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Lean Kanban to Lean Business

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My keynote talk at Asia's First Lean Kanban Conference at Bangalore, Dec 11-12

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Lean Kanban to Lean Business

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  29. Our Product • Network Management domain • Windows-based specialized hardware (“Appliances”) • Installed in data centers for traffic monitoring, analysis and network troubleshooting • but not generally on production network • Typical users are technical folks – CIO, Network Manager, Network Engineers • Selling cycles typically align with quarterly or annual budget cycles • Many sales require implementing customer specials
  30. Old Process, circa 2003 • Customer Bugs prioritized based on multiple business parameters, including (partial list) - • Severity • Impact on Revenue, Volume, Competitive, etc. • Case age • PMO would prepare Maintenance Release Plan of Record (MR POR) and get buy-in for various types of MRs - • Service Packs – bunch up ~50-60 bugs typically every quarter • Hot Fixes – 1-2 high-urgency bugs that can’t wait until next SP • Patches – workaround for customer-specific issues • SPs would have • Above The Line (ATL) requirements – must fix • Below The Line (BTL) requirements – fix if time permits
  31. Problems with Old Process • Dev team had no bandwidth to take on maintenance releases • Huge pile of customer escalations without “home” • Compounded by high incoming field rate • Low closure rate (largely due to no dedicated resources) • Large wait for customers to get bug fixes • Tech Support often tasked team directly and broke the process • Hot fixes not always available to all customers • Sometimes, a new bug fix might break a hot fix • If a hot fix failed in the field, rollbacks would be very difficult • Difficult to estimate time to resolve a bug and give an ETA • High-priority bugs could arrive at any time • Customer specials could arrive anytime with top priority • High internal rejection rate of bug fixes by Tech Support
  32. New Process, 2004-05 Dedicated Customer Sustaining Team New “Cumulative Hot Fix” process Improved collaboration with all stakeholders
  33. Our Kanban Process in action… GUI Product A Backend Product B Product C Product D Protocols Device Drivers PMO CST Manager Tech Support QA Team Dev Team = 15 WIP = 15 WIP = 2 WIP = 1wk WIP = 1wk Queue = 0 Queue = Infinite
  34. Process improvement…the beginnings…
  35. Cumulative Hot Fix Process
  36. Weekly Build Process
  37. So, what is happening? • Though not an originally stated vision or goal, the “Work in Progress” (WIP) is being limited to # of team members • At any time, one developer is assigned only one piece of work, thereby achieving “One-Piece Flow” • New work is only assigned when current work is completed (or cancelled/stalled), and a team member is available • No wait state or switching costs at an individual level • Smaller lead time for bugs (in contract to lead time for SP) • The process is allowing ‘continuous deployment’ of each of the hot fixes – even though it is only being practiced in a limited manner • Finally, the flexibility gained is not a zero-sum game – there is no penalty on performance in rest of the process
  38. Did this move the needle? • Bugs addressed each quarter • Quality of bug fixes • “Homes” for bugs • Total bugs open • Open days open • People motivation
