The Next Step for Enterprises: Optimization Transformation
Author: Gregory Glockner, PhD
As we begin to recover from the global pandemic that has upended the business world over the past year, many organizations are:
- Taking a look at the external business landscape (which is characterized by immense complexity and uncertainty) as well as their own internal business strategies and practices,
- Taking stock of the technological tools they have at their disposal, and
- Taking the necessary steps to transform themselves – so that they are positioned for growth in the post-pandemic era.
The question is: Which technologies do companies need in order to transform themselves so that they can survive and succeed in today’s challenging business environment?
Data and analytics technologies are poised to play a major role in today’s digital transformation projects. According to a recent survey by Gartner, 72% of data and analytics stakeholders are leading or heavily involved in digital transformation initiatives.
One data-driven, advanced analytics technology that has been and continues to be a pivotal tool for cultivating digital transformation is mathematical optimization.
A powerful prescriptive analytics software technology that was first introduced over 70 years ago, mathematical optimization gives enterprises the capability to unlock the value of their data by utilizing it to:
- Optimize their most critical business processes such as production planning, workforce management, and financial portfolio allocation.
- Make the best possible decisions to achieve their business goals such as minimizing costs and maximizing efficiency and revenue growth.
- Transform their operations by enabling optimal and automated decision making and execution.
In this article, we will explore the transformative impact of mathematical optimization across various industries and across all levels and functions of the organization, and explain why this technology remains an essential tool for enterprises today.
Driving Transformation across Industries
Mathematical optimization has made its mark across more than 40 industries, as this prescriptive analytics technology has been deployed by enterprises – in a wide variety of off-the-shelf and custom-built applications – to boost the efficiency and effectiveness of highly complex, high-stakes business processes and achieve improved business outcomes.
There are some industries, though, where this prescriptive analytics technology has had a particularly profound effect and has become a key driver of industry-wide transformation. Broadly speaking, these industries can be divided into two categories:
- Established industries: Over the past few decades, mathematical optimization has become an essential element embedded into the day-to-day operational fabric of established industries including automotive manufacturing, airlines, and logistics. Leading companies in these industries rely on mathematical optimization, utilizing it to manage their critical resources (such as aircraft and crew, assembly lines and spare parts, and trucks and drivers) in the most efficient manner possible – so that they can consistently deliver products and services to their customers and profits to their shareholders.
- Emerging industries: Mathematical optimization helps keep the operations of many different disruptive industries – such as e-commerce and ride hailing – running. All the primary players in the ride hailing space, for example, use this technology to automate and optimize their routing (matching riders to drivers in real-time), pricing (determining how much to charge for rides based on demand and other factors), and many other crucial processes. It’s not an exaggeration to say that mathematical optimization is helping to transform the industries that are transforming the business world.
The truth is that many established and emerging industries simply would not be able to operate – on the same scale and with the same productivity as they do today – without mathematical optimization. This technology is used ubiquitously in these industries, and continues to be a vital catalyst of digital transformation, business agility, and revenue growth.
Empowering Transformation across the Enterprise
In order to understand the full impact of mathematical optimization as a technology of transformation, we need to take a look inside the enterprise – to see how mathematical optimization has revolutionized operations across functions and levels of the organization.
As a prescriptive analytics technology, mathematical optimization excels in fostering greater integration, automation, and optimization in the domain of decision making.
This makes mathematical optimization particularly relevant in today’s complex and ever-changing business landscape, where making decisions is arguably harder than ever.
Indeed, today’s executives state that around 65% of the decisions they make are more complex – involving more stakeholders and choices – than they were two years ago, according to a recent survey by Gartner.
Mathematical optimization helps organizations conquer this complexity by empowering them to transform their decision-making process across various time horizons:
-Strategic: Mathematical optimization enables key stakeholders across the enterprise – from the boardroom to the back office, the field, and the shop floor – to:
- Explore numerous what-if scenarios,
- Evaluate long-term risks and opportunities across the organization’s operational network, and
- Generate optimal strategic plans and make optimal strategic decisions (that are aligned with their corporate objectives).
Automotive manufacturers, for example, use mathematical optimization to facilitate crucial strategic decisions about capital investments (such as whether to open up new production plants, retool existing ones or close some down), product portfolio (such as when to introduce new models, and where and when to produce them), and supplier selection (such as which global suppliers to choose to provide spare parts) – and in other areas as well.
In other industries (including airlines, logistics, ride hailing, and many others) companies use mathematical optimization to conduct their long-term planning, where they create and evaluate various scenarios for supply, demand, inventory, investment, and more, and make optimal strategic decisions that determine the future direction of their businesses.
-Tactical: Companies also utilize mathematical optimization to fuel optimal tactical-level planning, scheduling, and decision making. Mathematical optimization enables businesses to automatically and optimally align supply, capacity, and inventory with demand over the medium-term time horizon.
Airlines, for instance, utilize mathematical optimization to automatically produce monthly schedules for their aircraft and crew (based on forecasted demand) and make decisions on which flights to offer, at which prices – so that they can maximize on-time performance, resource utilization, and revenue growth, while minimizing operating costs.
Automotive manufacturers rely on mathematical optimization to generate medium-term production, sourcing, inventory, and distribution plans and schedules and guide tactical-level decisions in areas such as product mix, and capacity and inventory management, and manufacturing operations.
And mathematical optimization helps ride hailing companies make the best possible tactical-level decisions on whether to enter new markets or recruit new drivers, taking into account forecasted demand and other factors.
-Operational: To successfully navigate today’s constantly shifting business landscape, companies must be able to react and respond efficiently to changing conditions and disruptions. Mathematical optimization gives enterprises the visibility and agility they need to preserve ongoing business continuity and profitability because it:
- Runs on the latest available data from across each company’s operational network.
- Incorporates a detailed model (or digital twin) of each company’s current business environment.
- Rapidly delivers optimal solutions to each enterprise’s present-day, mission-critical operational challenges.
- Empowers key stakeholders to make optimal, real-time, data-driven decisions.
In the logistics industry, for example, mathematical optimization is used to automate real-time routing and dispatching decisions – to ensure the on-time delivery of goods and the optimal utilization of resources such as trucks and drivers.
Airlines – who face everyday delays and disruptions due to weather, aircraft mechanical issues, or other factors – use mathematical optimization in real-time to dynamically reoptimize their schedules, manage recovery operations, and make the best decisions on how to redeploy their crew and aircraft.
Across industries, mathematical optimization enables enterprises to achieve “continuous intelligence” by integrating prescriptive analytics into their day-to-day operations and optimizing their real-time decision making and execution.
The Transformation Journey Continues
The pandemic has been a “tipping point” for digital transformation, according to a survey by McKinsey, which showed that COVID-19 has accelerated companies’ adoption of digital technologies by three to four years.
This trend holds true in the mathematical optimization software space, as we’ve experienced a recent surge in demand for this prescriptive analytics technology – which continues to be such an important driver of digital transformation across the organization.
We’ve witnessed countless companies undergoing an “optimization transformation” – not only in terms of business outcomes (by fueling greater efficiency, resilience, and revenue growth), but also in terms of business processes (by facilitating integration, automation, and optimization in their decision making and execution).
Now more than ever, we’re seeing organizations – as they try to adapt to the “new normal” in today’s volatile and complex business world – apply this prescriptive analytics tool to power enterprise-wide digital transformation, decision optimization, and competitive advantage.
A version of this article was originally published on RTInsights here.