Today's digital revolution in the global industrial sector has been especially trying for machinery manufacturers and the equipment they produce. After all, as worldwide industrial businesses grow, those companies will require intelligent, efficient assets to drive competition. Machinery manufacturers will not only contribute to industrial sector prosperity, they will invigorate it and profit because of it.
While the potential for reward remains high, so too does risk. As industrial machinists innovate to meet the demands of Industry 4.0, they further complicate their operations by expanding their reliance on big data. As many are aware, the aimless collection of massive datasets in such an information-driven sector is like trying to stop a flood while chained to the bottom of a deep well.
As industrial machinists innovate to meet the demands of Industry 4.0, they further complicate their operations by expanding their reliance on big data.
Machinery manufacturing, the spark plug of the global industrial revolution currently underway, must recognize the importance of this data to its internal processes and learn to apply it capably and confidently. Therefore, if you want to succeed, you need the answer to one very critical question: What data matters most?
Understanding the whole of world economy in its simplest form
The globalization of industry has left many scratching their heads when it comes to all the different connections between countries, companies, commodities and economies. Industrial machinery manufacturers cannot dawdle in mastering this vast chess game.
Data must help industrial machinery manufacturers think a thousand moves ahead.
With the continued strength of the U.S. dollar and economic deceleration in China, two of the largest world players in the field have been thrown into a state of uncertainty, leaving European and Asian-Pacific powers to claim newly available market share. Furthermore, commodity prices for materials like copper and scrap steel remain low but are prone to steep fluctuations at the drop of a hat. Machinery manufacturers must have the capabilities to gauge demand and weigh it against materials costs and tariffs to strike while the iron's hot and act conservatively whenever necessary. Moreover, the less effort expended to do so, the better. As we'll see, this isn't merely crucial for the sake of the industry in question but for the sake of all its key markets.
Catching valuable opportunities as markets rise and fall
Because machinery manufacturing spreads out across many asset-intensive sectors, disregarding market conditions affecting those fields would be foolish. What would be more foolish, however, is to draw data from these niche industrial economies without a strategy for sound business intelligence.
For example, poor economic activity from commodities like corn and wheat may cause agribusiness around the world to reconsider capital investments in new large-scale equipment. On the other hand, energy and construction will see something of a renaissance and a need for more machinery to meet their own production and service goals. In either case, actionable data allows industrial machinery manufacturers to foresee, plan and pivot when disruptions are set to occur across their most important verticals.
Median revenue for machine manufacturers fell nearly 13 percent in 2016.
The value of this agility cannot be understated in modern manufacturing regardless of sector. Median revenue for machine manufacturers the world over fell nearly 13 percent in 2016, according to Euler Hermes. That's more than four times the decline from the year before.
The cause? Overproduction in years previous.
If only the sector hadn't lacked advanced forecasting capabilities through stronger data management, perhaps this significant drop could have been partially averted.
Evolving to handle the cost intricacies of smarter products
How could one have a complete discussion about data in industrial machinery manufacturing without talking about the technology going into the machines themselves?
The industrial machinist has been charged with equipping assets with automated versions of data management processes and applications once performed manually by the end-user: monitoring features, quality assurance controls, safety reporting, analytics, notification of deficiency or failure, etc. A hydraulic press is no longer just a press but a smart machine capable of delivering a constant feed of information to its operators regarding its performance.
And as the expectations of the modern machine user continue to mount, industrial machinery manufacturers will shape and reshape their products accordingly at a cost. After all, consumer investigation does not come cheap. Neither do R&D or a growing dependence on highly skilled labor backed by on-site training. To transform into the technological superpower the manufacturing sector needs it to be, industrial machinery manufacturing must develop a strong foundation for 21st-century collaboration or face an unaffordable future.
End-to-end enterprise resource planning software is that foundation.