Data storage considerations for digital thread implementation

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Digital threads are data-intensive architecture frameworks that support digital manufacturing and  transformation in manufacturing organizations.

As we know, digital thread is an architecture framework that integrates end-to-end supply chain (design, engineering, suppliers, manufacturing, maintenance, warehouse, transportation, warranty & service operations) seamlessly by capturing meaningful data across product lifecycle. The objective is to provide operational insights and make it easier for you and your management to improve, optimize your organization’s business, and even provide an opportunity for transforming the existing business model –  for example from “selling products” to “selling services” (Product-as-a-Service).

It’s best to consider the basics before you start designing your data supply chain for your digital thread project:

  • Obstacles and objectives, you wish to prioritize and address in the short-term, mid-term, and long-term horizon
  • Key stakeholders
  • Metrics/KPIs that you and stakeholders want to monitor in order to measure the performance and ROI
  • Data elements from various source systems followed by data collection strategy and data retention policies
  • Type of products, complexity of supply chain, number of components, cycle time, manufacturing operations, number of machines in a production line,  inspection stages, test stages, types and numbers of test stages, etc.
  • Future requirements and business roadmap that your organization is planning.

Often, you may want to skip to outcomes due to time and budget constraints, but these considerations are crucial in defining the scope of the digital thread project and in estimating infrastructure needs, such as data storage, for the digital thread implementation journey to ensure scale and sustainability.

In a high-volume manufacturing scenario, an organization’s processes will generate millions of records per day and the average size of each record (assuming an xml file format) could be in the range of 50Kb. This translates into provisioning of minimum 50GB of space per day per production line (depends on product, data, and volume). Additionally, you must consider storing at least one year of data in the active database/data warehouse for more effective operational insights and predictive intelligence, along with growth in production volume, warranty & service strategy, and data retention policies.

On-premises data storage may not be a good option for your organization as it consumes finite storage capacity more rapidly, which would trigger a non-value-added process for your IT & business teams for “addition of more storage.” The process of adding more storage may have a negative impact on business continuity (due to system shutdown). Therefore, it is a good idea to consider the cloud for data storage while estimating and planning for the infrastructure in the design phase. As we are aware, cloud solutions are an easy “pay as you consume” model, flexibility comes with surety of high up times as compared to. Increasing storage capacity in a cloud option is much easier than in a traditional on-premises model. While selecting the cloud option, it is highly recommended to evaluate the below points in detail to avoid conflicts:

  • Confidentiality and security of operations data: Usually, operations managers do not allow exposure of shop floor data (coming from production machines, inspection and test systems, As designed, As Built BOM, etc.) to cloud due to confidentiality and potential threats and hence it requires additional security measures. To ensure security, one option is to retain complete control of data instead of transferring to cloud service provider while cloud service provider holds the user’s security and privacy controls.
  • Quality of service: Availability of cloud application, performance, scalability, and responsiveness (turnaround time for the new request) should be considered to ensure streamlined operations and data supply chains.
  • Performance and costs: Digital threads are data-intensive projects and hence entail higher costs due to bandwidth requirements. So, you must evaluate the trade-offs (i.e. benefits/ROI vs cost) and consider a metered, dynamic pricing model or a fixed cost model for each service.
  • Governance: Governance must be strong and effective to ensure that information and data are used in accordance with agreed-upon policies, procedures, and business objectives.

Conclusion

Digital threads are data-intensive architecture frameworks that support digital manufacturing and  transformation in manufacturing organizations. We believe that Cloud will play  a crucial role in terms of providing flexible architecture and data storage policies which would facilitate efficient & accurate data supply chains, better performance, and minimized interruptions to support business continuity.

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