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Meter Reads-Optimum Calling Process Project Number 0307-23-001 Black Belt Rob Zinsky Northeast region.

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Presentation on theme: "Meter Reads-Optimum Calling Process Project Number 0307-23-001 Black Belt Rob Zinsky Northeast region."— Presentation transcript:

1 Meter Reads-Optimum Calling Process Project Number 0307-23-001 Black Belt Rob Zinsky Northeast region

2 2 Project Definition n Problem Statement: Meter collection performed by the various methods within IBS - call, web, fax, IVR, technician, 3rd party, vendor download, or AMR - are not meeting expectations. Meter collection agings reflect 34,500 meters uncollected, resulting in $5-6M in base billing revenue not being generated on a timely basis. This project will focus on the optimum method for the calling process. n Project Objective: To improve meter collection utilized for billing on a daily basis from 38.5% to 50% and increase the calls made on a daily basis from 65 to 100. This project will additionally focus on which meters to call on. n Project Benefits: Timely revenue billing, reduced unbilled contracts, improved cash flow and customer satisfaction while reducing customer calls into the CIT. Accountability Inspection Measurement

3 3 Big Y/Primary Metric n Y 1 Increase the number of meters collected daily. n Y 2 Increase the number of calls made daily.

4 4 DPMO for Percentage Collected

5 5 DPMO for Calls Made

6 6 Meter reads August (Atlanta)

7 7 Meters Reads August (Houston)

8 8 Meter reads needed by date Houston

9 9 Time Series Plot Houston Meters Needed Houston Meters Collected

10 10 Meter Collection Capacity n Number of Agents working on Meter Read Collection = 29 n Average Call Time for Successful Call = 5.5 minutes n Average Call Time for Unsuccessful Call = 3 minutes n Percentage of Successful Calls = 35% n Number of Calls that can be made per day per agent = 104 n Total number of expected successful calls = 29*40 = 1160 n Maximum time was consumed by customer on meter read recording (4 minutes average) n Number of meters that can be collected per month = 25520 Based on Houston CCC, sample size = 150

11 11 Current Meter Read Process

12 12 Scorecards Contracts Metric ReferencePrior WeekDate at beginning of measure mentEnding ##8/48/58/68/78/8 # Missing Meters Current* 1,2453,4082,1111,7701,5011450 # Missing Meters Past Due* 8,7827,1046,9686,8236,9807022 # Missing Meters (Oracle) 10,02710,5129,0798,5938,4818,472 # Florida Missing Meters Current* 1725,1274,8874,5464,3483365 # Florida Missing Meters Past Due* 263031 34 # Total Missing Meters19,03210,22115,66513,96613,13912,82911,837

13 13 Detailed Process Map

14 14 Detailed Process Map

15 15 High Level Process Map w/ X’s & Y’s

16 16 Detailed Process Map

17 17 Cause & Effect Matrix

18 18 Cause & Effect Matrix Results

19 19 Fishbone Diagram

20 20 FMEA

21 21 Average Cycle time for Collection

22 22 Time & Motion Study

23 23 Aging Contracts Comparison Bad Contact

24 24 Meter Read Aging Analysis Based on Atlanta CCC for the month of August, 2003 Current Proposed Oct.

25 25 Unbilled Root Causes Current Read, prior billing issue

26 26 Related Recommendations n Facilitate faster customer search for getting meter reads by posting meter “Flash cards” n Service technician “On site Customer” training n Enable estimations on all non-vendor contracts with selective exclusion capabilities n Groundwork for meter collection by Sales Force n Customer Orientation n Identification of contact, contact points for meter collection

27 27 Meter Collection Campaign n Prioritization decision parameters: n Estimation allowed n Contract Start Date n Availability of Average Daily Copy Volume (ADCV) n High Average Daily Copy Volume (results in erroneous estimation) n Base billing Amount n Key Accounts n Specific customer requests n Available meter collection capacity (time/manpower) n Final meter collection on contract terminations/upgrade n Number of equipment on contract n Machine Segmentation and color n Parameters change over time due to addition of contracts, equipment, product rollouts, consolidation etc.

28 28 Potential X’s n Generate a “Must Have” missing meter data based on prioritization parameters and distribute in a timely manner n Generate Total Meters list needed in the beginning of the month n Ranked Collection Campaign n Oracle Screens n Clean up unbilled list n Structured Meter Teams n Daily Inspection n Parameters change over time due to addition of contracts, equipment, product rollouts, consolidation etc., need flexibility

29 29 Training Thoughts n MR employees Why is it important, how to sell alternate, follow up training, SOP’s and SPIFFS. n Sales & Service Why and how, possible SPIFFS, training for our customers and ground work for collection of contacts and possibly reads.

30 30 Next Steps n 2 Sample t test n Design of Experiment n Clean Up Unbilled n Collect Detailed Data on X’s n Implement Quick Hits


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