presentation lube oil blending plant performance evaluation
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Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube Blending plantsGlobal market study and Performance evaluation
Feb 2016
Vikram RazdanBusiness Consultant
Plax Ltd, UK
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Objectives
• Present an overview of the global lubricants industry
• Lube blending, product formulations and growth markets
• Propose a methodology for developing a Lube Blending plant Performance Index, based on Plant Index and Operating efficiency
• MonteCarlo simulation for sensitivity analysis of Performance Index
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Global lubricants market overview
China (6 million tonnes) and India (1.7 million tonnes) are the fastest growing markets.
Global lubricants growth @0.6-0.7% for next 10 years as per Total, France (2015)
Lubricants market dominated by International Oil companies (IOCs) and National Oil companies (NOCs), with Shell as the market leader.
World’s largest Independent lube blender: Fuchs
World’s largest blending plant commissioned by Total in Singapore in 2015 (310,000 metric tonnes per annum) with a workforce of 100
Global,35 mil-
lion tonnes
China 6 million tonnes India,
1.7 million tonnes
2012
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Top 20 countries in 2012 by lubricants consumption
Global consumption: 35 million tonnes
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Global lubricants demand snapshot
Fastest growing market is Asia Pacific (mainly China and India)North America and Western Europe are mature markets
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Finished lubricants segment wise (2012)
Automotive oils segment dominated by major oil companies (IOCs and NOCs)
Industrial oils and MWF/CP/Greases dominated by independent manufacturers
Automotive oils Engine oils, gear oils, transmission fluids (ATF), brake fluids, coolants/anti freeze
Industrial oils Hydraulic fluids, turbine oils, industrial gear oils, spindle oils, open gear compounds, rolling oils, etc.
Process oils For manufacturing of textiles, optical-cables, tyres, polymers, cosmetics, fertilizers, explosives and crop sprays.
MWF/CP/Greases Metalworking fluids, Corrosion preventives and Greases
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Key players in the global lubricants market
Manufacturers130 major oil companies (IOCs and NOCs)590 independent manufacturers
Volume mix Top 10 manufacturers ~ 50% Rest 710 manufacturers ~50%
Top 15 (2012)
1. Shell2. ExxonMobil3. BP4. Chevron5. Total6. PetroChina7. Sinopec8. Idemitsu9. Fuchs 10. Lukoil (1.3 MMTPA)11. JX Nippon Oil12. Petronas13. Petramina14. Gulf/Houghton15. Valvoline (Ashland)
(source: Fuchs)
• IOCs and NOCs have market domination• Rest of the market highly fragmented• IOCs benefitting the most in shift from mineral (SN)
to semi-synthetic/ synthetic base oils (PAO/Esters)• Independents play a pivotal role in the industrial
lubricants market• More focus on high gross margins speciality
lubricants (automotive and industrial), especially in mature markets
Strategic drivers
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube manufacturing/blending
ABB: Automatic Batch BlenderSMB: Simultaneous Metering BlenderILB: Inline BlenderDDU: Drum Decanting Unit
Plant complexity depends upon
type and number of formulations /
grades
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lubricants formulations are technically complex
Engine OilsBase oil Group I, II (Low S), III (Low S, High VI), IV (Synthetic) : 80 to 90%
Additives (10 to 20%)ZDDP or TCP• Anti-wear• Corrosion inhibitor• Anti-oxidantPolymethacrylate or Olefin Copolymer• VII (Viscosity Index Improver)
Other additives• Friction Modifiers• Dispersants• Detergents• Pour point depressants• Anti-foam agents
GreaseBase oil Group I (90-95%) or IV (Synthetic) : 75 to 90%
Thickeners (5 to 20%) • Lithium• Lithium complex• Aluminium complex• Clay
Additives (0 to 10%)ZDP• Extreme Pressure• Anti-wearMolydisulphide or Graphite• Solid lubricants
Other additives• Oxidation inhibitors• Friction Modifiers• Tackifiers• Corrosion and Rust preventives• Metal deactivators
Gear OilsBase oil Group I or IV (Synthetic) : 85 to 90%
Additives (5 to 15%)Sulphur-Phosphorus• Extreme Pressure• Anti-wear• Corrosion inhibitor
Other additives• Friction Modifiers• Dispersants• Pour point depressants• Anti-foam agents• Metal deactivators
Mono-grade (SAE 10, 20 ,30, 40, 50)Multi-grade (SAE 5W30, 10W30, 20W40, 20W50)API SJ, SL, SM, SN (Petrol)API CF-H, CG-J, CF-I (Diesel)
NLGI grade (6 softest to 000 hardest)
API GL 4 (moderate duty, low speed)GL 5 ( heavy duty, high speed)Mono-grade (SAE 80, 90)Multi-grade (SAE 80W90, 75W90, 85W140)
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube blending plants – some figures
Fuchs: 33 blending plants worldwide. Largest independent manufacturer in the world. Gross margin: 37%, Net profit margin: 11.4% (2012)
77 Lubricants, Holland: Largest independent blender in Europe (130,000 MTPA)
Other key independent blenders: Motul, Pentosin, Liqui Moly, Unil-Opal, Carlube, Royal Purple, Amsoil, Red Line, Torco, Exol (largest in UK)
•50 blending plants worldwide•8 blending and 3
grease plants in China with largest in Tainjin (280,000 MTPA)• Indonesia (120,000
MTPA)• India (55,000 MTPA)
•30 blending plants worldwide•Operates the 2nd
largest plant in the world. •2 blending plants in
China.• India (70,000 MTPA)
•20+ blending plants worldwide.•2 blending plants in
China (Taicang and Shenzen)•5 blending plants in
India (BP/Castrol)
Shell
ExxonMobilBP
Top
3In
depe
nden
ts
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube blending in China and India – Growth markets
India
Industrial lubricants have 54% market share
IOCL is the largest blender (6 plants in India 505,000 MTPA)
Chennai: 140,000 MTPA Mumbai: 135,000 MTPAKolkata: 90,000 MTPASilvassa: 30,000 MTPATaloja: 20,000 MTPAAsaoti: 60,000 MTPA
7th blending plant in Sri Lanka (18,000 MTPA)
Other local key players: BPCL. 3 blending plants, 4 filling plantsHPCL. 7 blending plantsTideWater: 5 blending plants
1.7 million tonnes (2012)
China
Industrial lubricants have 46% market share.