  39. From SPs to Cumulative Hot Fixes Maintenance Releases (Service Packs, Patches, Hot Fixes) Q3 2003 Through Q4 2006 (Fiscal Year) Hot Fixes Patches 5 2 1 5 3 1 0 0 1 0 0 0 1 0 2 3 4 4 4 4 2 2 2 1 2 3 1 0 2 8 11 16 18 20 62 28 27 26 26 24 28 21 100 92 87 96 88 92 85 80 97 96 93 100 94 91 7 12 15 25 25 25 30 30 28 27 26 32 22 66 70 60 50 40 30 20 10 0 Q3 03 Q4 03 Q1 04 Q2 04 Q3 04 Q4 04 Q1 05 Q2 05 Q3 05 Q4 05 Q1 05 Q2 06 Q3 06 Q4 06 100 90 80 70 60 50 40 30 20 10 0 Service Packs Patches Hot Fixes % Successful Percentage of released Maintenance Releases (Service Packs, Patches, Hots Fixes) that addressed customer reported Total Maintenance Releases (Service Packs, Patches, Hot Fixes) for this quarter. Service Packs
  40. Increase in bugs with “homes” Customer Direct and Indirect % w/Homes - Historical Week Ending February 03, 2006 55 Percent with Releases Identified: 56% (Driect), 80% Indirect, 68% (Combined) 46 45 47 44 45 45 44 55 52 54 56 35 45 39 38 41 51 46 31 40 40 59 63 60 61 61 68 69 74 72 69 69 55 73 80 68 61 5356 48 54 55 52 51 51 51 50 49 54 55 47 47 11 15 18 16 47 53 55 43 42 43 75 82 76 76 75 82 83 82 81 71 72 65 87 82 75 47 51 51 50 48 48 48 48 48 53 54 51 51 21 27 27 25 44 52 51 41 42 67 72 68 69 68 75 76 78 76 70 71 60 80 71 64 44 47 100 90 80 70 60 50 40 30 20 10 0 5/6/2005 5/13/2005 5/20/2005 5/27/2005 6/3/2005 6/10/2005 6/17/2005 6/24/2005 7/1/2005 7/8/2005 8/5/2005 8/12/2005 8/19/2005 8/26/2005 7/15/2005 7/22/2005 7/29/2005 9/2/2005 9/9/2005 10/14/2005 10/21/2005 10/28/2005 9/16/2005 9/23/2005 9/30/2005 10/7/2005 11/4/2005 11/11/2005 11/18/2005 11/25/2005 12/16/2005 12/23/2005 12/30/2005 12/2/2005 12/9/2005 1/6/2006 1/13/2006 1/20/2006 1/27/2006 2/3/2006 Direct Indirect Goal (80% w/Homes) Combined
  41. Total Bugs Open Severity Across Products (Historical) Week Ending March 31, 2006 222 243 191 224 213 236 230 210211 201199 196197195 161 147146146146 132 122 131 121 127 127125 111 148150 65 605857 57 53 626466676768 71747575 6866 5049495052 494746 69707069 6063 7473 68 6160 5555 4749 5149 44 3538 323332343536 130 120 110 100 90 80 70 60 50 40 30 20 10 0 01-Oct-04 31-Dec-03 29-Oct-04 19-Nov-04 10-Dec-04 21-Jan-05 31-Dec-04 11-Feb-05 04-Mar-05 25-Mar-05 06-May-05 15-Apr-05 27-May-05 17-Jun-05 08-Jul-05 29-Jul-05 19-Aug-05 09-Sep-05 21-Oct-05 30-Sep-05 11-Nov-05 02-Dec-05 13-Jan-06 23-Dec-05 03-Feb-06 24-Feb-06 260 240 220 200 180 160 140 120 100 80 60 40 20 0 17-Mar-06 Total Severity 1 Severity 2 Severity 3 Severity 4 Severity 5
  42. Average Days Open Severity: Average Days By Severity (Historical) Week Ending March 31, 2006 203 155 244242246246244248 136 234235 232 226223 260 244 275275 268 299 232 210207 201 179 165 172 162 156 180 168 163 156152 147 142 139143 132135 148 140 155 144147146144 145 135 157156156 152 148 149 144 140 133 154155157 144147 177179183182 163166 164162165 154 146 149 141 134134 123 117 123 500 475 450 425 400 375 350 325 300 275 250 225 200 175 150 125 100 75 50 25 0 01-Oct-04 31-Dec-03 29-Oct-04 19-Nov-04 10-Dec-04 21-Jan-05 31-Dec-04 11-Feb-05 04-Mar-05 25-Mar-05 06-May-05 15-Apr-05 27-May-05 17-Jun-05 08-Jul-05 29-Jul-05 19-Aug-05 09-Sep-05 21-Oct-05 30-Sep-05 11-Nov-05 02-Dec-05 13-Jan-06 23-Dec-05 03-Feb-06 24-Feb-06 300 275 250 225 200 175 150 125 100 75 50 25 0 17-Mar-06 Avg Days Open Severity 1 Severity 2 Severity 3 Severity 4 Severity 5
  43. People motivation • Started with 16 people dev team • We had zero attrition in the team • Once the backlog started coming down, engineers were ramped off the team to do new features • Eventually dismantled the team and rolled-up engineers into dev teams when backlog came down to single digits 16 16 16 16 16 16 16 16 16 16 16 16 12 12 12 12 12 10 10 10 4 4 4 4 4 6 6 6 18 16 14 12 10 8 6 4 2 0 Resource Staffing Sustaining Mainline Approved Headcount
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