work in the lab, to basically help um with the, with the creations. Uh, any growth, we had some, um, expansion of the lab, so we also brought in some more equipment, and that was, I was also involved in that. Now, when I was involved in those small, smaller projects in the lab, and we started to think about bringing in a new resource, there was a lot of back and forth with the manager and the director that, you know, this is the budget, we can't add another resource. So it was at that point that I said, OK, well let's have a look at the data, and let's see how we can go about, you know, I is there room for change? Do we have any slack for ourselves that we can create by changing some process? And then that was when I took it on.

Yep.

So actually that, that, what you, what you just identified there was exactly the the way, because I, because I was to some degree free floating, um, where I was, you know, adding impact where I can. So I like to call it as kind of like a growth consultant, so like I was involved in so many projects across the board where I can add value. Um, so, how did I get buy-in? Well, exactly, I, I was kind of available to do the, to do the project and to add that value. And obviously through other projects or through my interaction with the team, um, also through my ask as well. So, you know, so, yeah, so through, through approaching the manager, saying to him like, I've, I've spent time on the, on the, on the shop floor, I've looked at the operators, I've watched the way they work, um, I've spoken to them, I've taken qualitative data from them. I think there's room for change, I think we can change something, I think that I understand where we can change and where we can look, er and then the project to go on with it.

OK, so the most important quantitative data, so the, the most important quantitative data that I looked at was how many requests were coming into the lab to start with. So, for example, to to exactly identify the bottleneck. How many requests were coming in, and then benchmarking that against our capacity. And how did I benchmark against capacity? Well, I literally went onto the shop floor and collected the data, because for this, the data wasn't hard, it wasn't um on the system, because this was just kind of ad hoc, it was just the operators were working with time, and we grew organically. So no manager actually ever took the time to see what is our capacity. Um, so that was the two things I took, and I just worked, worked those two. To work with each other. So what was it, what was, what was coming in?

Yep, so I, so I, I stood in with the operators, with a stopwatch, and literally timed, for example, the, the, the, the requests that are coming in, and then took all that data in, for example, sub-categorized it as well, so is this a, what style of request it is, because we also had 3 styles of requests. Um, and then they were broken down even more, um, and then all of the different tasks that they would do through the day were timed. Compiled, put together, and then compared as well, between the operators too, until eventually we had the dataset which would tell us, on average how much our capacity is, if we were to put all, all our operators together.

So I collected that for about A week and a half. Um, but every time I collect it, because it wasn't like a week and a half in full, so it was like a week and a half, and every time I collect I, I kind of tab in the start time, the end time, and the time of day as well, just because obviously at different times of the day you're getting different styles of data as well.