SlideShare a Scribd company logo
Achieving Customer
Delight Using Kanban
Radhika Halappa
Agile Consultant
2
❑ Leveraging Lean and Kanban
methodology to transform operations
❑ Addressing Context Switching
❑ Being Innovative - Use of Cognitive
tools
❑ Predictive methods for improvement
Takeaway
Image credits – Photo by Clem Onojeghuo on Unsplash
3
Agenda
• Setting The Context
• Go To The Gemba
• Recommendations For Improvement
• Kanban Implementation
• Benefits Realized
4
Context Setting
Image credits – https://pixabay.com/photos/puzzle-pieces-of-the-puzzle-3476931/
Problem Statement
About 60% of the “Same Day
Resolve” tickets not closed within
the requested date
Team Experiencing Low Customer
Satisfaction Score
Image credits – https://www.pinterest.com/pin/692921092646833370/5
6
Roadmap For Improvement
7
Go To The Gemba
Image credits – Photo by Alex Gorbi on Unsplash
❑ 35% of total tickets received are “Same
Day Resolve” tickets
❑ On average across all applications, only
40% of “Same Day Resolve” tickets closed
within Resolve date
8
“AS IS” Value Stream Mapping & Analysis
Touch Time (TT) | Lead Time (LT)
Process Cycle Efficiency = TT/LT *100=17%
9
Root Cause Analysis : Key Root Causes For Delays
❑ Frequent customer clarifications
❑ High dependency on other vendors
❑ Less Awareness on impact of “Same
Day Resolve” tickets
❑ Very frequent Context Switching
Image credits – Photo by Daria Nepriakhina on Unsplash
10
Recommendations Proposed For Improvement
❑ Adopt Kanban methodology
❑ Use Predictive Models to improve
predictability
❑ Use Cognitive tools such as Watson, Slack
& Chatbots
❑ Alerts to SDM* ahead of Requested date
elapse time
❑ OLA# between with vendor teams
Image credits – https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTu-
O5YR8AvijzAt3oH_C2Rph9jR1H-kG-Oe0bls4SVP-g9IgfL
*SDM – Service Delivery Manager
#OLA – Operational Level Agreements
Recommendations listed above is not a comprehensive list
11
Adopted “To Be” Value Stream Map
Touch Time (TT) | Lead Time (LT)
Process Cycle Efficiency = TT/LT *100 = 22%
❑ Eliminated Non Value Added
Activities
❑ Enforced 1 operator to complete
all activities of a ticket
❑ Automated creation of Kanban
card
12
Kanban Board Implemented
Defining WIP Limits
❑ Single Piece Flow System - 1 FTE does all
activities and takes tickets to closure
before pulling next ticket
❑ Cumulative Flow Diagram indicates
system is stable with 2 WIP limits
❑ Capacity for ‘Same Day Request’ is
decided during Daily Stand up.
13
AAR – Average Arrival Rate
ACR – Average Closure Rate
Context Switching : Eliminate Multitasking
❑ Dedicated FTE’s to support tickets
❑ Enforced clear policies and processes
❑ Captured detailed inputs through
templates and cognitive tools (chatbots)
❑ Set WIP limits to prevent context
switching
Image credits – https://www.dreamstime.com/vector-cartoon-man-multitasking-sticky-notes-cartoon-man-multitasking-sticky-notes-image11211355814
15
Predictive Analytics : Leveraging Predictive Analytics Tool
❑ Predictive Analytical model are developed on the historical measurement data
❑ Tool Predicts – Future Ticket Volume, Takt Time, & Team Utilization
16
“To Be” State Value Stream Data Analysis
❑ On average across all applications,
57% of “Same Day Resolve” tickets
closed within the closure requested
date
❑ Preventive strategy to reduce “Same
Day Resolve” tickets
Immediate Improvement of 17% in closure of Same Day Resolve Tickets
17
Challenges Along The Improvement Journey
❑ Customer was not keen to change to new way of working
❑ Diverse Consumer ecosystem - no opportunity to talk to Consumers
❑ Lack of team’s awareness to Agile Ways of Working
❑ Multi vendor scenario impeded the ticket closure velocity & throughput
❑ Developing WIP limits to smoothen flow
18
Benefits Realized. Improvements on going….
