Why buy when you can gain SME Insights instead! Digital Engineering as a service, is this the new commercial contract from a manufacturer?

The client facing commercialized Digital Twin concept is relatively new to the supplier / manufacturing world. However, a previous iteration of this has been well embedded in the manufacturing industry for decades. The concept of having a digital representation of an asset to facilitate the manufacturing process and look to improve this process driven by margin and market competition. With the concept of continuous improvement product development, the R&D never stops with a key focus in mind (Quality, Cost, Availability = OEE).

As the market and the customer data matures and becomes more sophisticated and data driven, should the commercial contract also. Could we see a world where no longer manufactures sell a product, but co-manage an asset (Conveyor, Crusher, Screen, Pump) and sell a subscription style contract to the data insights to improve production, cost, and safety for the asset owner.

OEMs have been managing a continuous improvement process ever since they entered the market and therefore their SME’s and domain experience far out ways the commercial complexities of a supply contract that is looking to negotiate the lowest price for the product. Could we see OEMS starting to move to a subscription-based business model where they take equal share liability of an asset and in turn a loss initially over the first period but then are driven by value not cost, as they look to add to the asset and agreed KPIs, like that of a Saas arrangement.

Are we seeing not only the convergence of data science and spatial reference and location in time but more importantly how the supply chain now reacts commercially? Is the missing piece in a true digital twin the commercial requirement to move to this to achieve market driven insights more so than asset driven insights.

Could we see 100s of small-scale digital twins of assets in multiple ways, shapes and forms driven by necessity of commercial contracts more so than an aspirational digital transformation project instructed by senior management. Innovation typically only happens by necessity and not by choice. Unless we need something desperately, a new idea without a need has no purpose. The biggest driver in business associated to necessity is cost, so should this be where we start from a digital twin perspective in the mining industry more so than engineering and data management. ?

By transitioning to a data insights digital twin from manufactures and OEMS, instead of a huge raw dataset un-interpreted and captured at the edge by the asset owner themselves, this enables quicker and more intelligent use of time by the reliability / asset management teams. Therefore, instead of learning and continuously looking to refine their data science analysis based on the ever-evolving data and expectations, they concentrate on correlation of insights to gain actionable business value, not a raw data driven digital twin, but a data insight driven digital twin where time and the actions from the insights is of more value than the raw data itself.

We have seen in recent times an expectation that all professions develop data science skills to an extent with huge datasets acquired and accumulated over time. But is this actually a distraction to our main purpose and KPI. Do we all need to be data scientists, or should we just concentrate on knowing what action to take based on the data analysis provided?