Interview: Understanding the Business Value of Mathematical Optimization

Date: 5/7/2020

The mathematical optimization software industry is booming, as an ever-increasing number of organizations are deciding to invest in and implement mathematical optimization tools.

An AI technique for decision making, mathematical optimization is used today by leading companies – around the world and across numerous industries – to rapidly solve their complex business problems and make optimal business decisions that maximize their operational efficiency.

There are many companies out there that are considering using mathematical optimization, but are still wondering:

Is mathematical optimization the right solution for my company?
What business benefits will mathematical optimization deliver?

Indeed, it is imperative that – before making the decision to invest in a mathematical optimization solution – companies understand the business case for using mathematical optimization and the business value that it can bring.

In this interview, we speak with Dr. Evan Shellshear – Head of Analytics at Biarri (which is a Gurobi Premier Partner) – to find out about the signs that mathematical optimization would be a good fit for your company and also learn about the business benefits of implementing a mathematical optimization software solution.

Gurobi (G): How do you know if mathematical optimization is the right tool for your company?

Evan Shellshear (ES): Many companies today are looking to achieve an AI-driven digital transformation. One of the key elements of this involves optimizing the processes that they use to make decisions like scheduling and ordering and prioritizing things. Often, they are handling these processes using Excel sheets or via manual extracts from databases. When these processes and business problems reach a certain size – if people are having to schedule and prioritize and list and rank thousands of things every single time over a given time period – then we know there’s a good case for optimization and we know we can do it a lot more efficiently.

Interestingly, we noticed that optimization is not necessarily a good fit for all companies. In the construction industry, for example, companies are essentially trying to optimally schedule and carry out a certain set of activities. This seems like it would be a good case for optimization, but the issue is that some construction companies have not reached the Excel stage yet – they are still working with pen and paper. And if they haven’t at least taken the first step to recording the information digitally in a consistent format, then we realize that – in order for them to carry out optimization – a level of basic digital transformation first needs to occur.

G: So, in order for a company to be able to successfully implement a mathematical optimization solution, they need to have access to accurate data?

ES: Absolutely. One area where optimization is booming is healthcare – as companies in that space are very meticulous about the data they collect. I guess the challenge for healthcare companies is that although they collect a lot of data, very few hospitals and health service providers have bothered to go beyond that. Often, they are looking to figure out how to diagnose a patient based on long historic longitudinal data sets they have on patients and presentations that they have had at general practitioners or in an emergency department. They’re trying to figure out how should we diagnose someone with AI, and that’s very difficult because it requires natural language processing and a large database of co-morbidities and other issues that the individuals have and often this information is not stored in a consistent format – which is essential for easy use of AI.

That’s one extreme end of the spectrum of optimization use cases in the healthcare industry, but on the other end, many healthcare companies use optimization to manage their operations. In the operations arena, healthcare companies typically have simple dashboards and there’s lots of data and it’s usually of a high-quality nature. With an optimization engine, you can – for example – optimally allocate patients and resources throughout a hospital. This is a case where optimization can deliver significant benefits because the reliable data exists, but many healthcare providers are trying to process this data (in huge amounts) using Excel sheets.

G: Besides data availability and quality, what other essential elements does a company need to have before they can start using mathematical optimization technologies?

ES: A champion. You need a champion in the organization who has a data-driven approach to solving problems and sees and understands the value of using an automated optimization tool. Also, to implement such a tool, you usually need a front end for people to interact with. There is a reasonable step up from just a human being managing a business process in Excel or in databases to a fully automated tool that has a database capability, an optimization engine, has a user-friendly interface and actually now starts solving people’s problems.

G: What are some of the common signs that indicate that a company needs a mathematical optimization solution?

ES: Risk is a big one. Companies that have significant risk profiles or operate in industries where risk can have catastrophic consequences. These are often industries where we see an advantage for companies using an optimization engine.

I’ll give you a good example of this from the healthcare industry: A couple of years ago, we were engaged by a hospital in Australia to assist them with managing their waiting lists. We looked at the problem, we understood that they had good data, they had the will, there was a champion in there, but things were being done in Excel. It ticked all the boxes for us to be able to turn this problem into something that an optimization engine could solve easily but most importantly there was significant risk if things went wrong, this was the catalyst to get the project off the ground, not the good data.

