POSITIVE EFFECTS OF QUANTITATIVELY MANAGING YOUR SERVICE DESK

Transcription

1 POSITIVE EFFECTS OF QUANTITATIVELY MANAGING YOUR SERVICE DESK NAME: Andreas R. Felschow TITLE: PRESIDENT/CTO ORGANIZATION: TPC

2 INTRODUCTION/BACKGROUND Establishing and improving your journey to improved capability Background Company A Struggled meeting commitments and specifically could not meet the required Service Level Agreement (SLA) consistently Service Level Agreement (SLA) was to meet 90% of the defined Performance Based Measurements (PBM) Results were well below <90% Issues with not meeting the SLAs for the majority of the PBMs Company did not utilize the available measurements related to the ticket to fully understand performance No real analysis of failed PBMs Became Complacent LOE and customer was happy in years past

3 THE JOURNEY Understand your current state and develop a detailed process flow Identify Business Objectives based on improvement or capability needs Identify quantitative management techniques to support the measures Examine existing historical data for Central Tendency and Distribution of Variation Establish Process Performance Baseline(s) Use Process Performance Baseline data to establish predictive process performance models Compose project process using the predictive performance model(s) Monitor project progress towards meeting objectives Calibration of models, if necessary

4 STEPPING INTO A NEW ROLE ValidaTek to the rescue Had the resources and an excellent resume to step in and provide Service Desk Support First step was to put a solid process in place Trained specialists in Service Desk Activities Trained personnel how to use the ticket system software Used the existing Standard Operating Procedures (SOP) Kicked-off Service Desk Support with a quick timeframe

5 UNDERSTANDING MEASURES FROM TICKET SYSTEM Immediately realized the ticket reporting and analysis required significant manual interpretation Some tickets were not resolved or escalated to the proper groups Measurements were limited Help Desk Number of tickets/week Simple analysis resulted in Yes versus No SLA satisfaction was based on % of Y/N by tickets/month Second Tier Third Tier Requests Start Requests Open Ticket Product No Technical Resolves No Software Corrects No Data Correction Yes Yes Yes Ticket system was developed and maintained by another contractor so making improvements was a challenge Help Desk New Hire Documented Ticket New Hire Training Type of Ticket Service Daily Meeting Updated Ticket PSS Resolves Yes Close Ticket Communicate to Stakeholders End

6 A NEW START 1 st Month 82% compliance, which was a significant improvement from the previous contractor 2 nd Month >91% Some SLAs were being achieved, some were not, which was a cause for concern Management posed a challenge Not just satisfied on meeting 90% How well is the service desk process working

7 FURTHER PROCESS DEFINITION AND ANALYSIS WAS REQUIRED [PBM Process Flow] Alerts Goes To Level 1 Wait Test Issue Identified Create Outage Escalate L2 Acknowledge/ Accept Notification Complete Trouble Shooting Holiday/ Closures Complete Trouble Shooting PBM 9 60 Minutes To Complete Requests ISRs Minutes Waiting PBM 6 10 Minutes Test Time in Minutes Close Alert PBM 7 15 Minutes to Escalate PBM 8 7 Minutes To Acknowledge / Accept Complete Trouble Shooting PBM Minutes To Complete Start Data Center O&M Services Phone Call Resolve Goes To Level 1 Resolve PBM 1 10 Minutes Remedy Ticket Goes To Level 1 Resolve/ Re-assign No Assign ESOC Assign To Proper Group New Service Request Resolve Ticket PBM 21 2 Business Hours Yes Assign ITSC PBM 5 15 Minutes Other Groups PMO/CCM Facilities, etc. Resolve Ticket Close Ticket PBM 1 10 Minutes

8 MEASUREMENT INVENTORY

9 THE APPROACH Data Collection and use of Historical Data Establish Process Performance Baseline(s) Establish Process Performance Model(s) Using the Model to Predict Performance Estimate resources to support service desk Meet all PBMs and SLA

10 DATA COLLECTION/HISTORICAL DATA Results were initially based on SOP, which stated to report based on Yes or No, with no other insight expected SLA was based purely on % met Original contractor - 61% 82% - 1 st Month Service Requests Total 245 Found 251 Service Requests Total Met 202 Due Completed Met Note 2/28/ :00 2/28/ :12 y 2/28/ :00 2/28/ :09 y 2/28/ :00 2/28/ :12 y 2/28/ :00 2/28/ :10 y 2/28/ :00 2/28/ :35 y 2/28/ :00 2/28/ :29 y 2/28/ :00 2/28/ :14 y 2/28/ :00 2/28/ :49 n Late complete. 2/28/ :00 2/28/ :19 n Late complete. 2/28/ :00 2/28/ :44 n Late complete. 2/28/ :00 2/28/ :44 n Late complete. 2/28/ :00 2/28/ :12 n Late complete. 2/28/ :00 2/28/ :57 y 2/28/ :00 2/28/ :41 y 2/28/ :00 2/28/ :37 y 2/28/ :00 2/28/ :12 n Late complete. 2/28/ :00 2/28/ :16 n Late complete. 2/28/ :00 2/28/ :44 n Late complete. 2/28/ :00 2/28/ :04 n Late complete. 2/28/ :00 2/28/ :33 y 2/28/ :00 2/28/ :37 y 2/28/ :00 2/28/ :41 y 2/28/ :00 2/28/ :12 y

