Industry 4.0 readiness: A self evaluation

Digital manufacturing is an integrated approach to production, centered around a modern information technology (IT) architecture from art to part. By reading a computer aided design (CAD) file, a machine can create prototypes and produce finished or intermediate products. Digital manufacturing enhances productivity while in many instances reducing production costs.

To explore this topic in a thorough manner, we should consider:

• What is digital manufacturing, and what can it become?
• What’s preventing industry from adopting it?
• As a manufacturer, where do you start?

Digital manufacturing scope

The consensus is that digital manufacturing starts at the idea stage – 3D CAD – and stops when the part is retired and recycled. It includes all steps in between, including:

• Make vs. buy decisions
• Planning
• Sourcing
• Programming
• Machining
• Assembly
• Usage, maintenance, recycling

Readiness

Why is manufacturing lagging behind several other industries in automation or systems integration?

Earlier in my career, at Kennametal, I had the privilege to visit dozens of machining facilities and ask about systems and processes used in the shop, from request for quote (RFQ) to shipment. This exercise helped my team develop NOVO – an app that manages process planning, inventory availability and purchase, cost-per-part management, and productivity improvements – one of the first artificial intelligence (AI)-based manufacturing advising systems.

These processes have not changed much throughout the years, and the gap between manufacturing and other industries seemed to be getting worse. That’s because manufacturers are dealing with an alphabet soup of closed systems, usually coming with machinery or legacy systems acquired long ago within a complex ecosystem of vertical silos [computer aided design (CAD), computer aided manufacturing (CAM), enterprise resources planning (ERP), controls, coordinate measuring machines (CMM), and tooling management systems (TMS)].

Adding certifications, regulations, and culture accounts for the lack of enthusiasm to embrace full digitization. The challenges relate to data, cybersecurity, hardware/software integration, and interoperability.

To define levels of maturity, compare the automotive and aerospace industries:

• Level 1: Driver/pilot assistance required (cruise control in automotive/ altimeter heading in aerospace)
• Level 2: Partial automation options available (adaptive cruise, lane departure warning in auto/autopilot engage in aero)
• Level 3: Conditional automation (highway autopilot in auto/ navigation engage in aero)
• Level 4: High automation (active autopilot in auto/ ADS-B navigation in aero)
• Level 5: Full automation (no driver required in auto/just programmed destination in aero)

In my experience, Level 4 is today’s most advanced operation. That system features full automation of CAD, CAM, controls, ERP, and TMS. It still requires some human supervision to inspect castings and perform final CMM while running 24/7.

Collectively, the manufacturing sector is at about 2.5, using some of the newest hardware and software available today. Automotive and aerospace are getting closer to Level 5 as evidenced by the work done by Uber, Tesla, and SpaceX.

Holistic view

In many manufacturing environments, traditional productivity improvement programs, such as Six Sigma and Lean, have been in place for a while and are in danger of running out of steam. The next step is digital integration of manufacturing systems and processes.

Creating and sustaining those savings continuously will require manufacturing cells or locations to cooperate and share best practices. Those actions can eventually lead to the development of industry standards for manufacturing practices.

The common wisdom is that 20% holistic manufacturing speed improvement equates to 15% total cost-per-part reduction – 5x to 10x more than point solution methods. Speed also reduces or delays capital expenditures by improving capacity.

Levy Industrial
www.levyind.com

About the author: Francois Gau is president and CEO of Levy Industrial. He can be reached at 724.875.5358 or francois@levyind.com.

Self evaluation

Before gaining a deeper understanding of your maturity level and beginning the journey to becoming best in class, review three categories related to challenges the manufacturing sector is facing: data/hardware/software and culture/vision/leadership. This exercise can help you build a checklist to evaluate or benchmark.

Data: extracting useful information

• Bad data/no data – Databases everywhere, double entries, paper trails; huge startup costs needed to create a clean data set
• Lots of software – From CAM to CMM, list all software and versions; difficult to harmonize, integrate
• Parts variation – Every part must be modeled, mapped out; building unique models for each part can be daunting, specifically for low-volume shops with high variation

Hardware/software: Successful interfaces essential, difficult to achieve

• Internet connection, cybersecurity – Access to machines, controls; cybersecurity, intellectual property (IP) protection needed for success, peace of mind
• Interoperability/legacy systems – Determine whether systems can be connected; start with MTConnect standard communication protocols compliance first; most machines built after 2005 can be made compliant, with added cost
• Lack of holistic view – Most CAD files lack specifications, tolerance, constraint (metadata) details; if there, often difficult to share between platforms

Culture/vision/leadership: Attitude, knowledge make the difference

• Fear of, or resistance to change – Big investment, all stakeholders must be on-board before investing
• Return on investment (ROI) concerns – Leaders prefer tangible machinery with good ROI; less inclined to invest in systems and tools supporting the machine with soft ROI projections; start small, get early wins to gain internal support
• Regulations – International Traffic in Arms Regulations (ITAR), Export Administration Regulations (EAR), Federal Acquisition Regulations (FAR); certain data may be classified as confidential or subject to restrictions; design program around specific needs; make systems capable of fencing data, IP correctly