1. 17% Increase in closure of “Same Day
Resolve” tickets in Q1 2019
2. Improved transparency and
governance across the ticket lifecycle
3. Improved “Turn around Time” of all
tickets
Delighted Customer
Image credits – https://www.kochiesbusinessbuilders.com.au/
Thank You
19
Mani V | IBM Global Business Services
Lean Kanban India Team
Acknowledgements
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Lean Kanban India 2019 Conference | Achieving Customer Delight Using Kanban | Radhika Halappa

  • 1. Achieving Customer Delight Using Kanban Radhika Halappa Agile Consultant
  • 2. 2 ❑ Leveraging Lean and Kanban methodology to transform operations ❑ Addressing Context Switching ❑ Being Innovative - Use of Cognitive tools ❑ Predictive methods for improvement Takeaway Image credits – Photo by Clem Onojeghuo on Unsplash
  • 3. 3 Agenda • Setting The Context • Go To The Gemba • Recommendations For Improvement • Kanban Implementation • Benefits Realized
  • 4. 4 Context Setting Image credits – https://pixabay.com/photos/puzzle-pieces-of-the-puzzle-3476931/
  • 5. Problem Statement About 60% of the “Same Day Resolve” tickets not closed within the requested date Team Experiencing Low Customer Satisfaction Score Image credits – https://www.pinterest.com/pin/692921092646833370/5
  • 7. 7 Go To The Gemba Image credits – Photo by Alex Gorbi on Unsplash
  • 8. ❑ 35% of total tickets received are “Same Day Resolve” tickets ❑ On average across all applications, only 40% of “Same Day Resolve” tickets closed within Resolve date 8 “AS IS” Value Stream Mapping & Analysis Touch Time (TT) | Lead Time (LT) Process Cycle Efficiency = TT/LT *100=17%
  • 9. 9 Root Cause Analysis : Key Root Causes For Delays ❑ Frequent customer clarifications ❑ High dependency on other vendors ❑ Less Awareness on impact of “Same Day Resolve” tickets ❑ Very frequent Context Switching Image credits – Photo by Daria Nepriakhina on Unsplash
  • 10. 10 Recommendations Proposed For Improvement ❑ Adopt Kanban methodology ❑ Use Predictive Models to improve predictability ❑ Use Cognitive tools such as Watson, Slack & Chatbots ❑ Alerts to SDM* ahead of Requested date elapse time ❑ OLA# between with vendor teams Image credits – https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTu- O5YR8AvijzAt3oH_C2Rph9jR1H-kG-Oe0bls4SVP-g9IgfL *SDM – Service Delivery Manager #OLA – Operational Level Agreements Recommendations listed above is not a comprehensive list
  • 11. 11 Adopted “To Be” Value Stream Map Touch Time (TT) | Lead Time (LT) Process Cycle Efficiency = TT/LT *100 = 22% ❑ Eliminated Non Value Added Activities ❑ Enforced 1 operator to complete all activities of a ticket ❑ Automated creation of Kanban card
  • 13. Defining WIP Limits ❑ Single Piece Flow System - 1 FTE does all activities and takes tickets to closure before pulling next ticket ❑ Cumulative Flow Diagram indicates system is stable with 2 WIP limits ❑ Capacity for ‘Same Day Request’ is decided during Daily Stand up. 13 AAR – Average Arrival Rate ACR – Average Closure Rate
  • 14. Context Switching : Eliminate Multitasking ❑ Dedicated FTE’s to support tickets ❑ Enforced clear policies and processes ❑ Captured detailed inputs through templates and cognitive tools (chatbots) ❑ Set WIP limits to prevent context switching Image credits – https://www.dreamstime.com/vector-cartoon-man-multitasking-sticky-notes-cartoon-man-multitasking-sticky-notes-image11211355814
  • 15. 15 Predictive Analytics : Leveraging Predictive Analytics Tool ❑ Predictive Analytical model are developed on the historical measurement data ❑ Tool Predicts – Future Ticket Volume, Takt Time, & Team Utilization
  • 16. 16 “To Be” State Value Stream Data Analysis ❑ On average across all applications, 57% of “Same Day Resolve” tickets closed within the closure requested date ❑ Preventive strategy to reduce “Same Day Resolve” tickets Immediate Improvement of 17% in closure of Same Day Resolve Tickets
  • 17. 17 Challenges Along The Improvement Journey ❑ Customer was not keen to change to new way of working ❑ Diverse Consumer ecosystem - no opportunity to talk to Consumers ❑ Lack of team’s awareness to Agile Ways of Working ❑ Multi vendor scenario impeded the ticket closure velocity & throughput ❑ Developing WIP limits to smoothen flow
  • 18. 18 Benefits Realized. Improvements on going…. 1. 17% Increase in closure of “Same Day Resolve” tickets in Q1 2019 2. Improved transparency and governance across the ticket lifecycle 3. Improved “Turn around Time” of all tickets Delighted Customer Image credits – https://www.kochiesbusinessbuilders.com.au/
  • 19. Thank You 19 Mani V | IBM Global Business Services Lean Kanban India Team Acknowledgements and