The digital transformation is here. Manufacturing leaders are driving value through data-driven business decisions and enabling the digital ecosystem by optimizing and automating processes from design to delivery. During this panel discussion the participants shared tips and tricks on leveraging data and digital technology to enable better decisions and competitive advantage. This write-up summarizes some of the key messages and learnings shared. The panelists represented different perspectives from midsize to large manufacturers, manufacturing leadership to IT, and industry experts.
Watch the full video here.
Panel Participants:
- Nina Anderson, Data Scientist, Association for Manufacturing Technology
- Kim Cato, IT Director, Whirlpool Corporation, NAR Integrated Supply Chain and Manufacturing
- Ester Codina, Managing Director, Sandvik Coromant
- Stephanie Hendrixson, Writer & Editor, Gardner Business Media (Additive Manufacturing Magazine)
- Elisabeth Smith, President & CEO, Acutec Precision Machining, Inc.
- Susy Mark, Director of Marketing, VIMANA, (Moderator)
How to Succeed with Digital Transformation
Define the problems to be solved with digital transformation
Manufacturers should develop a digital business strategy that aligns to their business pain points and delivers real business outcomes.- Identify and prioritize problems that are impacting performance and competitiveness.
- Uncover the barriers to maximizing spindle time and improving the bottom line.
Prioritize key technologies to enable digital transformation
There are many smart manufacturing technologies, to include: Cloud computing, analytics/AI, robotics, automation, and additive technologies all contributing to the optimization of the supply chain, profit improvement, and efficiency. To narrow the discussion focus, 3 tiers of technology were discussed:- Connect and capture data. The first step is to lay the foundation: design and deploy the plant network infrastructure and connect shopfloor devices. Manufacturers need to have the right infrastructure (network optimization and device connectivity-wifi, RFID, sensors, edge aggregators, cloud computing) in place to support the digital journey, establish standards to connect the machines (MTConnect, OPC UA), determine how devices will be connected and define which machines need to be connected first.
- Big data and data analytics allows manufacturers to extract and categorize large and varied sets of data to identify hidden patterns and correlations that informs decision making. Machine monitoring software addresses these data collection and analysis needs, providing shop floor visibility and insight to improve efficiency and shift from reactionary to proactive practices.
- Advanced manufacturing solutions such as Additive Manufacturing, a process that uses digital technology to reduce the steps and materials needed to build a product, and shifts the entire supply chain. Still in its infancy stage for true production machining – additive is mainly used for prototyping and validating theories, understanding the manufacturability of a product, and enabling users to make adjustments before the part is in production.
Develop a Data Strategy
Much of the manufacturing data today is siloed. Manufacturers need to connect and collect data across the plant and enterprise. Integrate the data into a centralized location and eliminate all the disparate data silos. The more information there is in one place, the easier it is for the people consuming this information. Make the data useful by aggregating, standardizing, and bringing context to the data. Don’t assume all data is created equally or is accurate. Do data audits. In manufacturing all the information exists, but manufacturers have not been using it. Data needs to be extracted from the machines and analyzed to look for trends. Data analysis and visualization should make information easy to interpret. Data modeling and machine learning should be used to leverage data you already have, to predict what is going to happen in the future. Employees must use the insight to take corrective action before issues occur and to improve processes, i.e. preventative maintenance, workforce efficiency, and capacity planning. Collect data as part of the normal business process. Make the data transparent across the entire organization, from the machine operations to the financials. This provides diverse teams visibility to the operation, enables better communications and speeds decision making. If someone wants to know the current operational state of multiple factories they can see that in seconds with manufacturing analytics, without incremental time or resources. Piecing all that information from multiple systems manually is not going to give anyone quick and easy information. Manufacturers should have the same level of transparency into manufacturing facilities today as consumers have to their personal banking.Examples of how to use manufacturing data analytics for improved decision making
- To optimize asset performance
-
-
- Use data analytics to guide buying decisions for new machine tools
- Understand utilization of every device to improve capacity planning and scheduling
- Understand how critical machines that are needed 24×7 should be optimized
-
- To improve workforce productivity
-
-
- Use data to define the time to manufacture parts and complete jobs, so that operators can accurately charge time to jobs
- Use data to set targets for number of parts produced/shift
- Use data to drive awareness from machinist to management – awareness has increased asset utilization from 70% cutting to 90% cutting
- Analyze relevant process parameters and determine reasons behind the performance variation of equipment across shifts/workdays
-
- To improve processes i.e. maintenance & quality
-
-
- Use data to uncover areas of waste that were unknown
- Determine why additional preventative maintenance is needed on some machines and not on others
- Design PMs around actual machine use vs. time schedule
- Optimize tooling – replace parts based on actual in-cut time
- Optimize end-to-end manufacturing processes – across the shop floor from machining, assembly, and delivery to improve cycle time and on-time delivery
-
Drive a data driven culture and enable change
Changing the culture, the ‘mentality’, is critical to digital transformation success. What we did in the past is not going to work in the future. It’s important that at all levels of the organization there is recognition that data will help manufacturers be successful. Leadership is eager to deploy machine monitoring. However, the Operators initial perception is that it is micromanagement. Operators need to understand what’s in it for them, how it simplifies work and makes their jobs easier. It’s also important to find workforce promoters that will share their successes and engage others. Leaders do need to set the expectation: machine monitoring is for everyone to use and the whole operation should identify opportunities to improve processes in a collaborative manner. Examples of collaborative process improvement driving efficiency and cost reduction:- Operators were disposing of expensive tooling at 50% of the parts lifespan. Using a production monitoring solution, the operators understood the lifespan of the tool, and optimal replacement timing. Additionally, implementing a vending solution offered accountability over which operator was using the most tooling components and the expense of each tool. The Operators embraced the process change and reduced costs.
- Production monitoring improved workforce productivity and was self directed. Sometimes, Operators clocked in at 7 am, but the machines didn’t start running until 7:20 am. Giving the operators awareness to utilization, parts produced, and change over times, provided the insight into how operators could make small changes to improve their productivity. This resulted in an increase of 28% in the plants overall machine utilization.
