For many industries such as Medical devices, Industrial equipment, Manufacturing, Transportation, Utilities and many more, field service organization is key to the success of the company. The effect of technology on field service and manufacturing cannot be underestimated, especially as new technologies like IoT, Artificial intelligence (AI) and Machine learning (ML) have proven capabilities to streamline and transform traditional workflows & service delivery models. These technologies can also enable digital transformation initiatives in field service organizations. However, companies are facing technical challenges in developing & implementing an end to end solution and understanding how they can benefit from these technologies. In this webinar we covered: 1) Field Service Challenges 2) Key challenges in building IoT/ML/AI enabled Field service operations 3) Customer Case studies 4) Our Solution Overview 5) How you can grow revenue and optimize operations Derek Binkley, Director of Technical Services at ARCA talked about how they leveraged the IOT/ML/AI solution to solve their service challenges. ARCA is a provider of largest selection of cash handling devices of any manufacturer in the world. They provide cash automation technologies to customers in over 50 countries and support these devices from their headquarters in North Carolina.

1. Accrete Solutions Confidential
Reimagine Service Delivery using
IOT and Artificial Intelligence
November 15th 2018

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Agenda
➢ Service Challenges & Complexities
➢ AI/IOT/ML Solutions
➢ Spare Parts Planning
➢ Problem Identification
➢ Predictive Maintenance
➢ AI/ML/IOT integration with ERP and CRM
➢ Customer Case Studies
✓ ARCA’s experience with IOT/AI Service
✓ Case Study – Industrial Equipment
✓ Case Study - Medical Devices
➢ Questions
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Services = Recurring Customer
Relationships
$1 Trillion is spent in United States alone every
year on Devices already owned!
Service - Customer Relationship
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Service Challenges
Reactive operations
and wastage of
resources
Service Leaders need Intelligence driven technology
Increasing and
Changing demand by
Customers
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Service Complexities
People
Root Cause
Safety
Service Area Plan
Dispatch Plan
Tools Plan
Parts
Forecast
Parts plan
Dispatch Schedule
Shipment
Warranty Cost
Equipment
Repeat visits
Downtime
Fix time
Refresh vs. Repair
Maintenance
Schedule
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By using Artificial Intelligence
and Machine Learning to
understand the failure pattern of
the field assets, we can
proactively address problems in
the field, reduce part inventory
and improve technician/tools
resources plan?
AI/ML for Service
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Integrated Predictive Model
Use the field data and look for predictive signals to
understand the failure pattern and anomaly
Create forecast from these signals and by applying
learning method; proactively address problems;
Predict parts in time t is for the demand in time
t+L+R to account for Lead and Receive times
Account for variance by testing the data and adjust
for the install base changes
Integrate and provide visibility to see the impact at
P&L level to make proactive operational decisions
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Device
Ontology
Pattern &
Anomaly
Clustering
Transfer
Learning
Connect with workflowsIntegrate
Continuous learning
to improve within
and across
customers
Evolve
Transforms to
provide continuous
“Supply and Service
chain” predictions
Predict
Multi-dimensional
adaptive predictive
model that is MWBF-
based
Sense
Problem Identification Predictive Maintenance Technician plan Inventory management
AI/ML/IOT Technology
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Ready to be deployed and works through the emerging
IoT trend
Time →
Connecteddevices&IoT
Trend
Future
More and more IoT
integrated devices
with real time
streams
Today
More connected
Devices and IoT
has started to take
off
works through the
Enterprise’s
digitization Journey
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Service Planning Solution: Differentiators
Traditional Prescriptive
Optimize Services operations based on device
usage patterns and machine learning
Provide early warning of an impending parts, assets,
tools inventory problem
Gain more accurate P&L impact based on future part
demand prediction
Gain comprehensive understanding on technician
and tools projections based on predictive models
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Prescriptive Service Modules
Prescriptive
Intelligence
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IOT/ML/AI integration with ERP & CRM
Device Data
AI/ML/IOT Engine
CRM
Service
Ticket
Service
Order
Offers
Installed
Base
SCM
Inventory
Parts
Transactions
Forecasting
Bill of
Distribution
ERP
Material
Inventory Quality
Parts
Transactions
Usage
Data
Invoicing
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• Global cash automation company headquartered in North Carolina
• Celebrating 20 years in 2018
• Most widely deployed cash recycler in the world (CM18)
• Operating in Financial, OEM, and Retail channels
• US Service footprint covering all 50 states with a network of 600+
certified field technicians
• Full service installations and technical support maintaining
approximately 6,000 contracted units
arca.com
ARCA – Company Overview
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Arca’s advanced model is multiple times better than industry
standard
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Intuitive
(most)
MTBF
(Best Practice today)
MWBF (Arca’s
use of eKryp)
Assets
Service Ops
Prediction
Accuracy
Improvement
over traditional
approach
Today’s best practice is based on
time and use(notes) whereas
Arca is using multi dimensional
model for much better
predictability
2-3x over
best
practice
$6.3M
increased value
by Arca’s
customers

