You think the shopper is smart?

With the rise in consumer preferences towards natural, organic and non-GMO food, retailers are faced with the challenge of supplying fruits, vegetables, and protein with a shorter shelf life, and adjusting to these trends of a dynamic marketplace. 86% of shoppers are confident the food they buy is safe from germs and toxins, down from 91% in 2014. Retailers must become more operationally efficient or increase their stock to overcompensate for higher rates of spoilage in order to counteract shorter shelf life challenges. Planning for fresh produce is more complicated than for non-perishable goods. According to a BlueYonder study, 68% of shoppers feel disappointed with the freshness of their purchases, and 14% of shoppers seek organic certification.

By using machine learning solutions, retailers will be able to optimize the environmental conditions affecting spoilage. In addition, there are risks of being out of compliance on food, health and environmental safety regulations with very high penalty, like Walmart paid $81M in environmental compliance.

How can you keep up?

Grocery retailers generally have low profit margins, so slight improvements to efficiency are important. Our machine learning solution is aimed at helping retailers improve their management of shorter shelf life products, and ultimately their profitability through optimization of their energy cost and prediction of temperature control equipment failure.

• Energy Savings: In some cases, utilities can amount to up to 50% of profit margin for a store, and energy savings driven by machine learning translate immediately to profit margins. For example, within the perishable seafood or meat sections, overcooling is a significant cost that can automatically be optimized by sensors that measure temperature in a cooler or refrigerator.
• Effectivity and Efficiency: Better allocation of resources like people and machines is very useful for top and bottom line. E.g. out of stock inventory can lead to $24M lost sales per $1B retail sales. Automatic tracking of inventory levels can help increase productivity and also revenues.
• Predictive Maintenance: Because refrigeration equipment has to run 24 / 7, there are high breakdown rates of equipment. Sensing equipment can be applied to HVAC and Nitrogen equipment to predict failure ahead of time. Even just small freeze / thaw cycles can quickly damage product and lead to waste for retailers.
• Compliance: FSMA and EPA includes multiple guidelines for retailers and grocery stores to follow, with high penalties for out of compliance.
• Consumer behavior: Consumer preferences and potential trends can be identified and acted upon if predicted. The Amazon store could even track which products you are interested in, but had not purchased.
• Risk mitigation: We could observe financial transactions, customer behavior etc. to predict risks, fraud, shoplifting etc. automatically and proactively.

Organizations are already moving to smarter technology for help.

What if the store was also smart?

Grocery retailers could use advanced analytics through IOT and other technology to revamp the way they monitor their stores.

1. Video feeds
2. Point Of Sale sensors
3. Mobile phones / equipment of Associates in store
4. IR Motion Sensors
5. HVAC and Energy monitoring using sensing of temperature, pressure, humidity, Carbon Monoxide
6. Weight Mats
7. Parking Space sensor
8. Digital Signage
9. Gesture Recognition/ accelerometers
10. Door Hinge Sensor motion/ pressure
11. Wifi Router and connections
12. Shelf Weight
13. Air Filter/humidity
14. Lighting
15. Electricity, Water, gas meters
16. Spark (Temperature) for places this device is taken to

Pilot

We would perform A/B testing to measure sensor performance and outcome in the controlled environment. The specific experiment is for milk preservation and storage. We would like to measure energy saving, ensure compliance to FSMA and EPA and predict refrigerator breakdown and maintenance.

• • Refrigeration is the single most important factor in maintaining the safety of milk. By law, Grade A milk must be maintained at a temperature of 45 °F or below. Bacteria in milk will grow minimally below 45 °F. However, temperatures well below 40 °F are necessary to protect the milk’s quality. It is critical that these temperatures be maintained through warehousing, distribution, delivery and storage.
  • The cooler refrigerated milk is kept, the longer it lasts and the safer it is. As the product is allowed to warm, the bacteria grow more rapidly. Properly refrigerated, milk can withstand about two weeks’ storage.
  • Pilot program: For the cost of $15 per fridge/freezer we can monitor food’s temperature and receive audible and visual alarm when temperatures exceed its minimum or maximum temperature range. For $39 we could measure temperature, humidity as well as when the door is open and closed.

We would partner with two to three smaller to medium size organic grocery stores (Trader Joes) with the high traffic, ex: city to test the impact over two to three weeks period.

Compliance info for meat preservation and storage is provided below:

• https://www.fda.gov/downloads/Food/ResourcesForYou/HealthEducators/UCM109315.pdf
• https://www.fda.gov/aboutfda/transparency/basics/ucm194273.htm

Example use cases:

1. Predictive Device Maintenance to avoid compliance lapse (e.g. Fridge for Food Safety, Fire Safety equipment, lighting, etc.)
2. Hazard detection and prevention through monitoring of toxic substance spill and disposal (air filter, shelf weight and video sensor)
3. FSMA compliance across labels, food expiry, storage conditions, etc.
4. Health safety with store conditions like canopy use, weather, leaks etc.
5. Temperature, defrost and humidity monitoring for Ice-cream, meat, dairy, and pharmaceuticals
6. Video analysis to predict long lines and avoid bad customer experience or lack of lost customers increased productivity etc. by alerting and optimizing resource allocation
7. Video + Point Of Sale analysis for fraudulent transactions avoidance

A central monitoring within stores, and centrally can be created, to mimic the Nasa base in Houston, is always able to support all adventurers within the store. Roger that?

