The River Logic Blog

A Prescriptive-First Approach to Driving Business Impact with Analytics – Part I

Aug 22, 2017 Eric Kelso Prescriptive Analytics

Author’s comment: this is part 1 of 2 of a hypothetical case study. The story described below can be any company willing to consider using a Prescriptive Analytics approach to improve their decision-making abilities.

Little Rock Packaging’s mediocre financial results were not unexpected, but still disappointing. Eight straight quarters of declining margins and profitability had worn down the company’s executive management team.

CEO Ken Jackson—the 3rd-generation leader of the paperboard packaging company—and CFO Tim Lott, who was brought in a year earlier to help turn the company around, analyzed the company’s current Profit and Loss Statement, along with the last two years’ worth of financial metrics versus industry averages:

Metric Industry Avg. 2015 Q2 2015 Q3 2015 Q4 2016 Q1 2016 Q2 2016 Q3 2016 Q4 2017 Q1
Profit Margin 4.2 1.7 1.2 -0.5 -0.2 0.7 1.2 -0.3 0.2
Return on Equity 10 7.6 5.3 4.2 3.6 5.7 8.2 3.8 6.1
Debt to Equity 1.2 0.25 0.27 0.29 0.33 0.26 0.22 0.34 0.25
EPS Growth 5 Yr. 8.6 4.1 3.6 3.3 2.5 4.2 5.5 2.7 4.4

 

“Luckily”, said Ken, “at least our debt remains quite low. We don’t have to worry about high interest payments consuming our cash.”

“Yes”, Tim responds, “but we might not have to worry about it anyway if we don’t raise our stock price soon. I just got a call from our largest shareholder, FP Investments. They’ve just been offered $12 a share from DoubleG Ventures for their 20% stake in Little Rock.”

Both men knew precisely what that news meant. DoubleG Ventures, known for their leveraged acquisitions, typically acquired companies they deemed seriously undervalued, and then pursued strategies to get to a 10x return as quickly as possible.

Ken, charged with maintaining his family’s legacy, and owing an obligation to all the hard-working people of his company, knew he had to find a way to turn things around…and fast.

Little Rock Packaging: Company Profile

With eight plants and 30 distribution centers scattered across the United States, Canada and Mexico, the company is a mid-tier producer within the 500+ member North American paperboard packaging industry. A fortunate inheritance nearly 70 years earlier allowed Jeremiah Jackson, Ken’s grandfather, to purchase a small, existing packaging business in Little Rock, Arkansas.

Ken’s grandfather was a good businessman and a great real estate owner. The original plant’s deed included about 50 acres of property. Little Rock grew over time and now held the deed for six of the eight manufacturing plants, though all the DCs were leased. The value of the land alone was probably worth $500 million to real estate developers, a huge amount considering the company only had $700 million in revenue in all of 2016. It was likely these assets that DoubleG was after.

Unfortunately for Ken’s family, due to deaths, divorce and other reasons, the family no longer owned a majority of the voting shares and could not directly control the company’s future.

The Three-Year Cost-Cutting Plan

Beginning in early 2014, the company’s board and senior management team worked extremely hard implementing a 3-year turnaround plan. The plan required serious cost-cutting, including permanently letting go about 20% of the workforce; dropping 10% of the least profitable products; closing two inefficient manufacturing plants; and not renewing leases on 7 DCs.

Ken, the former CFO, and the rest of the executive management team seemed confident they could at least move the company in the right direction again. Yet, for all the change that occurred, the stock price barely budged upward: from $3 per share in January 2014 to $6.75 today—a far cry from the $28 per share price reached in 2005.

All Options on the Table

With an upcoming Board meeting in late May, just 9 weeks away, both Ken and Tim knew they faced a huge challenge. If DoubleG Ventures acquired FP Investments’ stake, then they’d likely purchase enough shares on the open market to demand several board seats, which they could then leverage to control the company.

But all the “easy” decisions had already been made and implemented. What to do next? Little Rock was too small, and the total packaging market too fragmented, to just raise prices substantially.

“Tim?” said Ken “Let’s list all the realistic strategic and tactical options we still have.”

“Great idea”, said Tim, who grabbed a marker at the white board and began writing. “Here’s our best opportunities”

  1. Streamline existing network
  2. Tweak product mix
  3. Trade with other packaging companies
  4. Acquire smaller competitors
  5. Merge with a larger rival

“Don’t forget selling to DoubleG Ventures”, added Ken.

