COVID-19 has changed almost every aspect of how we live and work. Prior to COVID-19, processes deemed accessible, reliable, and robust during normal times, e.g. when offices and worksites were fully staffed, networks were accessed from onsite, and supply sources were at usual levels of inventory and distribution, have, in many cases, been exposed as being fragile and easily disrupted. More than ever before, decision makers at all levels require accurate and timely decision ready data as well as the automation, generation and escalation of alerts and work orders. COVID-19 has further driven the need to access data remotely. The proposed solution is to install robust and secure Internet of Things (IOT) capabilities in concert with devices and utilities. Using these new technologies to model, monitor, and analyze unlocks the ability to proactively get information to decision makers at all levels, regardless of where they may be located. Data can also be used to generate work orders and maintenance actions. It allows a historical look at what has happened in the past to predict what should be done today. It also allows for future planning based on data and digitally generated scenarios of future possibilities without the limitations, delay, and cost of trying to execute those scenarios manually. To prove this, we propose: 1. Identify a small representative sample of current fragile systems or processes a. Simulate a fragile environment, we propose using an inventory and need management solution with example product x b. Simulate hot anticipated need zones, connected zones for logistics, supply stock and capabilities 2. The placement of a POC sensor and data into the Platform matching current manual efforts (i.e. prove it can do the as-is) 3. With IOT Platform POC model and generate analytic historical analysis to provide future logistics model a. Generate model for period of time, compare vs. actual b. Regenerate models using situational variables for source data c. Deliver automated communication delivery for alerts, coupled with representative system API calls that would, under a full deployment, automate actions in response to identified problems. 4. Secure wireless comm technology for sensoring with store and forward capability for outages. E.g. The system continues to collect data until the outage is resolved and then the data is forwarded to the receiving system or database. (aka. Asynchronous data collection) Phase I Technical Objectives 1. Create a true digital twin of an existing process considered fragile today 2. Prove the digital twin can generate the exact actual outcomes (i.e. prove its a true twin) 3. Utilize data analytics to build future prescriptive models and serve data (real-time and most recent) to remote areas using multiple methods (e.g. AR/VR, tablets, laptops, phones) 4. Utilize Machine Learning in what if scenarios that mimic potential process modifications and subsequent process reaction.
Benefit: By combining sensoring and data analytics capability with digital twin scenario processing, the DON can exponentially increase their ability to provide robust planning and focus critical personnel resources where they are most needed. This solution would provide: - Advanced planning for situations and locations based on advanced predictive models with validation - Actionable insights and instructions at the edge - Connected enterprise - Secure access and deployment - Multi-process data streams - Agility - Access to new data insights and - Reduced costs.
Keywords: IOT, IOT, business intelligence, edge, Artificial Intelligence, Logistics, VR, Digital twin, readiness
