Moving manufacturing into the competitive 4th Industrial Revolution world demands a sequential/phased approach, typically over 6 stages and resulting in a Smart/Digital Factory.
Step-1 is the macro-strategy and vision to build for company-wide adoption.
Step-2 is connectivity that identifies ways to build the foundations for intelligence - namely data.
Step-3 (before you go out and buy lots of disconnected technology) is integration that brings together operational and information technology.
Step-4 is making use of the connected data that's gathered
Step-5 with so much data "flying around" is the use of AI to leverage it at scale
Step-6 moves us into "at scale" whereby depth and breadth across the ecosystem propels new collaborations.
There are followers and there are leaders. There is Defense and there is Offense.
Stepping into new ways of doing things demands a preparedness for things to fail, things to be learnt and speed.
An unwavering focus on where you want to be, exploring the unknown at speed but with reviews as you go, means you become better for the future than your competitors.
Getting it right in the 4IR, means improving efficiencies, cost savings and responsiveness to the market demands.
It starts and it ends with leadership - a clear vision, belief and resilience.
The business case
Yes, the ROI is a pre-requisite for most companies and the business case has to be in place with a clear vision, goals and objectives of what is to be achieved that fits the organization’s business model.
Early and fast adoption is significant for a competitive advantage and cultural change over late adopter competitors.
Leading from the front
Due to the nature of new technology programs; their complexity and cross-departmental impacts, it's critical someone leads the charge, who has the right soft and technical skills to:
Oversee the strategy
Facilitate activities across the impacted people
Manage the project implementation and adoption
Communicate progress up & down the organization
As I say over and over again, you can have the best technology, the best processes, but unless your people are prepared to move with them, it will be a waste of effort and money.
Bringing your people with you, into the future relies on a readiness to enable them. Their technical adaptability, maturity, skills and willingness to invest time in new ways needs full consideration.
A momentum strategy and change management approach needs careful deployment.
The connectivity across the Operational and Information Technologies is the platform upon which a Smart Factory is built.
Having a dedicated IT infrastructure in place for Industry 4.0 technologies should be considered, for example a local on-premise data center, or potentially a hybrid on-prem/cloud solution. But for now, at least, anything purely cloud based, with mission critical systems running, isn't something most would consider.
To be smart relies on data and the capturing of this comes from the IIoT. Collecting, storing and harnessing this data, in order to provide information, initiate events and prescribe a course of action starts here.
The total solution comes in various forms:
The "Things" - is the hardware attached to the product/equipment from where the data is collected
The "Sensor" - detects and measure the parameters of interest
The "Edge" - consists of embedded hardware/software to control the functionality of the sensors and performs onboard processing of the data.
In manufacturing, your typical CNC Machines and Robots combine these first three elements.
From here a communication layer enables the transporting of the resulting data. Key communication protocols for data representation, widely used in manufacturing are OPC-UA, DPWS, UPnP, MT-Connect, DDS and other key messaging protocols such as MQTT, JSON, XML, HTML, HTTPO, COaP and REST.
The Things, Sensor, Edge and Communication elements can sometimes be combined in one device often referred to as an ‘IIoT Gateway’ combing both the hardware and software with internet-enabled components enabling communication and data transfer to a digital platform or infrastructure, as detailed below.
Communication is typically with an Application layer that consists of software components to communicate and manage IIoT devices.
Then comes the Processing & Analytics that convert all the data into useful information. Additional insights into this potentially huge amount of data can come from Data Science and its techniques for deeper insights and even contextualizing it with the data collected from other connected systems.
Lastly, in the immediate solution there sits the dashboard where the meaningful information and insights are presented in visual format.
Here we integrate the OT & IT functions and we make sense of data and technology in a practical way.
IT manages business applications from the front office and the OT keeps the plant operations run smoothly.
The integrating of the two are the backbone of Smart Factory Architecture.
It's the vertical integration of enterprise and manufacturing operations (ERP, PLM, MOM) and industrial automation, along with the horizontal integration of IIoT on Product, IT Systems across the production and Supply Chain is arguably having the greatest impact on manufacturing today.
Today, in turbulent times, realtime visibility becomes more and more necessary. Agility demands fast intelligence. ERP systems used in the business operation level play a major role in connecting day-to-day activities across the manufacturing facility, providing essential insights into operations and the manufacturing process integrated into one database.
In the operational management level, manufacturing operation management (MOM) systems and manufacturing execution systems (MES) operates to bridge the gap between the enterprise business level and the automation levels but there are limitations and complexities associated with connectivity and vertical integration of real-time production data. They are still heavily dependent operators’ inputs and lack data processing analytics capabilities.
Connectivity, vertical integration and synchronization between the manufacturing operations levels are essential for greater insight into the production process for rapid decisions making, corrective actions to be taken and to enable optimization.
There are already many platform solutions to overcome the complexities of IT and OT integration to solve the challenges of interoperability (using open and multiple standards and protocols), data security (using best practice CS standards and protocols) and scalability (via cloud ready solutions).
