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Driving out waste by revealing hidden data

Property Repairs: the problem

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C/Sat84

%

RFT 93%

Recalls 7%

Variat-ions 40%

RFT 71%

???

The As Is process

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64 process steps

Process walk-through

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We needed data (in 6 weeks)

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Right First Time (e.g. fault repairs)

Complaints (e.g. formal and informal)

Timeliness (e.g. faults fixed when promised)

Customer Satisfaction (e.g. perception)

Objective customer feedback (e.g. delays, RFT, changed appointments)

Input volumes (e.g. fault calls, failure demand)

Costs (e.g. value of jobs)

Processing times (e.g. call handling)

Delay times (e.g. approval times)

Process Measures:

OutputMeasures:

Satisfaction Measures:

Data collection approach

Existing data CRM system

BUT: few existing reports met our needs

Customer Survey BUT: not very current, or

specific enough Contractor SLA Reports

BUT: questions over definitions

Complaints/Quality Audits BUT: snapshots, few trends

New data CRM system

Case tracking (times) Case categorisation

Customer Interviews Using current cases

Contractor Log Daily record of cases

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CRM data quality challenges Over 5% of the data we

extracted for this review had “issues”.

It doesn’t sound a great deal, but it meant that two of the main reports we produced could not be used without extensive manipulation in Excel every time we ran them.

Examples: Repair types incorrectly flagged Properties incorrectly flagged Fields not filled in – not necessarily

critical to operations, but critical to analysis

Free text in surveys and questionnaires

Text Case – inconsistent case use: Upper / Lower

Use of Excel when CRM could/should have been used

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Once we had cleaned the data…

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The voice of the customer…

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43% felt they

were not kept

informed

58% of orders had up to 3 interactions.25% involved 6 or more interactions.28% of customers called after their order was placed.

4100 calls logged &

categorised over 6 weeks

75% completedat first visit

Some conclusions…It was clear from the data used to manage the Repairs process prior to

this project that the organisation was relatively “immature” in its approach to performance management and continuous improvement. There were too few trend graphs which would enable managers to see whether or

not performance was improving (monthly snapshots seemed to be preferred) There was no data routinely available to track some of the important process

response times; in some instances because fields within CRM were not being used Root cause analysis of some problems was impossible because they were

reported in categories such as “Other” or “No reason given” Data quality issues had not been recognised and their impact on the organisation’s

ability to drive improvement had not been understood

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Some benefits…

Staff loved using the CRM system to improve the way they logged customer contacts because it meant they could give a better service

Managers had better data to hold staff and contractors accountable for performance

Some very simple Quick Wins leading to 30% reduction in “Failure Demand” in 3 months

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Presented by Ian Seath at the Operational Research Society’s 2014 Conference (OR56)

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ian.seath@improvement-skills.co.uk

07850 728506

@ianjseath

uk.linkedin.com/in/ianjseath