Getting Started with Digitalized Manufacturing Operations

Published by:
Dipankar Ghosh
2nd July 2017

I come across manufacturing managers who feel excited about the possibilities of a ‘connected’ shop floor and ‘connected’ products, but don’t know where and how to start. They are very experienced and knowledgeable and have been, in recent times, attending conferences on Industry 4.0 and Industrial IoT.

Many of them have worked with and have implemented, with some degree of success, several improvement initiatives such as TQM, Kaizen, Lean (or Toyota Production System), Agile Manufacturing and Theory of Constraints. 

With Industrial IoT they see immense opportunities to do all of these better, faster, proactively and innovatively. After all, accurate and timely information that produces actionable intelligence, form the backbone of all such methodologies when put into practice. Connected machines can deliver real time data collection and analysis, automation and autonomation; and predictive and prescriptive analytics. This makes Industrial IoT a very attractive proposition to them. 

One of the key issues for them is to develop a business case to present to their management to obtain approval to get started. What are the use cases that they could be looking for? What are the KPIs that could possibly be dramatically improved?

I have attempted to come up with a potential list of areas and KPIs that they may investigate and I would request my readers to augment this so that we can all benefit.

Operations and Manufacturing Problems

Areas to look at: Real time asset monitoring, inventory and material tracking, predictive and condition based maintenance, quality assurance; and connected operational intelligence

KPIs that can be improved: Throughput (cycle and lead times), defects and rework, returns, recalls and complaints, manufacturing costs, uptime, operator scheduling effectiveness, OEE, energy costs, maintenance costs, compliance, customer satisfaction 

Product Development Problems

Areas to look at:  Product quality analysis (through simulation and under testbed condition), real time performance analysis, real time usage analysis, real time operating environment and condition analysis

KPIs that can be improved: R&D costs, product development costs, time to market with new/improved product, product quality, safety and reliability, profit from new products or services, customer satisfaction

Support and Service Problems

Areas to look at: Predictive and automated service functions, service parts inventory management, remote monitoring and troubleshooting

KPIs that can be improved: Availability and uptime of products in the field, warranty and service costs, customer complaints, complaint response and resolution time, returns and recalls, customer satisfaction

Each of the above areas merits its own space for further exploration. I urge my readers to share their views and also any case studies that they may be able to share from their experience.

Note that while zeroing in on a specific area and building a business case for IoT implementation maybe relatively easy, doing the implementation successfully is a different story that shall be discussed in a later article.

Meanwhile, wishing the very best to all the bold manufacturing managers in their new journey with Industrial IoT. 

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