A sequential approach to data maturity – Equipment Data
In my previous post, I spoke about MachIQ Software’s recommendation to machinery companies that are getting started on their digital transformation journey. The closing advice I gave when choosing where to begin with data, was to start with cleansing data that delivers the most immediate value.
In this article, I’ll expand further on this approach by outlining the sequence that we propose most often to our clients.
For machinery companies, starting with equipment data strikes at the core of their business and ensures that they digitalize on very solid foundations.
Typically, this includes data covering:
1. Machine models & variants
2. Equipment serial numbers (install base) & PID’s
3. Project references for companies that build entire lines/plants
4. Bill of materials of parts for the above
5. Equipment documentation
The proprietary data models of MachIQ DCX (for Digital Customer Experience, our cloud-based software suite) enable us to treat such data more rapidly, by limiting the data attributes that our customers focus on, to be outcomes-driven, than having them spend time on information that doesn’t serve a known business purpose.
Most machinery companies have a tough time finding resources for digitalization and most decide to focus on ensuring that day-to-day business doesn’t suffer. With our approach, we deliver tangible resource and budget savings for our customers which they then utilize to reskilling their personnel and rebalancing their headcount in favor of digitally-delivered services.
To illustrate the benefits that working on equipment data can deliver in isolation, let’s consider typical opportunities when it comes to dealing with spare parts in engineering and service use cases:
Modularization of machines is a big source of savings on the engineering side of operations. Identifying commonalities between machines of a given model or product line and clustering them into variants would enable machinery companies to significantly cut their supply chain costs, reduce time to market and therefore order-intake to delivery and invoicing. Eliminating variants further and reducing the number of product references on offer to customers would also reduce sales complexity and shorten the sales cycle.
Eliminating redundant and obsolete parts is also a direct result of cleaning up BoM data and has a material impact on the bottom-line. First, identifying spare parts that have long been stocked but do not serve many customers enables machinery companies to reconsider their consignment stocks strategy. Also, reclassifying spare part categories enable them to consolidate their suppliers further and achieve better economies of scale.
Re-balance engineering resources: Reducing the complexity of machines through modularization frees up a lot of critical engineering resources which are also the easiest to retrain to become digital-ready. Not every mechanical engineer will become a great programmer, but grounded in engineering concepts make the average engineer much easier to train in valorising his/her understanding of electro-mechanical machines into digital assets – especially in IoT and predictive maintenance services, where an understanding of the physics of the machine is critical to developing effective algorithms.
At MachIQ Software, we have observed that these savings alone can finance a large part of the remaining digitalization journey for the average machinery company. Our biggest differentiator for machinery companies is that they can start their digitalization journey with our DCX suite with very limited resources, gradually scaling up their engagement as they accrue the savings that our “Smart Data” approach delivers by freeing up their operational resources.
In our next article we’ll talk about Customer Data. Stay tuned!
Roy Chikballapur, CEO & Co-founder @ MachIQ Software