This and the last year has been eventful for all sectors, but the pandemic has been particularly devastating for the manufacturing sector, which relies on the delicate balancing of supply chains to work effectively. As one of the first industries to adopt robotics, automation, and IoT, the sector has a long history of technological advancement. Manufacturers have access to an enormous amount of data.
But less than 11% of the data is used to drive business decisions.
IDC survey found several impediments to companies extracting value from their data.
The fundamental one is the lack of integration of the technology infrastructure. For instance, although the growth of IoT devices at the edge of manufacturers’ data networks has been rapid, these devices are disconnected from manufacturers’ headquarters system. Data collection points are dispersed and uncoordinated, making it hard to use data to achieve desired business outcomes such as increasing throughput. Furthermore, this still leaves Information and Operations leaders driving siloed decision making.
Deriving intelligence from data is another roadblock. Manufacturing lags behind other sectors in terms of multi-cloud and hybrid cloud adoption, and the sector also has a lower likelihood of having a central data centre. As a result, data is either discarded rather than being backed up for long-term storage or stored in long-term storage but never analyzed. Managing the storage of collected data and deriving intelligence out of it in a timely manner to drive business decision making is the second most significant problem, behind making the data available.
Ageing infrastructure is often the cause of the slowdown. For instance, data management may find it challenging to incorporate the latest sensor varieties on various factory machines. Or, the ageing infrastructure may not be capable of handling the volume of connected assets entering the facility. As a result, plants often use ad-hoc processes to link and manage assets in the absence of an underlying infrastructure that enables comprehensive management.
Additionally, a skills shortage exists. Manufacturers are confronted with an ageing workforce and a shortage of qualified workers willing to work on the shop floor. Due to the scarcity of new talent, manufacturers can only invest in upskilling current employees – which is not a bad thing on the surface but is not a long-term plan on its own. If skilled workers are the future of manufacturing, a dearth of sufficient skills is one of the most difficult obstacles to overcome.
Makoro™ alleviates these barriers to adoption.
- Makoro™ has an open architecture and can be deployed in any configuration. It can be on a fully managed Makoro™ Cloud (managed by us) or in your infrastructure in a BYOI (Bring your own Infrastructure) model or in any cloud and edge configuration.
- Makoro™ can work with a wide variety of assets and IT and OT systems. If the system has data in any form, Makoro™ can tap into that data to demonstrate value. You do not have to “big-bang” upgrade your current assets and/or infrastructure.
- Once deployed and connected to your assets and OT and IT systems, Makoro™ continuously delivers intelligence based on your data, so your data is not just stashed away without analysis.
- Natural language recommendations from Makoro™ augment your workforce. It can capture tribal knowledge, rapidly onboard new employees and enable them to be productive from day one.
#MakoroAI applies the power of #RecommendationEconomy to #SmartManufacturing, augmenting decisions related to the management of critical assets, so you can manage assets more efficiently with a lower total cost of ownership.
Ignoring the intelligence that your data could provide means passing up opportunities to expand, innovate, and boost your bottom line. Investing in intelligent operations will always have a transformative return on investment, but you need to start somewhere and start soon enough.
It is time to start collecting data and deriving intelligence. All in a single thread, and not as separate stages of the project. And then repeat this thread as frequently as you can.
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