It's for sure that your job relies mostly on data. So, it’s imperative that you give giant steps towards Digital Transformation to succeed. Now, to take crucial decisions such as cycle-time insights, how many people you need to hire, or how many lines of operation you need to meet the so-rapid-escalating-customer demands, you can accomplish this via a Digital Transformation.
Stop using those data standard-distribution processes because you might need those outliers you earlier set aside, as they could become later crucial data points in your current manufacturing process. Taking care of so many lines in operation and in such a little time, you limit your small dataset whilst restricting the chances of predicting better distribution.
More Data gathering drives more-better decisions
To understand which data point is an outlier or indicative of a pattern, you require a significant amount of data points to comprehend your process better. No doubt the hardest place to gather data is the floor at your warehouse and drove by your operator's performance. Even counting on newly-advanced Automation, such as robotics, connected factories, and more, even now depends on traditional-human processes, in spite of the Industry 4.0 revolution. Recent studies confirmed that humans still perform 72% of tasks on the factory floor, let's say done manually; consequently obtaining little information of what’s going on at the factory floor, making it invisible for Analytics.
Manufacturers have been forcing to make expert guesses about line improvements, increasing productivity, whilst diminishing cycle time, different staff shifts, and much more. Nowadays, Digital transformation in the factory is bringing more efficient processes when gathering data. The continuously collected-data is used to develop high performance and visualise Analytics; there’s a need to make changes in many areas, but in the end, it's worth the effort.
Most manufacturers will soon be flooding in data!
Do you as a manufacturer, know how to handle this amount of data? The amount of data required to drive real digital transformation is jeopardising the change of the vast amount of raw data or "data lake" into a “data swamp” (data lake that is not used.) Most manufacturers aren’t capable of processing the data required to accurately get the picture of your data distribution and pull out significant insights. That’s why transforming your amount of daily data points with nonstop volume also requires an evolution in your cultural mindset.
Look ahead for confrontation from several areas
There’ll be the coworkers who feel threaten to be assessed for the first time. Others that will feel uncertain of their knowledge or expertise and who it’ll be challenging to recognise that it might be obsolete; those who will feel as if their decisions are no longer good enough, making it difficult to operate on new skills, feeling deep inside that those changes will push them out of their comfort zone, and out of work!
It means, complementing Data transformation with workflow adjustments. Through coaching, you can make the best out of these swift changes in your data and drive value. It will change your decision, so your standard operating processes as well. The needs for Hiring will also change, as it will do the transformation of your technology and the requirements for job positions in your enterprise.
See this Digital transformation as an opportunity to develop high standards, volume and value in your factory performance, even better levels of quality, productivity, plus problem-solving capabilities through an outstanding use of Analytics. The data you gathered will reveal how critical human jobs have to be enhanced through the deployment of new digital-automated technologies such as robots, AI, and Machine Learning, and more emerging tech. By all means, they are here to support human manufacturing today.
Summing up: the Analytics from tasks performed by humans are incomplete and inaccurate, due to the massive amount of Data collected. Take the advantage by reaching higher levels of productivity through Human Analytics, complemented by Digital Analytics.
