Author: Duke Perrucci
Date: 9/29/2020

Over the past 25 years, I have worked for a wide variety of companies â€“ from Fortune 50 giants to small start-ups. The common thread that runs through all of these experiences is the immense challenge of driving consistent growth. Whether you are trying to grow the second largest soft drink brand in the world with a seemingly limitless budget or a nascent technology company that is struggling to conserve its series A funding, the business environment is tough as hell. Decision making in such a highly complex and competitive environment is fraught with peril â€“ in fact, according to a recent McKinsey survey, only 20% of executives say their organizations excel at decision making.

One of the main reasons why good decision making is so rare is that many organizations use intuition, rather than data and analytics, as the basis for their decisions. Fortunately, there are many data-driven, AI-based technologies â€“ such as mathematical optimization â€“ available today that help organizations make decisions that maximize their efficiency and profitability. Unfortunately, I didnâ€™t have access to mathematical optimization tools for most of my career â€“ and, in many instances, this resulted in lost opportunities and lackluster growth.

Thinking back on my career, I have identified three situations where having a mathematical optimization tool would have really saved my bacon:

### #1: Solving the Olive Oil Supply Problem

During my time as a brand manager at a multinational consumer products company, I was tasked with helping a struggling olive oil business. The main issue the olive oil business was facing was that access to raw material and prices simply fluctuated too much to maintain a healthy margin on the product. The raw materials for the oil were sourced from all the traditional olive-growing countries around the Mediterranean â€“ Italy, Spain, Tunisia, Greece, France, and Turkey. If, for example, a drought hit Tunisia or a plague of locusts swept through Spain, supply would plummet and raw material costs would skyrocket.

### #3: Creating the Perfect Media Plan

Without a doubt, this process could have been run with far greater efficiency and precision had we used mathematical optimization. With the objective of driving the lowest cost per conversion, we could have found the ideal mix of ad type, spend, and properties to deliver the optimal media plan to these customers. Digital advertising provides a brand with myriad options to build awareness and drive conversion, but finding the most efficient and effective mix without mathematical optimization is simply not possible.

### Seeing the Opportunities

As I look back on my career, I can see many other missed opportunities where â€“ because the companies I was working for didnâ€™t have an AI tool like mathematical optimization to help us make data-driven, optimal decisions â€“ we left money on the table or fell short of growth targets.

And now, as I work for the leading provider of mathematical optimization software and support, I can see so many opportunities for optimization in the business world and so many businesses out there today using this technology to reach new heights of efficiency and profitability.

Are you missing out on opportunities for growth by not using mathematical optimization technologies? I would encourage you to learn more about mathematical optimization â€“ so that you can begin to identify and explore possible applications of this AI technology in your organization.

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