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This is Part I: Data models and creating a “plug-and-play” environment of a 2-part series. Forward-looking food and beverage manufacturing operations − through horizontal organizational alignment and better data capture and integration − have driven significant business performance. In my most recent blog, I reviewed some organizational constraints that prevent food and beverage manufacturers from maximizing operational efficiency. These obstacles include a traditionally reactive culture, an absence of integration across process silos, and an overall lack of agility across operations. Improved data integration provides a powerful means for breaking down business barriers and silos. To achieve the benefits of data integration, organizational data must be accessible, structured, relatable, and easy to interpret. (It must also be reliable, trustworthy, and transparent – more on that in my next blog.)
Each organization faces its own unique data challenges, however. Data may not always be available or accessible. Data may exist in the wrong formats or reside across multiple, incompatible systems. How does this happen? As food and beverage plants evolve over the years, islands of systems form, independently developed to cater to specific needs.
In some cases, third parties are brought in who install “black boxes” that are never fully owned by the manufacturer (although they generate services revenues for the third parties). The third parties wanted to exercise control over the data and the know-how, while the manufacturers wanted to gain full ownership and drive continuous improvements. Under this scenario, the control of the future destiny of the operation devolves into a win-lose situation. Such a business model is no longer sustainable. Technology providers, OEMs, and end users all must work together to devise an approach that guarantees a win-win-win situation for all parties involved.
The power of data integration models to help future-proof operations
In order to phase out traditional siloed approaches, technology modernization projects should no longer be restricted as relevant to only one area of the operation. As new solutions are defined, stakeholders need to be cognizant of both upstream and downstream processes and consider what may be needed in the future from the solution currently being deployed. Building in such contingencies up-front provides simpler and more cost-effective system integrations for the next series of projects. Although recently installed systems may not be connected right away, the “hooks” are already in place to allow for easy connection in the future when the organization requires an agile response to an unforeseen marketplace change.
Another important factor to consider is creating a data model that identifies the elements of the complete plant value chain and then defines how the various pieces of the operation can be bolted on. For example, company sustainability executives want access to SCOPE 3 emissions data for shareholder and regulatory reporting. To generate such reports, access to multiple data sources is required. When integrated systems are implemented across the value chain as “plug and play” components into the data model backbone, the task of data extraction becomes much easier.
Similar approaches can also benefit life science industries. Consider legacy serialization solutions as an example. In many cases, the original serialization solutions were driven by quality departments and were designed to address regulatory requirements. The solutions were not intended to accommodate the planning, operations, or logistics needs of the supply chain or even the health and well-being of the end customers who use and consume the pharmaceutical products. For example, issues such as the circulation of counterfeit medications were difficult, if not impossible, to trace and track.
Today, however, it is possible to integrate the upstream and downstream value chain cost-effectively − using digital solutions that enable end-to-end visibility and traceability (even to the level of a single product lot) − through quick capture, consolidation, and analysis of data.
For more information
To learn more, download our e-guide “Empowering your workforce through digital transformation in food and beverage manufacturing.”
