1. SMART METERING GAS Director Strategic Market Development, André Wankelmuth, Itron Istanbul, May 9th, 2014 Revenue Potential or Waste of Money?

2. SMART METERING - WHY SHOULD YOU CARE? ? Net Benefit Equipment Cost + installation cost (Capital Expenses) Efficiency Improvements + Avoided Costs + New services (Cash Benefits) Break Even 1.0bn revenue USD $$$$$$$$$$ 450 employees 1.5 m gas meters installed 2. 5 m m³ distributed per year 30 m yearly Investment budget $$$ 100 m profit USD

3. THE RIGHT WAY? From features to benefits – technology in search of a problem Assets Smart Meter Functionalities Remote management of meters Benefits Reduced meter reading costs Monetary value milllions/year *Methodology for performing a CBA (own representation acc. Giordano, et al., 2012)

4. LOOKING AT PROBLEMS, THEN TECHNOLOGY »Do you know the impact meter accuracy has on your net income? »Have you wondered about the risk exposure of prepayment meters to gas price and exchange rate fluctuations? »Do you know the impact of improving your read-to-bill-to-cash cycle ? 8m8m 5% undersized 5% undercounting $$ 100 m 60 days outstanding receivables $$$ $$$$$$$$ 9m9m 250 m USD worth of gas hedged $$

5. CREATING A WIN-WIN VIA SHARED DISCUSSIONS at stake ? Resource management Regulatory compliance Consumer satisfaction Revenue protection Network delivery performance Operational efficiency What is Safety and security

6. RISK ANALYSIS METHODOLOGY Identify Risks 1 Business model analysis using the business model canvas to find areas for improvement Risk analysis methodology to identify key risks to be addressed Revenue protection

7. FMEA like approach Risk Assessment impact likelihood 4m4m 1m1m RISK ANALYSIS METHODOLOGY Prioritize Risks 2 Identify Risks 1 Assess Risks 3 assess impact, likelihood quantify impact (min, max) cash | income effect Revenue protection 40-70 M YEARLY IMPACT – 4-7%

8. RISK ANALYSIS METHODOLOGY Prioritize Risks 2 Identify Risks 1 Assess Risks 3 Risks Mgmt Opt. 4 Eliminate Anticipate Moves: Anticipate, Mitigate, Eliminate, Transfer, Increase, Collect more data, Do nothing Revenue protection Revenue protection

9. RISK ANALYSIS METHODOLOGY Prioritize Risks 2 Identify Risks 1 Assess Risks 3 Risks Mgmt Opt. 4

10. RISK ANALYSIS METHODOLOGY Prioritize Risks 2 Identify Risks 1 Assess Risks 3 Risks Mgmt Opt. 4 Risk Mgmt Strategy. 5 $

11. RISK ANALYSIS METHODOLOGY Prioritize Risks 2 Identify Risks 1 Assess Risks 3 Risks Mgmt Opt. 4 Risk Mgmt Strategy. 5 $ Investment $150 M Payback 5 years IRR 55% Scenario evaluation: 100 M $ NPV

12. INVESTOR│TURKEY Turkey Turkish DSO 199 M 61 M MAIN RISKS IDENTIFIED  Timeliness of readings  Supply/demand balancing 3 YEAR MIN IMPACT3 YEAR MAX IMPACT  Tamper detection  Meter rightsizing

13. THANK YOU www.itron.com

14. GOVERNMENT│UZBEKISTAN Uzbekistan Deputy Prime Minister 694 M 259 M MAIN RISKS IDENTIFIED  Security Of Supply  Demand response  Revenue protection 3 YEAR MIN IMPACT3 YEAR MAX IMPACT

15. EXAMPLE: RATE MAKING »Workshop 1: Basic data mining / gathering »Workshop 2: understanding business processes (business process audit), verifying assumptions »Workshop 3: Presentation of results, fine tuning of underlying assumptions »In depth analysis of current assets and planned invests (KR), Opex (KA / CS), revenue streams per segment »Rate making regulation: »Findings: regulated margin will drop 50% when regulation applied in 8 years (!) instead of expected increase.

16. EXAMPLE: METER TO CASH »Up to 60 days outstanding receivables Assuming 1Bn$ yearly revenue with 1.5 M residential customers Average 2.74 M$ per day (1Bn$ / 365) Or 110$ per point in 60 days (2.74 M$ /1.5M meters *60 days) »Payment period (e.g. 1 month) will be programmed by utility or factory »At the end of the payment period the meter gives a warning and the consumer has 5 days to pay the total volume of what he consumed. »If the consumer doesn’t pay the total outstanding volume on the meter, the valve of the meter will close until the total outstanding volume is paid.

17. City Gate Customer 1 Customer 2 1 st read 2 nd read Estimated data City Gate Customer 1 Customer 2 1 2 1 st read 2 nd read Granular data