Culture Corner Digital Transformation: A Focus on Improvement Not Software
WRITTEN BY: BOB SLOMA
What do we mean by Digital Transformation??For some executives and managers, it can simply mean connecting equipment up for monitoring; obtaining new data access, and utilizing that access to gain new insights.
Because Sandalwood has a diverse mix of disciplines, we tend to view Digital Transformation differently.?In the range of manufacturing related processes we serve, we have witnessed the steady incorporation of software, automation, sensors, and other digital tools established as part of best practices in the field.?Digital Transformation is not an “add-on” or “next-level”, it is an embedded element of a contemporary competitive manufacturing operation.
Digital transformation is also not a new phenomenon. We have witnessed the evolution in practice over several decades, starting with large, multi-national manufacturing customers.
For nomenclature, we tend to use “Industry 4.0” as the industry movement, and “Digital Transformation” as the shift for a particular company to adopt it.?There are many excellent summaries of the history of Industry 4.0, which tend to focus on the maturity model of the technology itself.?Since we are process centric and focused on a particular company’s journey, our view of the history of Digital Transformation centers around the evolution of Six Sigma.?In 1986 Bill Smith, an American engineer working at Motorola, introduced a set of tools and techniques for process improvement, mainly centered on manufacturing quality.
In the late 1990’s Six Sigma expanded to include reduction of variability in manufacturing and business processes increasing the need for data to make data-driven decisions. During this time factory information systems began to be implemented in large, multi-national manufacturing companies, pulling data from factory floor devices, eventually feeding enterprise systems.
In the 2000s, low-cost sensors and widespread access of cellular infrastructure led to development of the MQTT protocol over TCP, allowing small data payloads to be sent from devices in remote locations to a central enterprise system. Other protocols also advanced the ability to capture data from manufacturing devices and integrate them to enterprise systems.
We see Digital Transformation becoming an imperative for manufacturers for different reasons.?For some, their company or their industry has been affected by certain technology trends that compel them to adopt the new standards.?For many, it’s the normal search for gains in safety, quality or productivity in processes that lead to Digital being the next threshold for improvement.?The other big driver is the search for ever more improvement into operations from data being captured, analyzed and converted to actionable insights not previously available.
Today, there are several trends driving Digital Transformation. We see the following five as key to the type of work that we do.
·??????OT/ICS Cyber Security
·??????Industrial Internet of Things (IIoT)
·??????Machine Learning / Artificial Intelligence systems (ML/AI)
·??????5G cellular technology
·??????Increasing need for manufacturing agility & responsiveness
Cheaper sensors, 5G, and open standards and protocols are supporting an explosion of IIoT devices. Even legacy manufacturing device companies like Siemens and Rockwell are enabling their devices to send data via open protocols and getting away from their own proprietary ones. With these enabling technologies, the amount of data being produced from manufacturing production will be 10x to 100x what we have today. To process this data and enable intelligent decision making, ML and AI systems will be a must. Finally, as the COVID pandemic exposed weaknesses in our current supply chain systems, not to mention rapidly changing customer preferences, the need for agility and responsiveness within the manufacturing ecosystem will only continue to increase.
Gains in Safety, Quality, and Productivity in processes need no explanation except to point out that too often the difference between material management and information management is not fully appreciated.?This is one of the reasons Process Mapping is an essential first step to any successful Digital Transformation.
We see three major categories of maturity a manufacturing organization needs to go through to receive the full benefits of Digital Transformation: Capture of data, analysis of that data (Analytics), and creation and communication of Actionable Insights from the data.
New insights into company operations from newly captured, accessed or converted data can be subtle or dramatic depending on the situation. Typically, we launch this process by gathering data from equipment or operators not previously logged. Data capture requires the ability to communicate with production PLCs and/or MES systems normally using proprietary protocols. This requires some type of middleware software with drivers written to work with those protocols.
Once the data can be captured from the devices, a data or information model needs to be in place to allow definition of meta data associated with values as well as a standard format the data will need to be mapped to for upstream systems. Finally, the middleware software needs to enable the definition of data normalization/transformation flows to map the data to the standard format.
Interestingly, often the data being captured was not properly accessed or converted to concise, distilled metrics resulting in actionable insights to improve company operations.?“Data Contextualization” is a process increasingly required to realize the full potential of Digital Transformation.?Without it, these investments and projects simply result in data overload and information fatigue at every level – from the workcell to the line, from the plant SCADA and order management to reporting for executives and managers. Data Contextualization via Analytics can be accomplished in several ways. A common way to implement this is visualizing the data using dashboards. These dashboards can show captured data in near real-time along with the associated meta data. Oftentimes, additional processing needs to occur to calculate other values from the raw data and enable the calculation and display of trend data.
Finally, the goal is to provide Actionable Insights. This can be as simple as having someone view a dashboard and make decisions based on the data displayed. The next level is to enable the ability to set alarms that show up on a dashboard and/or are sent via email or text message to people who can resolve an issue. More advanced levels utilizing machine learning or AI can begin to predict when equipment is about to fail and provide prescriptive actions i.e., automatically send a work order to maintenance. An even more advanced level is to have ML/AI monitor the production process and automatically adjust machine parameters to keep the process within target ranges.
The more Digital Transformation is viewed not as an “add-on” but as an embedded element of manufacturing operation, the more it needs to be influenced by the processes it is converting. Digital Transformation is a journey, not a destination. It is not something new. The evolution of the technology will continue to make the journey easier for more manufacturers and reduce the cost of design, development, and implementation. The key is to focus on the business problems you are trying to solve, keep the scope of the initial projects small, and work on getting quick wins. The benefits from those wins will help create excitement and buy-in and create proceeds from which the next project and use-case can be funded.