A path for progress: A guide to supply chain digitization maturity
Medical device organizations can use the following Supply Chain Digitization Maturity Framework as a reference for adopting digital technologies.

Maturity level 1: Building a digitally enabled foundation

▪ ERP: The impact of COVID-19 on supply chains will spur manufacturers’ spend on ERP, which is expected to reach $14 billion by 2024. ERP systems can assist in precise scheduling of work, dispositioning materials, monitoring equipment for output, and managing quality and maintenance issues in real-time
▪ Data integration and management: Connecting data from operational systems (such as inventory management) with clinical systems using interoperability standards (such as GS1, HL7, EDI and FHIR) can help generate business insights, including optimum inventory levels, demand trends, and more. Besides valuable integration capabilities, investing in data quality and governance is crucial in streamlining and accelerating time-intensive data curation and analytics.
▪ Mobile apps: Real-time location tracking of medical device parts not only prevents theft and unauthorized use, but also ensures optimum inventory is maintained across facilities. Remote access to supply chain data using mobile and tablet apps can help device service teams check the availability of parts and troubleshoot issues while on the go.

Maturity level 2: Harnessing digital tools for proactivity
▪ IoT: The Internet of Things has brought unprecedented connectivity, efficiency, and visibility to many different industries, and helps medical device suppliers to realize enormous potential in their operations. Digitally enabled medical devices allow for remote service, maintenance, and configuration of devices – this not only reduces the cost of servicing, but also improves customer satisfaction as they benefit from enhanced operations.
▪ Descriptive analytics: ERP data analytics can generate insights for device demand planning, inventory optimization, cost drivers, and other functions that drive critical business functions. Armed with these insights, medical device organizations can make customized action plans based on practical data, such as checking the status of the stock at medical centers or suggesting production line ups based upon the availability of finished goods.

Maturity level 3: Process improvements through analytics & automation
▪ Advanced analytics: Carrying analytics capabilities a step further, medical device organizations can proactively identify products that require maintenance and notify service staff to carry out servicing even before the device fails. Machine learning algorithms can be used to understand batch release and UDI patterns over time, and identify any suspicious or potentially disruptive activities. Again, this can significantly reduce service costs and support greater operational optimization.

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