Yet another benefit emerging from the new technology-enabled shop floor is the power to apply financial analytics to manufacturing operations, equipping decision-makers with the visibility they need to improve profitability.
This kind of capability can be called Financial Overall Equipment Effectiveness (FOEE), according to David McPhail and Dave Edstrom, the respective CEO and CTO of Ontario-based Memex Automation. FOEE builds on the standard OEE metric, calculated by multiplying availability, quality, and performance. However, adding the financial dimension helps answer the all-important question of whether the company is making money.
FOEE, McPhail and Edstrom say, is built upon deploying a way to collect the data from manufacturing equipment and the operator input panel and channeling it into a manufacturing execution system (MES) or manufacturing operations management system (MOMS).
Speaking with ThomasNet News, McPhail said, “Manufacturers who embrace this path will produce more product with less effort and cost, thereby boosting their productivity and profitability.”
McPhail and Edstrom stress that FOEE needs to work in real time to quickly show management the financial impacts of decisions on the manufacturing floor. Enabling the necessary real-time functioning requires a shop application programming interface (API) that communicates bi-directionally with enterprise resource planning (ERP) systems and other data repositories to make the most of data at the machine, workstation, and job levels — and even at the level of the product or individual part.
McPhail and Edstrom characterize such an API as an “adaptor” that can “link all critical production and financial information in real time” and “can be easily modified to talk to everything from a CSV file to a database.”
Generating useful financial analytics depends on solving what McPhail calls “the last meter challenge,” i.e., the crucial connection between the machine or device and the company’s network. Certainly, such an improvement in financial analytics requires technology tools that can generate the right data and present it in usable form for management. However, it also requires companies to focus on the right kinds of metrics.
Michael Rothschild of Profit Velocity Solutions (PVS) believes that the right metric is not the traditional unit-margin figure, but rather is what he refers to as “profit velocity,” which combines profit per unit with units per hour. Providing this metric to company management allows sales efforts to focus on the products that are truly most profitable, he asserts.
In an interview with ThomasNet News, Rothschild acknowledges that unit margin is a necessary metric but warns that “it is not sufficient to inform decisions.” Said Rothschild, “It’s kind of like taking snapshots when what matters is seeing the whole movie.”
He offers the example of inventory figures that help the business understand the value of raw materials or finished goods on hand. For such purposes, a “snapshot” metric is fine. But ultimately, the company exists to generate profits for its shareholders. “So if I’m the CEO,” said Rothschild, “my question is, ‘In the next quarter, how do we get more money out of the business?’”
Adding the dimension of time allows that kind of question to be answered. “Within a certain upcoming period of time, how do I get more money to flow through the plant and into the treasurer’s office?” he commented. To get that knowledge, he says, you have to multiply “margin per unit by units per hour, which gives you margin per hour.” This way, the business can identify the plants, production lines, machines, and products that are making the most money.
For example, a large packaging manufacturer used the profit-per-hour metric to identify poorly performing products out of the over 4,000 varieties it was making and shift production to the products that were actually generating profit for the company. For its North American market, the company was able to increase profit margins from 11 percent to 16 percent in two years using this approach.
A large metals producer used a similar strategy, shifting from measuring dollar-profit-per-ton to a time-based metric allowing it to focus on the particular products out of a total line of 5,000 that were actually delivering the most to the bottom line. The executive vice president in charge of sheet products commented, “We’ve known for a long time that what we really sell our customers is time on our mills… if we could price and manage this time more effectively, we could offer customers more value and make more money at the same time.”
A recent study by San Ramon, Calif.-based Ventana Research suggested that complacency is preventing many finance departments from employing advanced finance analytics. Researchers interviewed a sample of finance professionals and decision-makers from a range of North American firms. They found that, while businesses are collecting and tracking data from “a wider and deeper set of sources than ever before” and are using “analytics-based insights increasingly in every aspect of their business,” finance professionals themselves have “largely failed to take advantage of advanced analytics to address the broader needs” of their enterprises.
Ventana also found that analysts, who should be spending time developing actionable intelligence, are often spinning their wheels with data, saying they are “waiting for it, reviewing it for quality and consistency, or preparing it for analysis.”
Data accuracy correlates highly with companies’ ability to respond to issues and make decisions. The researchers found that out of the companies that said their data is very accurate, 85 percent reported that they “are able to respond immediately or soon enough to changes in business or market conditions.”
Researchers also discovered that training yields benefits. In companies that provide training for people who create analytics, analysts are able to spend more time on actual analysis rather than struggling with data-related tasks.
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