These platforms do not completely replace or rebuild long established systems but acts as a new layer to the infrastructure orchestrating the new way of Industry 4.0 in smart manufacturing.
IIoT platforms with built-in MOM/MES applications are a potential option. They are capable of integrating with enterprise and industrial automation applications and are accompanied with the elements of the IIoT solution (analytics tools, IIoT gateway connectivity, cloud scalability).
Other options combine industrial code-less IT systems with built-in software bridges, modules and communication protocols that are capable of connecting and integrating data across the manufacturing operation layers.
Another option is to adopt a full IIoT platform to seamlessly connect and integrate all layers of the manufacturing operation, enhanced with additional manufacturing operation applications
All three strategies have the potential to combine data from different manufacturing operation levels enabling capabilities to move to the next stages of the roadmap.
This is what's commonly known in i4.0 speak as Big Data due to the volume, variety, velocity, variability and value. Big Data in manufacturing is categorized in two ways:
structured data (data that comes from traditional database and equipment)
time series data (continuously changing and event-based data)
The volumes of data being collected is vast and as a result should be initially extracted, handled, cleansed, time stamped and processed at the Edge/IIoT Gateway.
The traditional view of the analytics framework, that drives improvements of industrial operations, is divided into four types:
Descriptive analytics are a set of metrics that describe the event (what happened) and are referred to as key performance indicators (KPIs).
Amongst the most common KPIs that manufacturing relies on (for managing operations) are mainly focused on financial business performance (e.g., manufacturing cost per unit), efficiency focused metrices (e.g., factory efficiency, overall equipment effectiveness (OEE)) and quality related metrics (e.g., scrap rate, yield).
However, more from the acquired data can be achieved by implementing a range of KPIs that allow for the assessment and tracking of the manufacturing process as well as success evaluation in relation to the goals and objectives. Reporting of KPIs can also be improved using real-time digital visualization dashboards built within MOM applications, IIoT platforms or business intelligence (BI) tools.
Asset condition monitoring is a snapshot of the health conditions of the assets in the factory (e.g. manufacturing machine). Tracking changes of the status, performance, utilization, etc of manufacturing machines enable significant change to be easily spotted once occurred.
Historical records of the snapshots collected over time from various machines in the production line enable comparison and performance analysis to be performed.
For example, machines utilization history, maintenance history, failures modes etc. can be captured and compared with its peers to identify trends and behaviors related to the manufacturing process. This is valuable for diagnostic analytics to enable manufactures understand why things happened.
In the manufacturing industry, AI is seen as the brain of the Industry 4.0 transformation while other digital technologies provide the muscles to drive the transition from automation to autonomy.
The main benefits of AI in manufacturing include:
improve cost savings
provide better customer service
create better workplace environment
Applications of AI and the use cases vary, but one of the more impactful is the Predictive Maintenance of machines and tools that has the potential to reduce maintenance costs by 12%.
AI based learning algorithms spot trends in the data for early warnings and indications of possible failures and breakdown. This allows maintenance to be scheduled and intervened rather than being dependent on regularized checks, enabling a proactive behavior instead of a reactive behavior, which leads to a more reliable and sustainable production line.
AI application also plays a crucial role in empowering the workforce with a baseline knowledge to improve process efficiency and productivity. AI based algorithms can provide the workforce with real-time recommendations to act upon especially for the young and inexperienced.
They can also autonomously act to address raised issues making machines to adjust themselves in order to optimize quality or energy efficiency during production operations.
Other use cases of AI in manufacturing include but are not limited to:
improvements in quality inspection using images processing and recognition techniques for process optimization and scrap reduction
safety monitoring and control using AI-based self-learning models embedded within manufacturing automation systems (e.g., robots). These systems learn from prior experience and human interventions and react to unforeseen situations, resulting in taking pressure off people, reducing human error and improving workplace health and safety
production scheduling using AI, combined with mathematical optimization tools to plan, schedule and optimize capacity planning.
The challenges ahead
Manufacturing Transformation such as this is what I call real Digital transformation. And Digital Transformation is associated with change and therefore, resistance is expected. We do farmer to avoid pain (change) than we do to gain pleasure!
Understanding and accepting change and effectively managing it within the organization is key to the successful transition into a Smart/Digital Factory.
Manufacturers need to:
get the buy-in from the organization,
have your stakeholders on board,
build your communication lines,
address any raised concerns, such as fear of the unknown, no personal reward, job security, untrustworthiness in the technology, etc. and
establish cross functional teams to help achieve and equip themselves for the change.
Increasing digitalization is forcing workforces in production facilities to change how they work on ordinary tasks and to allow them to spend more time on high-value activities dealing with what if scenarios such as monitoring datasets for preventative maintenance.
There are a number of skill gaps are associated with these digital skills, using the technology and managing it especially for an aging workforce.
Manufacturing companies must be prepared to support this development making sure that their existing workforce has the required level of digital skills together with other soft skills such as problem solving, creativity and critical thinking in order to help existing employees get up-to-speed with new technologies in addition to recruiting people who already have the digital and technical skills.