Executive teams in manufacturing rely on good data to keep their organizations growing and moving. Without it, they could only play a guessing game—becoming lost and unable to navigate their organizations through the shifting landscapes of today’s manufacturing industry. That is why the relationship leaders have with data is a foreground issue inside companies taking growth and success seriously.

Start looking forward, not backward

Even today, some manufacturing executives rely on manual or analog data collection when considering their KPIs. The data they have access to is often collected, aggregated down, and then delivered via a lengthy process that can take weeks to months, depending on the methods in place.

Leaders with inefficient data collection methods are at a constant disadvantage because they are trying to make decisions about today with information that is largely irrelevant to their current situation.

Consider, for example, Machine OEE. When collected through older methods, the information that creates an Overall Equipment Effectiveness score comes from blended sources. Each of its parts (quality, availability, and performance) requires multiple departments and people to collect specific data consistently—sometimes across different factories. The results from this kind of data collection can never be wholly accurate in the real world. Immature or faulty collection methods, misleading samples, human error, and other problems make this data more of a loose reconstruction of the past than an actual snapshot of facts. It’s easy to see how using this data to make meaningful choices can feel like a gamble, not an educated decision.

Missed information, missed opportunities

All of this has negative consequences for leadership teams in manufacturing. Most executives can tell stories about a time in their career when not knowing something caused havoc for their organizations. However, the lure of the status quo may blind some to the extent to which bad data is costing them…until that one unforeseen problem crushes an opportunity or ruins a balance sheet.

As a KPI, efficiency is helpful when thinking about data quality. Let’s assume a factory has set its target KPI like OEE to 70%. However, this data is collected from individual machines by hand every week. Because that data needs to be polished and standardized to be made usable, most executives in the company only see it in a monthly report devoid of the context of the current moment. Lucky for them this metric has remained stable, hovering between 68 to 73 percent. However, one month, the report shows that their efficiency KPI dropped to 60 percent—no one knows why.

It’s easy to imagine the questions that would be asked due to this drop in performance and the scramble after such a steep drop. It’s also important to consider the kind of resources executives would spend in the search to figure out the cause, let alone the fix. However, the good news is that situations like this can be avoided completely by emphasizing collecting good data using modern technologies.

We have the tools today for a better tomorrow

It’s no secret that the internet age has changed the world. Cloud computing has become the bedrock of most organizations and even the global economy. There is no going back.

Manufacturing is joining this revolution by adopting new technologies that are revolutionizing manufacturing’s relationship to data—equipping executive teams with better information and crafting better results.

KPI data collection is one way new technologies enable better relationships to data. Factories and individual machines now can have their whole data collected and stored in its native format in real-time. This removes problems in collecting data and allows information to be made actionable immediately through tools like artificial intelligence and machine learning. When fed into a central data pool, this data can be polished, put into a broader context, and shown in real-time – all within the broader context of the company.

For executive decision making, the results are earthshaking. Instead of waiting weeks or months, many KPIs can be shown in real-time, direct from individual machines to the KPI equation. Leaders can also choose how much information they need, going from broad overviews to hyper-granularity quickly and easily. When more complex questions need to be asked, teams can aggregate data and reports in minutes rather than days or weeks.

The ability to solve problems in near real-time, reassess KPIs, and fine-tune operations becomes more accessible in a world with this kind of data. All of this presents opportunities for executives looking to launch their organizations forward.