This is the third in a series of four articles reflecting on Brian Venis’s career as a business entrepreneur and sharing his experiences and insights from that journey. Please feel free to contact him to discuss the issues and challenges arising from your business.

When it comes to using financials for decision-making, most executives face a similar problem: the numbers represent things that happened in the past, but decision-making takes place in the present. In other words, when we read financial statements, we’re looking at a static, non-moving picture at one point of time. It’s locked in place, waiting for us to make sense of it, limiting our range of motion. Sure, we can use them to help us determine where to cut costs or interpret them to help us project forward (and we need to). But we also lack something important: what’s going on in the present.

In the manufacturing business, every day we were creating objects with hundreds of parts, often with thousands of pieces in motion at a given time. From various stages of preparation to different types of materials and styles of production, there were many touchpoints where things could happen and might go wrong. Trying to capture the financial implications of our production processes was difficult since every line item on any financial statement is merely a summary of multiple transactions to that point in time. It was clear that we were missing some important perspective if we were to develop an overall financial picture of operations. Early on, I went on a hunt to gain a more nuanced view of our production process: I needed a dynamic lens.

As a CPA, I had some clues on how to approach the problem. From my early days in accounting, I was taught to view financials like a storybook that captures what’s happening in the past and projects a view of the future. Whether it’s for financing, cash flow projections or strategic acquisitions, a clear financial statement is an abstracted representation of a complex multi-faceted machine. The skill of reading a financial statement comes from seeing each line item as a story of change. It’s about classifying your data in a way that allows you to see into the minutiae. This is especially true in a high growth period because costs are not necessarily linear. In a manufacturing environment, costs may well exceed revenues at the infancy of an expansion and an accurate interpretation of the financials depends on where one is along the project spectrum.

So that’s where I started. I was interested in knowing the cost of producing one piece rather than the cost of many. The microcosm of a single order, I thought, could tell the story of what was happening better than the trying to understand everything in aggregate.

It was fairly easy to calculate the material cost of a single part but calculating labour costs and allocating overhead to accurately reflect the size and complexity of each order was much more challenging. The issue was coming up with the appropriate metrics. What mix of numbers could help approximate what was happening on the shop floor? With tens of thousands of combinations, finding a singular number proved incredibly difficult.

At first, I tried using linear feet. By calculating a base labour/overhead rate of an object according to its length, I thought we could try to establish consistent and comparable measurement. Length, at least, was a uniform measure and all of our products and parts could be evaluated in those terms. Unfortunately, roller length alone did not capture the complexity of a final assembly.

The problem with measuring by length was that it didn’t reflect the variations in the diameter of our products. A given item could range from 1” to 27”. It also didn’t reflect the fact that the work to make a 1” product was at least twice as much as the work to make that was 6” in diameter. Nor did it reflect the weight and length of a piece that could require handling by 1 person, 2 people, or more, or ones that could require the use of overhead cranes or other devices.  All of these factors greatly affected time spent and ultimately, our overall costs. These factors also impacted how our products were priced to our customers.

The next thing we tried was surface area. But this, too, didn’t capture the full complexity of the production process.

Eventually, we took a step back and reframed our thinking. Instead of looking for the right single metric, we’d have to design our own formula based on a composite of different factors. We finally landed on the use of volume, weight, and handling factors to create a mathematical calculation that allowed us to take every product and create a unified metric.

And happily, it worked. We started converting all our orders into our new “unit measure” to see what would happen.

Quickly, we discovered that some of our products were being sold for too little while others were overpriced. We found we had far more that were under-priced than over-priced and we were losing money with some customers. We could now calculate a gross margin on every part we made, on every order and every customer. We could even use it to determine if overtime was needed. It was the information we needed to really understand what was happening on the shop floor.

Once our formula was validated, every department was kept abreast, but especially sales, customer service and production. Armed with the same information, everyone could now work more cohesively. Product units, and the cost of those units, was placed at the forefront of each department. Sales looked to maximize the value of each unit, customer service worked with the sales department to plan price increases, production worked to minimize the cost of each unit. We had a deeper understanding of every job we bid on, and production could determine the impact of having more employees, overtime or equipment on our day-to-day costs. Most importantly, we finally had a measure that created a cohesive team, which greatly reduced departmental infighting and internal politics.

It felt like I finally had the right measures to run the company properly. I used the information to restructure our historical financials so that it reflected the effects of each department’s decisions. I began to calculate all revenue at the formulaic price, or unit price, and it made a big difference.

A simple way to look at this is if your labour cost is $40 on a $100 sale, but now you sell that same part for $90 and it still costs $40 to produce, your labour percentage has gone from 40% to 44.4%. Who bears that responsibility? We tend to look at production and ask why their costs are going up, but in this case, it is a sales problem. We began to charge any deviations in pricing to a clearly defined discount account. This step provided the sales department with an overall account to track their pricing decisions.

It also allowed me to evaluate the efficiency of our production lines. The cost of labour was now evaluated with a stable revenue line based on our formulaic price rather than a mixed bag of revenues drawn from discounted to premium pricing. This meant I could take a more nuanced view, without sacrificing the big picture and allowed me to assign responsibility to Sales or Production.

At the best of times, financial statements tell a story about a company that can sometimes seem foreign. As leaders, our job is to interpret that language and make it understandable so we can make relevant decisions, or allow others (banks, suppliers, shareholders etc.) to see the underlying value of our decisions. We need to help paint the picture with as many colours as possible, so we can tell our story and solve our problems. But getting the right information, in a complex environment, can be difficult.

Historic financial information, or the past-tense data you get in a year-end statement, is not enough. To make more informed daily decisions, we need to capture dynamic information that better reflects what is happening today, and how that might affect what will happen tomorrow. Armed with both the appropriate tools, we can make more informed decisions and build a management approach that works.

To learn more about applying similar financial insights and discuss how best to use financial information in your organization, please contact Brian Venis at [email protected].