The Power of Analytics
Yes, data overload is a thing. And yes, we need to overcome it, make friends with data, and parse out the most useful information data reveals, because it’s key to the competitive edge.
An article published by McKinsey and Company, a global management firm, offers some great insights on optimizing data. Read the whole thing, titled Manufacturing: Analytics Unleashes Productivity and Profitability, here. Or just continue on for my highlights and take-aways.
The starting observation is: We’ve all got to tighten up operations. Pressure is hitting from all sides as raw materials rise in price, growth slows, and the general economic and political climate remains uncertain. McKinsey says, “High uncertainty and low growth have already forced manufacturers to squeeze every asset for maximum value. The next target is their own data.” Traditionally, manufacturing has not emphasized IT, but today IT is key to gathering and analyzing the data that will help manufacturers optimize their processes from beginning to end.
Computing power, data gathering, machine learning, visualization platforms and all kinds of analysis are more accessible than ever. “Advanced analytics also help manufacturers solve previously impenetrable problems and reveal those that they never knew about, such as hidden bottlenecks or unprofitable production lines. There are three applications of advanced analytics in particular that together are powerful tools for maximizing the physical and financial performance of manufacturers’ assets and often-complex supply chains.”
The three applications they discuss are:
- Predictive maintenance: Analyzing the historical performance data of machines to forecast when one is likely to fail.
- Yield-energy-throughput (YET) analytics: Ensuring that individual machines are as efficient as possible when they are operating.
- Profit-per-hour (PPH) maximization analytics: Scrutinizing the thousands of parameters and conditions for impact on total profitability of an integrated supply chain.
McKinsey proposes that harnessing these advanced analytics can deliver EBITDA (earnings before interest, taxes, depreciation, and amortization) increases ranging from 4 to 10 percent. If a company can make good on these numbers, that’s a huge competitive advantage.
Machines break—that’s not going to change. However, as technology advances, workers are less hands-on than ever, and machines are far more complicated. It’s harder to anticipate a breakdown, the costs associated with a machine going offline are greater, and repairs are likely to be more complicated as well.
“Predictive maintenance systems gather historical data (structured and unstructured, machine- and non-machine-based) to generate insights that can’t be observed with conventional techniques. Using advanced analytics, companies can determine the circumstances that tend to cause a machine to break and monitor input parameters so they can intervene before breakage happens—or be ready to replace it when it does—thus minimizing downtime. Predictive maintenance typically reduces machine downtime by 30 to 50 percent and increases machine life by 20 to 40 percent.
Yield-Energy-Throughput (YET) Analytics
In the same way that predictive maintenance can improve the uptime of an individual asset, YET analysis can maximize its effectiveness. Even small percentage improvements in operational efficiency can significantly enhance earnings before interest and tax (EBIT). The YET approach does that by balancing yield, throughput, and material costs to maximize the profitability of each process step.
McKinsey goes on to describe a case study in which a global chemical corporation analyzed 40 million data points relating to the function of a furnace, ultimately projecting a potential financial gain of €30 million. Yes, it was doubtless a big investment to set up that analysis, but €30 million is pretty motivating, right?
Profit-Per-Hour (PPH) Maximization Analytics
Whereas predictive maintenance and YET analyses are designed to improve the performance and profitability of individual machines or processes, PPH maximization can optimize the interaction of those machines and processes. Encompassing every step from purchasing to production to sales, this advanced modeling technique dynamically maximizes profit generation in complex production systems and supply chains, encompassing every step from purchasing to production to sales.
Once again, it’s all about computing power. Typically this advanced analytics approach factors in up to 1,000 variables and 10,000 constraints, helping manufacturers figure out what they should buy, make, and charge. McKinsey offers an example of a global chemicals company with a broad range of products, customers and contracts. Suboptimal production and distribution decisions were suspected of leaving a lot of money on the table. Analysis revealed tactical changes which could save millions of Euros annually.
Data: We Can’t Afford to Ignore It
The era of data and analytics is upon us. While it’s often a lot to get our heads around, the potential payoff is huge. Yes, the amount of data points we can access seems to multiply by the day, but we’ve now got the computing power to put this information to work, and no one can afford to neglect this opportunity.