Seven years is how long I have been involved with the term ‘manufacturing’. Sure, this may not be as long as others, yet it is long enough to understand that manufacturing can be interesting for a lifetime, logical enough to keep the brain sharp and will continue to be a critical component of every nation’s economy.
Funny story—I had started a sales career to attempt to stay away from manufacturing jobs and environments. And now (willingly), I dive deep into manufacturing and supply chain fundamentals to find gaps where technology can help improve processes. The more I interact with people in manufacturing, the more my curiosity grows for two things—the current state of manufacturing, which is challenging, and the possibilities within technology that are promising.
Challenges when evaluating digital transformation
- Knowledge. Everyone is educating themselves on the concepts of Industry 4.0, looking to learn more about how to get started, total project cost, timelines, and outcomes.
- Data. According to the National Association of Manufacturers (NAM), only 58% of companies are moderately capable of collecting meaningful full data, and 75% are somewhat capable of analyzing and using manufacturing operations data that takes up more than 80% of their resources. These two challenges, data collection and data usage skills, cause fragmented data that fails to tell a story.
- Trust and Support. These technologies are primarily based on data and gradual learning processes. The unexpectedness of the outcome makes it challenging for both the investor and the solution provider to build trust and provide the level of support expected from established technology providers.
- Budget. AI, machine vision, robotics, and digital transformation are all key components of a costly investment, or at least that may be the surface impression.
Four Tips for overcoming those challenges
1. Knowledge. Start with a small trial project. Most solution providers offering newer technologies allow companies to try out the solution for a short period of time.
2. Data. Go backwards when evaluating. Consult with providers and learn about how various models/tools work, and what is termed as ‘good data’ to achieve the desired results, then start building up a data inventory.
Suppose you want to collect data on quality inspection. How would a certain collection of data help you?
- The number of defects at one workstation might help you in deciding if the human operator at that station requires more training to connect the components correctly and consistently, and package products properly.
- Identified types of defects might help you in process visibility or in incoming supplier quality control.
- Final inspection data might help you with root cause analysis, tracking reasons for returns and shipping claims.
3. Trust and Support. Be as clear as possible in describing your ideal solution and be available for regular feedback & training opportunities when adopting new technologies. If confidential information limits sharing, sign an NDA to continue the conversation. Talk about integration requirements for an ideal fit with existing ecosystems. Try your new system or solution offline first, if possible, and deploy it on the production line after a successful offline proof of concept.
4. Budget. Check with government bodies (provincial/state or federal) for funding support. There are several government funded opportunities around technology adoption—another indication of digitization as a necessary and crucial shift in manufacturing. The longer you wait, the wider the gap grows between you and your competitors.
Digital transformation will streamline operations and contribute significantly to resource planning and future proofing. Any insights gained can be shared across the organization and used to address skill gaps, accelerate innovation, and support rapid decision-making.