“Yes, I suppose that would be option #6.”

“Ok”, said Ken, “it’s not like we haven’t considered most of these before. We closed plants and DCs the last 3 years, yet it still wasn’t enough to increase profits and raise our share price.”

“True”, said Tim, “it did help to stabilize the company and bought us some time. But those decisions were based on experience, intuition and some basic number crunching. Would we have made those same decisions if we had used something like mathematical science? I’m not so certain.”

Time for a New Approach

“Mathematical science?” asked Ken quizzically, “What are you talking about?”

Tim responded: “As part of my MBA program, I took a class on management science. It wasn’t science exactly—we didn’t blow things up.” (Ken laughs). “We learned that relationships between business data can often be described in mathematical terms. These relationships always exist; they just need defining.”

“What do you mean?” asked Ken.

“Well, what if we take the same data we’re using today to make decisions—raw material purchase costs and volumes, labor costs, manufacturing rates, shipping costs, forecasted demand, etc.—and relate them mathematically through a set of equations. Most importantly, we can then analyze all important decisions simultaneously using specialized algorithms designed for this purpose. Think if it as one big, complex story problem.”

“The point is”, Tim added, “it’s obvious that all facets of our business are inter-related. We don’t need a computer to tell us that. But the inherent complexities are too overwhelming for us to improve our planning without the aid of mathematics and computers. Very smart people in our company thought they could do it three years ago, but they could not!”

“If we allow these computer programs to guide us into making better decisions, it should improve profitability and our stock price. Of course, no computer is going to run our business, we will still need to make and execute all the important decisions.”

“OK, I’m starting to get it” Ken responded “Can you tell me a little more about this class you took? As you know, I was a philosophy major and didn’t spend much time in math class.”

“Sure, Tim added, “The mathematical technique we learned to solve this type of problem is called linear programming, or LP for short. I was surprised to learn the concepts were created decades ago and aided the Allies in World War II. But it wasn’t until the last few decades that computers and specialized software made the process of building and solving models easy enough, and affordable enough, for companies our size to use effectively.”

“I’ve heard of linear programming”, said Ken, “but I’ve never seen it used, and wouldn’t have a clue how to get started. Do we own any of this kind of software? Is there anyone on our staff who would know how to use it?”

“Well, no and no.” said Tim “My experience was using a Microsoft Excel add-in called Solver. It was easy to use and made learning fun, but we solved only small, textbook problems. It’s my understanding that using the Excel Solver for our situation would be difficult. To consider all the options we just listed, the size of the model will likely exceed Solver’s basic limitations. And, it could take weeks, even months, to create a model using some algebraic modeling language. Even then, it still might not be flexible enough to analyze these scenarios. We might want to consider more options too as really start digging into this thing.”

The First Step Forward

“I’ll tell you what”, Tim added, “I’m going to have lunch tomorrow with Ed Martin, a former classmate. Ed was far better in management science class than I was. I mostly enjoyed the financial aspect of it, but Ed really liked solving problems. He now works for a Top 5 consulting company and has spent his career helping clients to solve business problems using optimization. Many of Ed’s clients have faced similar decisions to what we’re facing now.”

“Great idea”, Ken responded, “Why don’t you ask Ed if he can give us a presentation? How long it would take to implement? What the technology is that he uses?”

“Be upfront with him, however. He must understand we are not a very technically-oriented company. We have a few business analysts who are Excel wizards, but our S&OP process is mostly talking and we don’t have any advanced decision-making tools. Heck, we don’t even do much statistical forecasting. Any approach he recommends must use whatever data we have today. And, since we need to make decisive recommendations to the Board in 9 weeks, there is no time for writing and debugging code. We’re simply not in a position to pay for a software development project—we need answers quickly, otherwise we’re probably going to have to start looking for new jobs.”

prescriptive-analytics-for-business-leaders-ebook

Written by Eric Kelso

Eric Kelso is Vice President of Product Management for Enterprise Optimizer at River Logic. He has over two decades experience building and implementing prescriptive analytics optimization software, and has successfully completed more than 50 major projects in the discrete, process manufacturing and consumer packaged goods industries.

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