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Sources

FMI U.S. Shopper Trends, 2016. Safe: A32. Fit health: A12. Sustain health: A9, A12. Community: A12, * The Hartman Group. Transparency, 2015.

http://www.cnsnews.com/news/article/wal-mart-pay-81-million-settlement-what-epa-calls-environmental-crimes

https://www.fda.gov/food/guidanceregulation/fsma/

https://www.epa.gov/hwgenerators/hazardous-waste-management-and-retail-sector

Amazon store  https://www.youtube.com/watch?v=NrmMk1Myrxc

https://foodsafetytech.com/tag/documentation/

http://www.securitygem.com/cao-gadgets-inexpensive-and-tiny-sensors-for-your-smart-home/

http://www.clemson.edu/extension/hgic/food/food_safety/handling/hgic3510.html

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Team – March & the Machines

Ewelina Thompson, Akkaravuth Kopsombut, Andrew Kerosky, Ashwin Avasarala, Dhruv Chadha, Keenan Johnston

Opportunity

Until recently, the automotive insurance industry used archaic methods to assess driver risk. Driver premiums were based on factors such as geography, age of the driver, whether or not the driver had been in an accident before and the type of car they drive. However, these factors are not good indicators of risk and heavily depend on an event taking place such as an accident. Any accident, leads to a huge cost and payout for an insurance company. The question the industry started to ask was, is there a way to predict risk before the fact and prevent accidents from happening at all?

This led to the advent of Usage Based Insurance. The premise is simple, using sensors to detect driving patterns which indicate risky behavior before a costly event takes place. This helped insurance providers identify risky drivers and charge more accurately based on driver’s risk profiles. The same technology can be used to provide feedback to driver, helping them improve their driving habits, in effect reducing the risk of an accident.

Solution

 

Arity recently spun out of Allstate, bringing machine learning and predictive analytics to better predict risk and help drivers understand their driving behavior. The solution initially started with the use of a OBDII based dongle, which once inserted into a car’s diagnostic port, would capture driving information from the vehicle (speed, diagnostics, accelerometer, GPS). Using proprietary models and machine learning, the data from the OBDII device is used to give each driver a risk score. This score determines the drivers likelihood to get into an accident and finally their viability as a customer. In most cases, the insurance company elect to not insure a high risk driver and most good drivers would actually see their premiums decrease.

While the solution using a dongle worked well, it was not cost effective and neither could it be used to gather information about the driver’s behavior. The next stage of this technology has moved to using the driver’s mobile device as a sensor. A driver’s mobile device being used to collect information such as

• travel speed,
• Acceleration,
• Deceleration,
• cornering speeds,
• time of day,
• Usage of the phone (connected over a Bluetooth device, or was the phone physically in the drivers hand)

All of this information is processed through proprietary models and compared to risk data. Arity has access to 21 billion miles and over 85 years of Allstate’s insurance underwriting data which is the baseline to creating accurate risk models. This allows Arity to create a driver risk profile and also provide feedback to the driver directly through the app on their mobile device. 

Effectiveness and Commercial Promise

According to the National Safety Council, cell phone use while driving leads to 1.6 million crashes each year. 1 out of every 4 car accidents in the United States is caused by texting and driving. The estimated economic cost and comprehensive cost caused by phone usage while driving are $61.5 and $209 billion. Arity’s solution monitors a driver’s interaction with a mobile device along with driving behavior, which would deter or notify the consumer when they are distracted.

Insurance companies can leverage this technology to identify risky drivers and help them improve their driving habits. For example, Allstate’s Drivewise application allows policyholders to save up to 3% of their insurance cost when using the app to manage their insurance. After the first 50 trips, the auto insurance holders may earn up to 15% cash back based on their driving behaviors and risk profile. Arity’s solution is particularly important to the highest risk group: teenage and student drivers and can help them be safer on the road.

Ride sharing and commercial fleet customers –Taxi, Bus, Uber and Lyft are also looking for was to better track the performance of their employees and drivers. These companies would be able to manage risk better through the platform. They can qualify drivers based on driving behaviors and retain the safe drivers. High risk drivers also negatively hurt the riding experience of customers and ultimately hurt their brands. By using Arity’s platform, the companies can weed out high risk drivers and thereby lowering their auto insurance cost.

