Readiness Assessment Insights
Thank you for your response. Your responses have contributed great value to our ongoing research and efforts to spur Industrial IoT/Data-driven manufacturing adoption in Northeast Ohio. Scroll down to find a summary of insights to understand your organization’s readiness to adopt data-driven manufacturing solutions and how your readiness compares to others in Northeast Ohio. To view the full insights document, please click the button below.
Your IIoT Readiness score reflects your overall readiness to implement IIoT /Data Drive Manufacturing solutions as compared to your peers in Northeast Ohio.
What Type of Business Are You?
Question #6: What Type of Business Are You?
High-mix, low volume businesses have the greatest opportunity to harvest value from the implementation of DDM. High mix, low volume typically means more tooling/jig change overs, and different machine set-ups throughout the day, and susceptibility to interruptions in parts causing operator wait times. All the issues can be addressed with an asset management program supported by DDM.
Low-mix, high volume businesses have a greater tendency to focus on workflows, work in process (WIP), and real-time continuous improvement. All these issues can be addressed with an asset management, inventory control and predictive maintenance program supported by a DDM system.
Process Control based businesses are typically more mature in their implementation of data-driven manufacturing since their manufacturing processes incorporate higher levels of feed-back control, a greater use of sensors, and data processing. Data Analytics, machine learning, and real-time process optimization are applications where DDM supports further improvement in business performance.
Batch processing usually relates to many clients requiring unique recipes for blending/reacting chemicals, gases, or liquids, or coating materials and involving relatively smaller volumes of product. This regime is usually sensitive to resource availability/change overs, variability in ingredients or process, and environmental influences all of which can be managed well through a DDM system that is focused on asset management and data analytics applications where new relationships between data streams from process variables and environmental influences are continuously being identified and any unwanted deviations compensated for in real-time.
Continuous processing usually relates to a few large customers having recipes for blending/reacting chemicals, gases, or liquids, or coating materials and involve large volumes of product. This type of processing is very sensitive to machine up-time and precision control of process variables – a minute improvement in output can add millions of dollars of profit to the bottom line. A DDM system supporting predictive maintenance or big data analytics will have a big impact on business performance.
Question #9: Improving Operations
Operational Efficiency, Inventory Management and Preventative Maintenance are the top 3 most popular applications for DDM. This is consistent with most IIoT/DDM market studies. Operational Efficiency is typically the first place to start when implementing a DDM system. Once a data network connecting all the critical production assets is established to support the Operational Efficiency application, the same infrastructure can be used for the other applications listed.
Organizational Leadership Sponsorship
Question #12: Organizational Leadership Sponsorship
Most manufacturers implementing a DDM system are successful when the project is driven at the Executive level.
Ideally, each stakeholder in the company should be backing the DDM implementation project. If either the IT or Operational Leadership is driving the project, they need to take the necessary steps to get the other stakeholders involved and be able to convince the Executive Leadership of the opportunity to improve business performance and efficiency. The Operational Leadership has the greatest impact on the justification for DDM since they are the closest to the production issues and the product cost issues.
Question #17: Embracing Change
The most popular response to Question 18 is Mildly supportive which indicates that manufacturers in this region generally have a challenge to overcome if we are to realize the true potential of DDM. Cultural transformation is essential to a successful implementation of DDM.
Ultimately, a manufacturer implementing DDM should have a managed their workforce through a cultural transformation process that has imparted fundamental training (Trained), left the worker feeling positive about their role in the implementation and use of the DDM system (Engaged: Enthusiastic), engaged in the planning and definition of the DDM transformation plan (Engaged: Identifying) and play the roll of a key driver in the improvement of business performance.
Workforce should become involved as-soon-as possible in the discussion of the DDS transformation as long as they have received sufficient training on the subject and have been part of the cultural change exercises.
Solution Providers along with local Community Colleges can help improve the culture and subsequently the acceptance of data-driven manufacturing.
Operational Technology Infrastructure
Question #18: Operational Technology Infrastructure
The Operational Technology (OT) function basically represents the infrastructure in the production area of the plant that supports the data interchange between operations and all the critical assets used in manufacturing. OT is the compliment to what is traditional referred to as IT and the rollit plays in the business management function. As can be implied from the scoring in the above table, a higher functioning OT infrastructure is indicated using many smart devices connected to a data network (Many devs & internet), use of ‘Cloud” based applications (Cloud based apps), and access to real-time information through mobile display devices like smart phones (Mobile enabled).
At a minimum, you need a few of your critical assets providing operational data to a data collection hub that then processes and shares the information with the workers on the plant floor displays or mobile display devices, so they can make better real-time decision.
A preponderance of white boards on the shop floor indicate that information is being collected by hand and not shared in real time with the other functional areas of the business, then typically process by hand and finally being noted on the white board by hand most likely in a stale form. By applying a DDM OT infrastructure, this whole process would be totally automated, hands free, and the data for key performance indicators (KPIs) would be processed and displayed in real-time to the workforce thus enabling real-time decisions.
Question #23: Getting Started
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Scale Up Approach
Question #27: Scale Up Approach
Most Solution Providers start with DDM pilot projects first so that they can slowly immerse the manufacturer into a learning experience that leads to a general understanding of concepts used to improve the business performance. Scale-up subsequently becomes much easier.
If you would like assistance with training, contact us below.
Looking for a More In-Depth Analysis?
If you need a primer on the subject of Data-Driven Manufacturing, help finding resource material like application use-cases, or help finding a Solution Provider that matches your company’s needs, schedule a meeting with us!
These insights are based on a few assumptions that reflect the unique characteristics of the manufacturing sector in Northeast Ohio (NEO):
We are focused just on the manufacturers that are in the 18-county region that is served by the Smart Manufacturing Cluster and therefore, the DDM Readiness Assessment respondent’s scoring and comparisons are relative to only this province.
There are roughly 7,120 manufacturers in NEO and 6,835 (or 96%) are considered small to medium enterprises (SME) based on their sales volume ($1K – $100M). Because the SME manufacturing sector plays such a significant role in the region’s economy, most of the insights will be offered from the SME perspective.
Since the SMEs are typically less experienced with the concepts of DDM (sometimes referred to as Industry 4.0, digital transformation, or IIoT) compared to the larger manufacturers, we assume the SME is more interested in the applications that improve business performance and efficiency and that allow for the harvesting of value from the new data being collected from production assets and resources. In other words, SMEs are technology agnostic and are just looking for a total solution for the application.
Larger businesses, on the other hand, normally have a greater DDM infrastructure already in place and are typically more interested in the technology levels associated with DDM system like data analytics, process optimization, or AI/machine learning, for example. These larger businesses typically have in-house experts responsible for the DDM system and most normally require specific assistance from Solution Providers with advanced capabilities.
SMEs also seem to do better at implementing and harvesting value from their DDM system when they partner with a culturally compatible Solution Provider that has extensive experience assisting other similar businesses with their DDM system implementations. The use of Solution Providers that make DDM use cases available to inquisitive manufacturer (through the Smart Manufacturing Cluster web data base) and aid those manufacturers during the planning, implementation, and long-term support of the DDM system, will be referenced throughout this document.