The promise of the benefits that can be reaped from Industry 4.0 big data and predictive analytics through access to machine and operator data is compelling enough for most manufacturers to seriously direct their IT and OT departments to look into ways to enable such machine and operator data acquisition.
Historically manufacturers have been focused on yield (quality) improvements only. However, this is just one third of the equation, since equipment availability and performance also directly affect revenue and margin. Being able to optimize overall equipment effectiveness or OEE in terms of equipment availability, performance and the quality of items produced by the equipment is the Holy Grail for manufacturers to improve their bottom lines.
A fully integrated solution requires other aspects of the manufacturing OT and IT infrastructure to be addressed as well. Depending on the scope, GEMBO is able to leverage its partner ecosystem, consisting of Yield Improving Software Vendors, Manufacturing Execution System Vendors and Backend Infrastructure Vendors, to augment their solutions in order to be able to deliver an end-to-end transformational OT/IT solution.
In the quest for big data and predictive analytics the question of rolling a solution in-house vs. outsourcing comes up inevitably. Given the many aspects of a well-designed solution, it is instructive to look at the pros and cons of in-house vs. outsourced.
BENEFITS PERCEPTION OF INDUSTRY 4.0
Rather than focusing on a laundry list of items to consider pros and cons for, it is more useful to boil this list down to the following six KPIs to measure and compare both approaches by:
- Flexibility: The ability to add-on new features or to modify existing features.
- Resources: The effectiveness and availability of human resources.
- Speed: The ability to meet project timeline goals.
- Know-How & future-proofing: The Domain expertise to ensure project success.
- Cost: The cost of human resources and cost impact on other projects.
- Ongoing Support: The resources needed to ensure post-project success.
The following presents an analysis on how each KPI may play out in both cases.