11 CONTINUED DATA COLLECTION AND ANALYSIS Previous year s data was available for service tickets recorded, which were analyzed PBM 006 Analysis SLA is 10 minutes. Mean is 1.37 minutes, UCL is 4.57, both of which are within the SLA There are many data points above 3σ Total number of 2635 data points/week. SLA met - 90%

12 OTHER DATA COLLECTION AND ANALYSIS PBM 021 Analysis SLA is 2 Business Hours (120 Minutes) Average is minutes, which is good, UCL is 63.13, which is well within the SLA of 120 minutes. SLA Met 94% Action: Investigate the 2 data points that exceed the UCL Analyze trending data not meeting Western Electric Rules

13 OTHER DATA COLLECTION AND ANALYSIS PBM 007 Analysis SLA is 15 minutes. Mean is 4.2 minutes, UCL is 10.02, Both within the SLA 3 data points above 3σ that some further analysis should be conducted to improve the probability of not exceeding UCL/Specification limit (SLA) SLA Met 98.5% Action: Consider investigation of data points at the upper extreme

14 DATA ANALYSIS Initial Data Analysis showed there were exceptions that related to Training Lack of clarity in Standard Operating Procedures Completeness of Ticket Information Communication of tickets, with shift changes

15 ANALYZING CAUSES OF VARIATION Raw Data Remedy Ticket Remedy Time Stamp SR/STP Assigned Time Stamp PBM 21 Time INC Tuesday, December 01, :18 AM Tue 12/1/ :23 AM 5 INC Tuesday, December 01, :52 AM Tue 12/1/ :29 PM 157 INC Tuesday, December 01, :15 AM Tue 12/1/ :33 PM 138 INC Tuesday, December 01, :50 AM Tue 12/1/2015 1:24 PM 154 INC Tuesday, December 01, :56 AM Tue 12/1/2015 1:27 PM 151 INC Wednesday, December 02, :04 AM Wed 12/2/2015 6:47 AM 47 INC Wednesday, December 02, :32 AM Wed 12/2/2015 7:03 AM 63 INC Wednesday, December 02, :27 AM Wed 12/2/2015 8:36 AM 9 INC Wednesday, December 02, :08 AM Wed 12/2/ :37 PM 149 INC Wednesday, December 02, :39 PM Wed 12/2/2015 2:48 PM 69 INC Wednesday, December 02, :35 PM Wed 12/2/2015 2:57 PM 22 INC Wednesday, December 02, :03 PM Wed 12/2/2015 5:25 PM 22 INC Wednesday, December 02, :55 PM Wed 12/2/2015 5:40 PM 45 INC Wednesday, December 02, :02 PM Thu 12/3/2015 7:48 AM 98 INC Wednesday, December 02, :50 PM Thu 12/3/2015 9:27 AM 87 INC Friday, December 04, :54 AM Fri 12/4/2015 9:11 AM 71 INC Thursday, December 03, :45 PM Fri 12/4/ :00 AM 120 INC Friday, December 04, :16 AM Fri 12/4/ :19 AM 123 INC Friday, December 04, :21 AM Fri 12/4/ :45 AM 225 INC Friday, December 04, :00 AM Fri 12/4/ :05 PM 125 INC Friday, December 04, :14 AM Fri 12/4/ :25 PM 131 INC Friday, December 04, :58 PM Fri 12/4/2015 3:32 PM 34 INC Friday, December 04, :16 PM Mon 12/7/2015 7:15 AM 15 INC Monday, December 07, :50 AM Mon 12/7/2015 7:52 AM 52 INC Monday, December 07, :57 AM Mon 12/7/2015 8:05 AM 5 Western Electric Rules

16 INITIAL PROCESS PERFORMANCE BASELINE

17 USING THE BASELINE INFORMATION

18 MONITORING & STABILIZING PERFORMANCE PBM 006(7/3-7/19) PBM 006 (7/10-7/16) PBM 006 (7/17-7/23) PBM 006 (07/24-7/30) PBM 006 (7/31-8/6) PBM 006 (8/7-8/13) PBM 006 (8/14-8/20) Baseline UCL LCL Process Under Control, No Action

19 PREDICTIVE MODEL LL Mean UL Weekly Requests Simulation Weekly Effort PBM PBM PBM PBM PBM PBM PBM Total effort: Mean Standard error Median Standard deviation Variance Skewness

20 MONITORING PROCESS AND PREDICTING CAPABILITY & AVAILABILITY Control charts were used to ensure PBM processes were stable Process Perform Model used to estimate capacity and availability Over time it was determined that through analysis, improvements to the service desk were realized Original LOE was 240 hours/week Support was reduced to 150 hours/week as opposed to the 193 hours estimated in the Process Performance Model PBMs and SLAs are now consistently being met SLA Met >95%

21 OPTIMIZING PERFORMANCE Established a strategy for improving capability for other PBMs PBM 005, 009 PBM 008 Root Cause Analysis Wrong ticket information Standard Operating Procedure Updates Automated calculation of Ticket analysis Tickets closed with no analysis, but not resolved Improvements are still a work in progress, but the organization is continuing to improve its capability towards 100%

22 OPTIMIZING PERFORMANCE Contract has been expanded to include more support with other service desk activities Quantified information was used to establish a Capability Statement which is now and integral part of their Business Development and Marketing Material

23 QUESTIONS Questions For more information contact: