By Ken Koenemann, Nathan Goldstein
During employee team meetings, the CEO of one of our clients would always make a point of reviewing how the company made money. “We take a bunch of random raw materials,” he would say, “and convert them into something we can sell for more than what it costs to make. What’s left over is called profit. We’re in business to make more of that stuff called profit.”
It’s a useful lesson. Too many capital investments and improvement activities, while promising dramatic productivity improvements and hundreds of thousands of dollars in cost savings, fail to make more of “that stuff called profit.”
A classic example of such tradeoffs is the push to maximize production output and asset utilization without really understanding if it’s worth it to A) run a machine with overtime labor, or B) produce more or less of a certain product type or configuration. This can create inventory headaches – having too much of what customers don’t want, and not enough of what they do want – and reduce cash flow.
From a solutions perspective, the challenge has always been that there are too many variables and not enough data to balance the process and functional tradeoffs for both short- and long-range decisions. Prescriptive analytics models, combined with today’s more available and better quality data, can provide that visibility. These solutions are already helping manufacturing business leaders and managers make decisions that optimize performance and profits — not just within their functional areas, but across their companies.
In our previous blog post on the topic of prescriptive analytics, we explained what prescriptive analytics is, why potential applications for manufacturers are expanding, and some of the challenges that these solutions can help solve, specifically as it pertains to supply chain management. In this post, we’re going to explore these challenges in more depth, and go into some more detail with a case study from River Logic, one of our new solutions partners.*
To reiterate,
Prescriptive analytics models can be used to make long-term decisions, such as capacity planning related to capital expenditures or long-term risk management. Medium-term models can optimize inventory, distribution and production strategies. And day-to-day models can help set production sequences and shift schedules, as well as material handling and truck-loading patterns.
Here’s an example of the profit-enhancing opportunities of prescription analytics. Based in Orangeburg, South Carolina, Cox Industries has 15 manufacturing facilities in eight states. The company employs some 400 people who make wood utility poles and marine construction products.
Cox uses prescriptive analytics for end-to-end planning in procurement, sourcing, demand management, inventory management and logistics. The analytical model they’ve created not only has drastically improved the company’s financial planning and forecasting processes, it’s helped them understand how to better run their operations to meet their financial goals. Better visibility into what’s happening and what could happen to demand has helped the organization prepare for and respond quickly to both regulatory and market changes.
Cox also uses prescriptive analytics to evaluate customer bids and capital investment decisions. For example, the company always did some log-peeling in house, roughly 25% of the logs it needed. It purchased its remaining requirements from outside vendors. Prescriptive analytics (specifically, optimization in the form of linear programming) revealed that the company could peel some profiles much cheaper than their outside vendors. Today, the company peels over 80% of its volume needs in house, a switch that increased the profitability of this one business unit by over 3% of their annual revenue. Additionally, Cox was able to institutionalize these decision-support practices while increasing cross-functional collaboration, agility and predictability.
In summary, Cox used prescriptive analytics to support an integrated planning process that brought them:
Production allocation, customer bid support and demand shaping are just a few things that can be drastically improved through the application of prescriptive analytics. Finding answers to questions around cross-functional tradeoffs are the conversations that manufacturing leaders should be having. That’s how you can make more of “that stuff called profit.”
* The case study in this blog post comes courtesy of River Logic, a new solutions partner for TBM Consulting Group. We have always pushed our clients to think differently about their businesses, helping them uncover new opportunities to improve performance and profitability. While many solutions vendors can model operational data, we are impressed by River Logic’s easy-to-use interface and blend of operations and financial insights to help our clients make better and faster decisions. You can request a more detailed version of this case study from River Logic.
Private Equity Operational Due Diligence + Value Creation
Operational Excellence
Don’t miss industry expert insights.
Join a community committed to excellence.