Some of the responsibility for maintaining smart manufacturing equipment will likely fall to IT departments. Here are some actions tech leaders can take to manage the transition.
U.S. tax reform, which reduces corporate tax rates from 35% to 21%, could prompt manufacturing companies to repatriate billions of dollars that have been stashed abroad to the U.S., with companies pumping some of this money into plant revitalizations and makeovers for Internet of Things (IoT) technology that will pave the way for smart manufacturing. Deloitte defines smart manufacturing as a "digital supply network" where one "can use a constant stream of data from connected operations and production systems to learn and adapt to new demands."
One of these demands will be responding to ever shorter product life cycles —going from 12-18 months to less than one year for many products — with agile manufacturing practices.
This brings us to IoT and the role of sensors, routers, hubs, servers, and other equipment that is likely to be localized in the same plants where manufacturing is occurring. IoT and automation will give plant supervisors real-time insights into how machines are running, timely notifications for when machine maintenance is called for, real-time or near real-time reports on production status, and a gateway into present and predictive analytics that technicians and supervisors can use to fine-tune manufacturing .
SEE: Internet of Things policy (Tech Pro Research)
You might think that IT would take an ancillary role in this situation, but that won't be the case. IT will still be responsible for the following:
- Defining the IT architecture for big data so it includes smart manufacturing
- Assuring that security and compliance measures are met
- Defining and developing present and predictive analytics reports for manufacturing users and for all others with a need to know
- Working out contracts with edge computing suppliers
- Contracting for the bandwidth needed to tether these new IoT devices together so they can exchange information
- Contracting with cloud services providers for the transport of any localized machine data that needs to be transported or stored elsewhere
- Developing active partnerships with plant managers, technicians and engineers
- Developing IoT failover and DR strategies
- Cleaning data so machine jitter like communications, handoffs, and acknowledgements are removed
The task loads for each of these items alone is worthy of an article, but the takeaway for IT leaders is that this list is not in the traditional IT playbook — which means that IT must adapt to this digital transformation.
SEE: Digital transformation: A CXO's guide (ZDNet/TechRepublic special feature)
Here are some tips for CIOs and others leading this effort:
Revisit your data architecture
This data architecture, if it doesn't already, should include all edge computing at plants around the world, as well as identifying all local data caches for IoT data that is generated in plants, then deciding which data will be retained, what will be eliminated, and what will ultimately be transported to more centralized data repositories as part of your big data aggregation.
Extend failover and DR practices
The more you automate, the more you increase the risk of your automated systems failing. Not only should plans be made for backup and data replications at local facilities where machines are generating data—but you should work hand-in-hand with manufacturing personnel as well to ensure that operations can keep moving if operators have to go into a manual mode while systems are being recovered.
SEE: Secure equipment repair policy and confidentiality agreement (Tech Pro Research)
Assist manufacturing in developing equipment maintenance practices
Once of the immediate benefits of IoT will be the ability to receive alerts from machines that they are in need of maintenance before they fail. More than likely, manufacturing techs on the floor will be given this task. They can benefit by borrowing from IT's equipment procedures, which have been perfected from years of maintaining computerized equipment. CIOs should consider transferring these tried and proven practices to manufacturing and if necessary, assisting manufacturing in establishing the duties of a maintenance tech.
Define the data "hop points" in your data architecture
Edge computing is characterized by data being kept proximate to the machines that generate it on the factory floor. But at some point there is a need to collect and aggregate this data for analytics. This means that localized data needs to be shipped to central data repositories that exist in your data center or the cloud. IT is the best department to determine where this centralized data should reside, and to steward that data.
Enact strong security practices on the floor
Users get used to signing in with their personal ID and password —but they can forget that security is not just of the cyber variety. Security also means that physical areas where critical equipment is kept should only be accessible by those who are cleared to work or be in those areas. This is an area where IT can assist by performing periodic on-site security audits to make sure that physical as well as cybersecurity practices are in place.
- How IoT and big data improved Toyota's manufacturing process (TechRepublic)
- Report: Smart manufacturing market to hit $395.2 billion by 2025 (TechRepublic)
- Industrial IoT: 64% of manufacturers will have fully-connected factories by 2022 (TechRepublic)
- 5 ways to advance robotics in manufacturing (ZDNet)
- This robot-run 3D printing farm is the future of light manufacturing (ZDNet)
- How blockchain can transform the manufacturing industry (ZDNet)