Learnings from the ‘Digital Transformation: Gaining a Competitive Edge with Data and Diversity’ Conference during IMTS 2018

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.

Data provides the insight to prioritize opportunities, test assumptions, find waste, pursue casuals, and guide continuous process improvements.  

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:

  1. 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.
  2. 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.
  3. 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.

Companies need to understand that digital transformation requires an upfront investment, but ROI will come, and will come quickly.  Some manufacturers are still weary to invest in digital technology and software.  They are comfortable purchasing machine tools, because a machine tool is a ‘physical entity’ they can use for years to produce parts, and the ROI can be calculated.  However, with data and software, it is not tangible, and that makes it more difficult. Manufacturing organizations must start small and demonstrate that with real-time data and insights, plants can lean processes, reduce costs, and improve flexibility and responsiveness to their customers.  Investment in analytics software will deliver hard dollar savings, projects can be self-funding, and value can be realized very quickly. Some plants realize a 6 month ROI and other large scale implementations look for an 18-24 month ROI.

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.

What’s Next?

Manufacturing and business system integration for process automation

You need to make sure all the systems play well together.  The integration of IT and OT systems is transformational.  This is the enabler to business process automation.  Integrating ERP, QMS, & MES systems with open API’s, can be implemented in large or midsize manufacturers, it reduces redundancies, manual data entry, and streamlines processes.  This is worth the investment.

Digitization technologies including analytics and additive enable on-demand, distributed manufacturing.

Right now, in manufacturing people think of making and warehousing physical parts, but with on-demand manufacturing parts can be thought of as files that live in the cloud on a server somewhere.  The way people think about inventory and the whole supply chain starts to shift. For example, with 3D printing parts can be designed differently. Making one part that previously required the assembly of 5 or 6 components consolidates the supply chain and reduces assembly parts and labor.  Likewise, manufacturers may start 3D printing parts that may have previously injection molded. Suddenly, these 50,000 identically molded parts can all be a little different, opening the door to mass customization. Also, the injection molding was accomplished at the tool location, now it can be printed at different locations, with reduced cycle times and at a lower cost.

Analytics provides the visibility to the entire manufacturing network, to the OEM’s plants and to their suppliers (contract manufacturers, and job shops).  Using data to determine how, when, where, and on what device – subtractive or additive, something should be manufactured to meet customer requirements. Manufacturers can use the insight to balance excess capacity with sites that are missing delivery dates.  Analytics provides the insight to help optimize all assets and the resources to manufacture products closest to the buyer, customized to specifications, and delivered on time. Building a customer centric and responsive manufacturing operation.

A Key Takeaway

Digital transformation will increase manufacturers competitive advantage and agility.  It is a ‘must do’ not a nice to have. Manufacturers shouldn’t wait to invest in digital technology and advanced analytics.  Start small and think big.  Start small with pilots in critical plants with devices that are easier to connect, realize business value, and expand.  

 

© MANUFACTURING SYSTEM INSIGHTS 2019