In a wide range of manufacturing contexts, pressure is building to empower manufacturing teams to become “connected workers.” Indeed, for a plant to progress along the digital adoption curve, how factory personnel interface with OT (operational technology) and IT systems to drive value is critical. It is as critical to successful digital transformation as the technology itself. 

DataProphet is a world-leading provider of AI-as-a-Service that empowers manufacturers by orchestrating and leveraging their most valuable resource—industrial data. Our technology proactively prescribes changes to plant control plans to continuously and holistically optimize production without the expert human analysis typically required. Developed and implemented by our team of 50 plus data scientists and solution engineers, DataProphet has partnered with manufacturers in diverse verticals worldwide to help them realize a significant and practical impact on the factory floor—reducing the cost of non-quality by an average of 40 percent. DataProphet is guided by the importance to OEMs, Tier 1 suppliers, and Machine Builders of rapid results. To this end, our service comes with collaborative value tracking and measurement, and we guarantee our manufacturer clients ROI in under a year.

In the context of actual DataProphet use cases—and with frontline project-based insights from our team—we will investigate the inhibitors and proven solutions to managing change in factory environments during smart factory initiatives. 

Rather than taking only a high-level view, we will (over the next few weeks) illustrate actual phenomena experienced between vendor and customer in partnership—as they work to establish technical data integrity and digital cultural maturity. The granular detail in these discussions will offer a rare insight into important terrain on the digital adoption journey—toward what we will define as Manufacturing Excellence 4.0. 

This first installment provides a practical basis from which manufacturers can confidently consider the digital transformation of their operations. It does this by defining the state of data-driven technology and offering a framework—both for evaluating the technology as worthwhile and cultivating the human capital so essential to yielding its value. 


To begin, let us first establish a definition of the oft-used term “digital transformation,” a working definition that is apt for manufacturers.


Broadly speaking, digital transformation means leveraging data via a technology stack to create new business value. In whatever way the company defines it, value is the North Star of any digital adoption process. Data is fuel for the journey.

Likewise, the digital transformation of manufacturing has only truly occurred when such a causal line can be traced. This line is one of value. It runs from utilized industrial data to a measurable business effect—namely:

  • Additional commercial benefits were yielded from the targeted manufacturing operation. 

In the case of advanced, multistep production, the primary objective is to transform input materials into components or finished products. Therefore, digital transformation is about harnessing the power of advanced analytics and AI/ML via OT stacks for continuous improvement enacted at an IT interface. 

In the near term, digital transformation looks like this on today’s factory floor:

  • Data from various industrial sources driving an increasingly higher percentage of quality products and components churned out more quickly, efficiently, and often than ever before.

Perhaps this seems obvious? Certainly, the idea of continuous improvement as the gold standard in manufacturing is nothing new.

Yet, the paradigm has changed.


Greatly increasing data mass and complexity has raised the stakes for last-mile optimization. There is now a new benchmark, a gold standard derived from the available industrial data manufacturers have at their disposal and the advanced methods used to control it.

As a result of this data complexity—and associated sophistication of techniques—manufacturers need to recontextualize traditional approaches to optimizing with data in factory settings. For example, statistical process control (SPC) is no longer the only, or even the best, option. 

World-class data scientists who understand an underlying manufacturing process can model AI and ML algorithms more powerful than SPC by orders of magnitude. Subsequently, deep learning insights from the data can yield actionable prescriptions manifested as preemptive line control. Beyond mere prediction, deep learning discovery applied as prescriptive analytics optimizes a production process proactively and perpetually. Such prescient insights, from their own data, were simply unavailable to manufacturers until recently. 

In sum, it is no longer controversial to assert that digital-era data expertise has arrived as the game-changer for continuous improvement. In reality, this expertise has evolved and been successfully applied to the point where these algorithms are acting as a turnkey solution in the factories DataProphet is working with—fit to bring manufacturing into the new era.

However, for this to succeed, a cultural shift is required in plants.


This cultural shift is towards a digital maturity mindset through which plant teams come to view machine data as a source of value to be unlocked while upgrading business problem-solving perspectives to be 4IR aligned.  It necessitates that factory personnel develops some data savoir-faire to complement their on-the-job experiential wisdom and subject matter expertise. Only then does sufficient trust in the insights of data-driven decision-making evolve to the extent plant teams will modify their workflows from shift to shift. How is a digital maturity mindset cultivated in plants?

To begin with, how manufacturers conceptualize industrial data matters. Digital transformation is ideally a line item on the overall business agenda, from the C-suite to the shop floor. Data-led technology needs to be front of mind as an augmenter of the physical transformation of a production process. If not, plant teams risk relegating data’s relevance or saddling themselves with a “digital solution” but no tangible problem for it to solve. Nor, at this juncture, will a digital production technology adopted a priori (i.e., in a vacuum) house the specific functionalities needed to address an existing KPI challenge relevant to the plant. 

The most impactful tools, after all, tend to be crafted for no other reason than to get something important done. This logic holds for data-driven technology in factories.

Digital transformation can only succeed if manufacturers interrogate any current (or proposed) data-driven intervention in concrete, practical terms. Specifically, manufacturers should ask the following of a smart factory use case:

  1. Is this digital tool measurably transforming my company’s day-to-day operations?
  2. If so, to what extent? Or, what is its effect on achieving (even exceeding) KPIs?
  3. How meaningful is the impact? In other words, what is the ROI?
  4. Can this tool be scaled?
  5. Or, if the digital tool’s effect is negligible—what else is available on the market?
  6. And how can I be sure it will be worth the investment?

Equally important to manufacturers is the digital maturity required to exploit the chosen technology. Digital maturity begins with a psychological orientation toward data-led recalibration of factory operations—to be more energy-efficient, market-adaptive, and productive. A digital maturity company culture does not seek to replace but, rather, aligns itself with and augments the plant’s existing manufacturing excellence best practices. 

In this way, manufacturing leaders imbue the digital transformation agenda with relevance to workers by building a culture of 4IR (fourth industrial revolution) digital excellence and capability. 

However, as with any organizational change management process, it should anticipate resistance. In light of digital transformation use cases, the following typical questions from personnel should be codified and embedded within the framework of any digital excellence regime:

  1. What are the projected implications of this digital transformation initiative for my job?
  2. How will this digital technology integrate with and augment current manufacturing work practices to drive and sustain continuous improvement?
  3. How will it impact the work practices for which I am responsible?
  4. What extra responsibilities will I have to undertake?
  5. Will I be held accountable if something goes wrong?
  6. What extra training will be required?
  7. How will I be upskilled/reskilled to accommodate any changes to the standard operating procedures brought about by this digital transformation? 

In conclusion, acting on data is key to affecting production processes’ last-mile optimization. To stay competitive, forward-looking manufacturers prioritize accelerating the digital capabilities of plant teams. However, reskilling factory personnel for digital transformation means securing their buy-in regarding the inherent competitive advantage of data-driven technology. Ideally, a digital maturity mindset shift is cultivated during use cases in which the adopted technology captures additional commercial value. 

Stay tuned for part two, “Conceptual Rebooting For the Data-Driven Continuous Improvement Journey”, in which we will hear from Hyram Seretta, DataProphet’s Head of Growth and New Markets. In this installment, Hyram will share his experiential wisdom on classic best practices for continual improvement and explain how, as part of a change management process, the utilization of operational technology is poised to define Manufacturing Excellence 4.0.

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