Digitalisation Business Optimiser

Assess-Choose-Action Framework


As with any task or project, it is imperative that a thorough assesment is carried out in order to fully understand the full picture.  It is also a way to create a benchmark which should be used in the future to measure the success after completion.


Often when properly assessed there will be multiple opportunities identified.  A systematic and methodical  approach is needed at this stage to consider all factors before deciding on which opportunity or opportunities to be tackled.  


For the successful outcome , it is important to take action quickly before momentum is lost.  Assigning a project owner who will be accountable for the completion must be carefully considered.  Projects can fail or be delayed unless a suitable project owner is assigned.  They will need the time, and be properly resourced to ensure success.

Upskilling and properly implemented training is vital also.  Often this point is left too late or not considered at all.  


This stage can make the difference between an ok and a great success story. Although it is important to stay within the scope and boundaries of a project , one must not shy away from adjusting and improving based on new information coming to light 


It’s time to celebrate the wins but also record the lessons learned – These nuggets of experience and knowledge should be documented and shared.  Any practices or procedures should be updated.  Often this step is missed in the urgency or eagerness to get started on the next opportunity


Intelligent products

Develop connected, self-aware products that are capable of sharing information about their health, location, usage level, storage conditions, and more. The data these smart products share can help improve everything from product quality and customer service to logistics and R&D. They can also anticipate service needs, receive remote upgrades, and open the door to new, service-based business models.

Intelligent factories

Referred to as smart factories – highly digitized, largely autonomous facilities that take full advantage of advanced technologies like Big Data, artificial intelligence, robotics, analytics, and the IoT. Also called Factory 4.0, these plants are self-correcting, employ smart manufacturing 4.0 processes, and make it possible to deliver customized products cost efficiently and at scale.

Intelligent assets

Almost every physical asset deployed today has built-in sensors – which, when connected to the IoT and analytics, are game changers for enterprise asset management. With intelligent assets, technicians can monitor asset performance in real time, anticipate and prevent downtime, employ dynamic and predictive maintenance, take advantage of digital twins, and tightly integrate assets and business processes.

Empowered people

No matter how autonomous your systems get, you will always need people. Empower them with technologies like AI and access to live sensor data – so they know what’s happening on the shop floor and are ready to make quick decisions and handle issues as they spring up. Wearable devices and augmented reality apps can also help them solve problems, monitor their health, and keep them safe.

What is Industry 5.0?

Industry 5.0 is already being spoken about and involves robots and smart machines allowing humans to work better and smarter.  

By connecting the way in which man and machine work together, estimates say that Industry 5.0 will mean that over 60% of manufacturing, logistics and supply chains, agri-farming, and the mining and oil and gas sectors will employ chief robotics officers by 2025. 

The European Economic Social Committee asserts that, “The proliferation of robotic automation is inevitable.”

AI and machine learning

AI and machine learning allow manufacturing companies to take full advantage of the volume of information generated not just on the factory floor, but across their business units, and even from partners and third-party sources. AI and machine learning can create insights providing visibility, predictability and automation of operations and business processes. For instance: Industrial machines are prone to breaking down during the production process. Using data collected from these assets can help businesses perform predictive maintenance based on machine learning algorithms, resulting in more uptime and higher efficiency.