And the other thing that actually helped is that the system (and the problem that we solved) existed to a certain degree in isolation. It wasn’t a hugely complex system that required ten things to be solved with all sorts of chain link dependencies across it – so that if you fix one thing, you wouldn’t ever fully realize the benefit. So this was really an isolated system where what would happen is people who were sick and required surgery would submit a request to the hospital, the hospital would hand that request to a booking clerk who would then book it into a theatre schedule over multiple operating theaters over a four week period. And they would be looking at thousands of people. And so what we saw was this isolated opportunity to – instead of changing the entire hospital process – go in there, build an engine that assisted these booking clerks every single evening, ran through everything, did a calculation and gave them those schedules and theater lists that they could then use for the next day to assist with managing their patients and assist with putting patients in slots instead of building those lists from scratch.

So now they had a leg up and were able to build these much-improved lists that showed double digit improvements in utilization, that showed significantly more patients going through, but – most importantly – helped them manage their risk. And their risk, in this situation, was the fact that they would not deliver critical care to terminally ill patients within the promised timelines. The optimization engine was able to assist them with mitigating that risk of not delivering healthcare in a timely fashion.

And it’s often in scenarios like this where there exists that type of risk that we’re seeing significant benefits. For example, in workforce rostering, there are risks around people violating fatigue rules and violating enterprise bargaining agreements and things like that. And we’re able to go in there and provide that value with optimization tools.

In fact, one of the big things that people forget is that optimization tools do not only provide value and efficiency gains on the day-to-day operational level or the tactical level, they also help improve strategic decision making. Indeed, business executives can make much better strategic decisions based on the output of optimization technologies.

G: Let’s talk about the different industries where optimization is used. You mentioned healthcare, but what are some of the other industries where you see optimization really gaining traction?

ES: Basically, it’s the industries that fulfill those prerequisites I mentioned – where the data is there, and the benefits of using optimization are there. Retail is one of industries – there’s a wealth of data from all the point of sale transaction information that companies collect. Aviation is another industry where there’s tons of data, and aviation companies have been heavy users of optimization tools for a long time – and have realized significant business benefits.

G: How do you explain – in a simple and succinct manner – the benefits of using mathematical optimization tools?

ES: Optimization enables easier, faster, and better decisions. That’s it. Make better decisions and make them faster, make them easier, and make your organization more cost-effective. And, and the other big part about this too is by making better decisions, a flow-on effect is that you can unleash new growth within your organization.

G: Can you give us an example of an organization that has achieved this?

ES: There was an organization that we worked with for a few years ago – a company that managed the ground handling operations around Australia at different airports. So, if you hopped out of a plane, you’d see their teams grabbing your bags and then shipping it off to baggage collection. And the problem they had was wanting to more efficiently roster their staff between different airlines and gain efficiencies within their crew.

What we were able to do was to enable them to move staff around different airlines, unlock that growth potential, and gain significant efficiencies. Then, all of the sudden they were able to credibly out-compete their competitors on price because the optimization engine allowed them to more effectively share resources and also grow to the next phase because they were no longer bottlenecked by their ability to roster staff. Previously it used to take them somewhere between five to eight days to create the rosters, but – with the optimization engine – this was reduced to 15 minutes.

So, all of the sudden this ground handling company could explore new “what if” scenarios: What if we brought a new airport on? What if we added this new capability at this airport? And it just unlocked that growth.

With optimization, you can make easier, faster, and better decisions to unlock the next phase of your company’s growth.

G: Businesses around the world today are facing immense challenges due to the coronavirus pandemic. How can mathematical optimization technologies help companies across various industries address and overcome these challenges?

The best answer to this question is a story. One of our clients transports goods around the globe and is the biggest provider of their products globally and was enjoying good business until COVID-19 changed everything. When the coronavirus hit, suddenly orders that were on a ship halfway around the globe were being cancelled in the middle of the ocean. The management team were facing losses of tens of millions of dollars. Fortunately, they had an optimization tool, which allowed them to run a variety of optimal scenarios, split their goods up, reroute them somewhere else, sometimes in a new form of product, and protect millions of dollars in revenue. So far, they have managed to avoid any significant loss of revenue due to their ability to quickly react and reoptimize the sales of their goods. Their suite of optimization tools has been the single thing that has allowed them to weather this catastrophe while their competitors are facing bankruptcy.