Ruminating on IoT revolutionizing the supply chain

Smart sensors (IoT) are pushing the frontiers of supply chain technology. By utilizing sophisticated sensors, Logistics Providers can enable greater visibility into their supply chains.

Smart sensors today can measure a variety of environmental variables such as GPS coordinates, temperature, light sensitivity, humidity, pressure, and shock events. These sensors then wirelessly transmit the data to the enterprise systems in real-time. For e.g. SenseAware provides smart sensors that can be dropped into any package and enable tracking of all these environmental variables throughout the shipment journey. Roambee provides intelligent sensors called 'bees' to monitor shipments.

Given below are some of the business use-cases where smart sensors can add value to the supply chain.

1. Cold Chain: A lot of shipments need tight temperature controls during the entire journey - e.g. bio-medicines, insulin, blood, live organs, vaccines, perishable food items, etc. By using temperature sensors, organizations can monitor the temperature excursions and take corrective action like re-icing the shipment, etc. 
2. Improve security of high-value products: By utilizing sensors, we can now track the location of each shipment in real-time and raise alerts if a shipment has deviated from its planned route. Most sensor-based platforms enable users to define geofences and trigger alerts if the shipment is moved outside of the geofence. This can be very useful for high-value products such as gems, jewelry, surgical items, etc. 
3. Enable faster cash collection: In many industries, suppliers are unable to invoice their customers till they get confirmation of the shipment delivery. By leveraging 'light sensors', suppliers can be notified that their shipment has been opened and hence considered to be delivered. This would enable suppliers to raise quicker invoices and thus faster cash collections. 
4. Reduce buffer inventory: Many manufacturing units maintain buffer inventory to avoid stock-out situations. Lack of information on inbound logistics (delivery dates) results in higher buffer inventory. By leveraging smart sensor-based logistics, manufacturing firms would have greater visibility into inbound goods delivery as they can track the location of shipments in real-time. This can result in tighter inventory controls and lower TCO. 
5. Reduce insurance premiums: Over a period of time, all the data collected by sensors can be utilized by an insurance firm to reduce the premiums for customers who take tangible steps to ensure the safety and quality of the delivered goods. For e.g. If Pharma company A is doing a better job at maintaining tight temperature controls than Pharma company B, then it makes sense for the insurer to incentivize Pharma company A. 
6. Avoid delivery penalties: Large retailers such as Walmart have stringent rules on shipment delivery times. It imposes a penalty if a shipment arrives earlier or later than its scheduled time-slot. By leveraging smart logistics, vendors can monitor their shipment delivery times and take corrective action. 

Thus, smart sensor-based logistics can provide business value across a range of industries. The combination of smart hardware sensors and scalable software platforms can help organizations build a new central nervous system for their supply chain.

Ruminating on EMPI

EMPI (Enterprise Master Patient Index) is a consolidated hub of all patient related information that would act as a single source of truth.

Hospitals have various departmental systems such as lab systems, radiology systems, EMR systems and other Health Information systems that operate in isolation. Typically patient data is spread out across these disparate systems and it is challenging to have a 360-degree view of the patient.

Hence, hospitals create an EMPI hub that assigns a unique ID to each patient. EMPI systems use algorithms to match and link records across disparate systems. The algorithms also identify duplicate records and reduce the number of false negatives. The typical attributes used by the matching algorithms are first name, last name, DOB, sex, social security number, address and more. The matching algorithms (deterministic, probabilistic/fuzzy) must consider typos, misspellings, transpositions, aliases, etc.

Besides the internal attributes, many organizations also source data from external third parties (e.g. Equifax) that can be used for increasing the accuracy of the matching engine. This is helpful as people change addresses, phone numbers, etc. with time.

Many traditional MDM product vendors such as IBM, InfoR provide platforms to implement EMPI.
Few organizations have also started using NoSQL and other Big Data platforms for creating a customer hub as explained here.

Analysis of Healthcare spend in the US

The US department of Health has released interesting stats on the healthcare spend across various dimensions. The report is a bit old and available at http://archive.ahrq.gov/research/findings/factsheets/costs/expriach/

Some eye-opening snippets from the report are copied here -

1. Five percent of the population accounts for almost half (49 percent) of total health care expenses.
2. The top 5 chronic diseases are - Diabetes, Hypertension, Heart Disease, Asthma and Mood Disorders. Treatment for these diseases account for almost 50% of the total healthcare spend.
3. 5% of Medicare fee-for-service beneficiaries accounted for 43 percent of total spending, with 25 percent accounting for 85 percent of all spending.
4. The elderly and disabled, who constituted around 25 percent of the Medicaid population, accounted for about 70 percent of Medicaid spending.
5. The five most expensive health conditions were heart disease, cancer, trauma, mental disorders, and pulmonary conditions

Ruminating on Usage based Insurance

Many Auto Insurance firms have started launching usage-based insurance (UBI) products - i.e. based on how much you drive (miles) and how you drive. These are called as PAYD (Pay as you drive) and PHYD (Pay how you drive) respectively.

Insurance firms typically ask their members to plug-in an OBD device onto their vehicles. The OBD device then syncs the data wirelessly to the backend platforms of the insurance firm.

Allstate's Drivewise program is an example of this. It was enlightening to know the various parameters that are captured by the device and transmitted back to the servers. The full list of parameters is available here - https://www.allstate.com/landingpages/drivewisedevice.aspx

Some of the parameters are:

• GPS trail
• VIN and Odometer readings
• Hard Braking Events
• High Speed Events
• Acceleration Events
• Vehicle Error Codes
• A comprehensive trip report - seconds in acceleration, seconds in deceleration, miles driven in each speed band, constant speed miles, varying speed miles, etc. 

With the help of these parameters, an insurance firm can assign a 'Safe Driver' score for all their members and reward members for safe driving. There was another interesting parameter that could indicate if hypermiling took place :) 

Besides the OBD device, auto insurance firms need to invest in creating a scalable cloud platform to process this vast amount of OBD data. Technologies such as Big Data Analytics, CEP, scalable messaging and event propagation engines, next best action modules are integrated together to build such a scalable and modular UBI platform.