Alterations

Today the algorithms do not take into account external factors. Our recommendation is to include:

• weather information,
• Location – proximity of known bars or high risk areas
• Improve data from mobile phones as the data is not very clean
• Make use of wearables as additional sensors which can provide insights into driver health information
• Lastly, partner with OEMs to get access to connected car data which is much more reliable than the data from mobile devices for tracking vehicle movement

Competitors

Given multiple potential applications there is a space in the industry for many companies to operate successfully. Arity is positioned to outperform its competitors today due to the amount of data they can collect through Allstate’s large and diverse customer base. Arity is also making its platform available to other smaller insurance companies and fleet operators which will enable Arity to collect larger amounts of data which in turn will help improve the risk models.

Octo America

• Primarily partners with government
• A global firm without strong data and market penetration in US

Cambridge Telematics

• Primary works with StateFarm insurance
• The data used for modelling is not as diverse as Arity

Zendrive

• Primarily targets the B2C segment but monetizes through B2B
• No current partnership with insurance company

 

Research Links

https://developer.arity.com/driving-engine-sdk

https://www.zendrive.com/how-it-works/

http://www.naic.org/cipr_topics/topic_usage_based_insurance.htm

https://www.arity.com/index.html

https://www.octousa.com

https://www.cmtelematics.com/

https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812348

https://www.edgarsnyder.com/car-accident/cause-of-accident/cell-phone/cell-phone-statistics.html

https://www.edgarsnyder.com/car-accident/cause-of-accident/cell-phone/cell-phone-statistics.html

https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812348

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Team – March and the Machines

Opportunity

To make data driven decisions, public and private investigative agencies, especially in legal, fraud detection investigations, compliance and governance issues, are overburdened by record amounts of investigative requests. Data is growing faster than ever and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. The document reviewing process is both time consuming and expensive. These organizations seek more efficient methods to analyze data given the cost associated with people sifting through documents. For example, the most efficient lawyers can sift through 80 documents in an hour, but this process is prone to mistakes due to fatigue, etc.  Digitization has given rise to opportunities for e-discovery solutions. Companies like Brainspace are developing software and machine learning tools that augment intelligence of public and private investing agencies to reduce the amount of time of reviewing documents to make data driven decisions.

Solution

Brainspace analyzes structured or unstructured data to derive concepts and context. Using visual data analytics, it then engages human interaction to refine a search for maximum relevancy.  Brainspace software is able to learn at a massive scale (1 million documents in 30 mins) and the process is entirely transparent in which a user can see and interact with the machine learning.

By using Brainspace, productivity is enhanced due to interaction between the machine and human. Brainspace is better at ingesting, connecting and recalling information than humans, while humans are better at using information to reason, judge and strategize than machines. For example, after the platform organizes the unstructured texts into concepts, humans could filter based on a concept and weight “suggested contexts” to curate relevant search results.

Brainspace is differentiated versus other text searching algorithms in that it searches to recognize concepts not just text.  The software “reads between the lines” as humans do, except that it can handle thousands of pages instantly.  The software is dynamic and unsupervised utilizing no lexicons, synonym lists or ontologies.

Effectiveness and Commercial Promise

The market opportunity and applications of Brainspace’s leading machine learning software are enormous, though the first application has been in the investigative industry.  One can imagine the immense savings associated with document reviewing costs.  Other research-heavy applications include legal e-discovery, fraud detection investigations within financial-services organizations and compliance or governance issues.

For example, remember Enron?  As a demo, Brainspace imported 450 million Enron documents, and traced the emails about an offshore account.  It took 5 clicks and less than a minute to nail down the executives involved.  By hand, it would have taken lawyer’s 6 months!

Brainspace software has many applications, but they have needed to partner with other companies to deliver a usable product to a customer.  For example, Brainspace partnered with Guidance to deliver a product called EnCase.  The Guidance software augments the Brainspace deciphering algorithm by providing protection against hacking and external threats.  The product is used in the investigative industry to provide auditing capabilities critical to large-scale investigations.

A potential competitor to Brainspace that we identified is Graphistry, which similarly takes data, quickly understands the results, and automatically visualizes the data to the user via graphs and cloud technology.  Graphistry may have better visualization software for analysis by a human user, but we believe that the patented deciphering technology of Brainspace is more powerful.  Building upon the software to improve the user friendliness of Brainspace is a potential opportunity.

Alterations

Brainspace could apply its context deciphering technology to online news agencies or social media sites.  For instance, it could analyze twitter feeds and be a more reliable protection against “fake news” generated by underperforming algorithms.  Perhaps CFO’s at companies could implement the software to automate financial packets.  The software may be able to derive trends in financial statements, and derive context for those trends from access to the company email database.

Brainspace’s technology can also be very useful for the sensors revolution that is taking place, creating enormous amounts of information. Brainspace could use its expertise in analyzing structured and unstructured data to create insights from sensors data which can provide immense value for firms that use this data to monitor real-time systems.

Sources:

1. https://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/#6055410d17b1
2. http://www.businessinsider.com/temp-attorney-told-to-review-80-documents-per-hour-2009-10
3. https://www.brainspace.com/

Posted by:  Dhruv Chadha, Keenan Johnston, Ashwin Avasaral, Andrew Kerosky, Akkaravuth Kopsombut, Ewelina Thompson