Industry 4.0 and the Industrial Internet of Things (IIoT)

The fourth wave of the industrial revolution is here

Industry 4.0 and IIoT are changing everything. Manufacturing is happening through intelligent connectivity between machines and technology, enabling manufacturers to make better, faster decisions, optimize production, and save time and money.

These technologies – advanced robotics, artificial intelligence, sophisticated sensors, cloud computing and big data analytics – all exist in manufacturing today in some form, but as they integrate with one another, the physical and virtual worlds will interlink and transform the industry. Industry 4.0 is still in its early stages, but it's already changing the manufacturing sector in terms of better transparency and agility, responsiveness to customer needs and cost savings.

Key Objective of Industry 4.0

To develop manufacturing to be faster, more efficient, and customer-centric while pushing beyond automation and optimization to discover new business opportunities and models.

Financial Impact of Industry 4.0

Manufacturing topped all industries in IoT investments in 2016 at $178 billion, including hardware, software, services, and connectivity, with $102.5 billion going to manufacturing operations – IDC

Industry 4.0 Maturity Model

Baker Tilly can increase your understanding of how Industry 4.0 can transform your business and improve your competitive advantage. Let's start with gauging your industry 4.0 readiness. To describe our view on Industry 4.0, we've created a maturity model that defines five maturity stages a company can be in and key functional areas the manufacturing organizations typically employ.

Level 1: Undefined / Undeveloped >
  • Incompatible software systems. No data transfers between systems. Heavy reliance on manual labor.
  • No plans/motivation to invest and adapt to advanced production techniques. Unclear on requirements and direction.
  • Minimal capture and storage of large data. Unorganized data storage, hard to access and utilize.
  • Data analytics have minimal use in the value chain. Do not impact processes and offer no value to decision-makers. Limited visibility through KPIs.
  • No automated exchange of data between machines. Minimal use of innovative products. Many occurrences of human error.
  • No investments to allow production data to be visible in the mobile world. Must be at the source to gain visibility.
  • Extended design to market timeframes. Expensive and time consuming prototyping techniques.
  • No change from traditional production processes. Manual labor intensive.
Level 2: Repeatable >
  • Some basic systems are integrated. Plans in place to invest in further integration. Mostly relies on manual data transfers.
  • Benefits of digitalization being realized. Motivation to adapt is being cultivated. Digitalization requirements are being realized.
  • Some data captured in effective ways. Plans to expand data capture and storage becoming an area of interest. Cloud technology starting to be utilized. Less manual labor required.
  • Big Data analytic packages beginning to impact decision making, more employees trained to use. Goals to further utilize analytics in production set. KPIs tracking efficiency.
  • Trial size of innovative products integrated. Minimal Machine-to-Machine interactions occur. Human error a problem.
  • Trial size of innovative products integrated. Minimal Machine-to-Machine interactions occur. Human error a problem.
  • Low customer responsiveness. Little experience with digital modeling.
  • Machines capable of simple automation. High probability of human error.
Level 3: Defined & Integrated >
  • Most software systems integrated. Integration plans are being put into action.
  • Vision of future state beginning to take form. Management establish goals and determine enterprise requirements. Culture shifting to accommodate changes.
  • Implementing advanced data capture systems. Expanding scale of implementation of cloud storage and integrated technology on shop floor. Employees utilizing new data.
  • Analytics important to decision modelling. Large understanding of usage. Moderate range of KPIs, efficiency problems highlighted and trends noticed.
  • Intermediate amount of devices integrated. M2M communication established. Sensors, wearable devices in some areas. Noticeable reduction in human error.
  • Some systems have mobile platforms established. Intermediate amount of data accessible on mobile devices. Employees trained in mobile platforms. Visibility increases.
  • Some systems have mobile platforms established. Intermediate amount of data accessible on mobile devices. Employees trained in mobile platforms. Visibility increases.
  • Introduction of minor robotic automation. Processes/ inventory tracking require machine-human interaction.
Level 4: Measured & Managed >
  • Data flowing throughout most areas of the enterprise. Investments are beginning to show returns.
  • Management has established and is aware of digitalization strategy. Investments are budgeted. Progress benchmarks are established.
  • Integrated technology systems have spread throughout most of the enterprise. Cloud data is accessible to relevant users. Data is being applied to improve operating systems.
  • Accessible and easy to compile data analytics nearly firm-wide. KPIs are essential to production decisions. Trends become large points of reference.
  • Innovative technology in many areas; sensors, wearables. M2M communication covers most of shop floor. Greatly improved efficiency. Large reduction in human error.
  • Mobile software compatible with many devices. Most data is accessible on mobile devices. Employees have a deep understanding of platforms. High level of visibility off site.
  • Large investments in Digital-to-Physical techniques. Product to market in reasonable timeframes.
  • Robots perform most warehousing tasks. Few human errors.
Level 5: Optimized >
  • Complete software integration throughout entire enterprise. Optimal level of compatibility between systems in all areas of business.
  • Culture adjusted for digital shift. Requirements are clear and defined. Timetables and budget are established.
  • Complete integration of data capture systems. Cloud data is organized and easy to access firm-wide. No manual labor required. Data is shaping decision making.
  • Data analytics are essential through-out value-chain. Historical trends captured and displayed. Wide-range of KPIs available. Clear, concise diagrams accessible.
  • Smart Factory status. Interoperability across all machines. No human error. Optimized efficiency.
  • Completely integrated mobile functions. Data accessible on all major mobile platforms. Complete off-site visibility.
  • Firm-wide digitalized prototyping technologies. Responsive to customer requirements.
  • Full utilization of robotics in warehousing. No human error. State of the art inventory tracking.

News and Insights

Maturity Model Inquiries

Peter Pearce
248 368 8838

Media Inquiries

Mary Gray-Craddock
312 729 8065

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Maturity Model Inquiries

Peter Pearce
248 368 8838

Media Inquiries

Mary Gray-Craddock
312 729 8065