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Potential value
6-8 HOURS
Unscheduled
Downtime
Avoided
$ MAR
Unscheduled
DOWNTIME
(plus Dispatch
hrs)
MarFeb
REPAIR OPPORTUNITY
Do PM with a Purpose
2 Hrs Scheduled Downtime
PREDICTION WITH DETAILS
04Feb - Device Failure
Prediction with Confidence
level and potential root
cause
REPAIR AT FAILURE
Dispatch 4 hrs plus
Potential Parts (additional 2 hrs)
Realized Downtime: Downtime +
Dispatch + Part replacement
Example Customer Downtime Analysis
Track Jam Example
EXPANDED
POTENTIAL VALUE
222 DEVICES
Based on Top 50 Prediction in
Feb
1,822
Hours
Unscheduled
Downtime Avoided
22,824 potential downtime hours
avoided in one year

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Large Contract Manufacturer involved in Flow
Manufacturing
Solution
eKryp Device prediction and Incident Insight
modules to enable the Contract Manufacturer
to increase Overall Equipment Effectiveness
(OEE
Modules Used
eKryp Device Service Intelligence
eKryp Real Time Streaming Connector
eKryp Real Time Alert and Notification
CaseStudy:Industrial
Analysis of
Sensor
data in real time
5%
increase in OEE
40%
increase in
operator
efficiency
2x
improvement in
PM schedule and
operations
Use machine learning to predict potential
anomalies from the sensor data and provide
alerts to machine operators and shift
manager to prevent downtime
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Medical Device maker of high end scanning systems
CaseStudy:MedicalEquipment
Solution
eKryp Device, Parts and Incident modules to
enable the large provider of high end
scanning machines to increase speed to
resolve complex field issues, predict
upcoming device downtime, and provide
advanced service parts plan
Modules Used
eKryp Incident Categorization
eKryp Device Service Intelligence
eKryp Service Parts Planning
eKryp Data Integration Connector
Analysis of
5+ years
of field data on
devices
3+ hours
reduction in issue
resolution time
97%
accuracy in
service part
demand
3x
faster warning
during warranty
period
Use machine learning to predict potential
device anomalies, automatically categorize
incoming issues to potential solution areas,
and create service parts demand
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Thank You
Ramki Pitchuiyer
CEO
eKryp
228 Hamilton Avenue, Floor 3,
Palo Alto, CA 94301
Email:ramki.pitchuiyer@ekryp.com
Phone: 650-440-7656
Ashish Saxena
Vice President
Accrete Consulting Solutions
3350 Scott Blvd, Bldg 34A
Santa Clara, CA 95054
Email: ashish@accretesol.com
Phone: 408-785-1052
Linkedin:
https://www.linkedin.com/in/ashishcrm
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