PetroChina is the largest blender. 10 blending plants. Total capacity: 1700,000 MTPA
Sinopec is the second largest blender. 11 blending plants. Total capacity: 1146,000 MTPA
Other local key players:• CNOOC.• Feoso Group. 5 blending
plants. Total capacity: 227,000 MTPA• Longcheng Shiye. 3 blending
plants (150,000 MTPA)
6 million tonnes (2012)
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube blending plant – Benchmarking possibilities
Performance Compare vis-à-vis the best practices of the leading Lube blending plant
Strategic Critical success factors (compare with other industries like FMCG and Paints)
Operational Evaluate running cost, staffing and productivity
Process Process mapping and technology
Product Product design/packaging (compare with market leader / paints industry for best practices)
Financial Financial ratios and return on investment
Performance level = Strategic positioning x Operational effectiveness
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Proposed methodology for creating Lube blending plant Performance Index
Plant Index Based on Strategic parameters• Plant location• Capital Investment• Blending complexity• Feedstock availability• R&D capability• Power and Utilities• Quality and Environmental compliance
Operating efficiency Based on Operational parameters• Quality• Cost• Time
Performance Index (Plant Index) x (Operating Efficiency)
Net Performance Index (Performance Index) x (Capacity Utilisation)
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube blending plant – Strategic parameters
Parameter Weightage (%) Yardstick Level Multiplier(0.5 to 1.0)
Plant location (low freight cost, market proximity, duties and taxes, labour costs) 30 Labour costs
> $10ph 0.5< $10ph 1
Capital investment • Plant size/Economies of scale (high production capacity, low cost per tonne) • Blending/Filling systems for product quality and quantity (high accuracy, low variance) • Storage and Warehousing
25Plant capacity in tonnes per annum
> 200,000 1100,000 to 200,000 0.75
< 100,000
0.5
Blending complexity (formulations/batch size/changeovers/cycle-time) 15 Level of
automation
Fully automated 1Semi-automated 0.75No automation 0.5
Feedstock availability
15
Base oil manufacturing
Manufacturer 1Non-Manufacturer 0.5
R&D capability
5
Product formulations
> 250 1100 to 250 0.75< 100 0.5
Power and Utilities
5
Captive or Procure
Captive generation 1Procure 0.5
Quality and Environmental compliance (ISO standards)
5
Level of compliance
ISO9000 0.5ISO14000 0.5Scores to be allocated for each parameter to generate a Plant index
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Plant Index example
Two hypothetical Lubricants blending plants Plant A
• In an OECD developed country• 100,000 MTPA• Fully automated• Base oil manufacturer• 200 product formulations• Procure power• ISO9001/TS16949 and 14001
compliant
Plant B• In a developing country• 150,000 MTPA• Semi automated• Base oil manufacturer• 300 product formulations• Captive power generation• ISO9001 /TS16949 compliant
Plant location 0.5 x 30 = 15.00 1.0 x 30 = 30.00
Capital investment 0.75 x 25 = 18.75 0.75 x 25 = 18.75
Blending complexity 1.0 x 15 = 15.00 0.75 x 15 = 11.25
Feedstock availability 1.0 x 15 = 15.00 1.0 x 15 = 15.00
R&D capability 0.75 x 5 = 3.75 1.0 x 5 = 5.00
Power and Utilities 0.5 x 5 = 2.50 1.0 x 5 = 5.00
Quality and Environmental Standards
0.5 x 5 + 0.5 x 5 = 5.00 0.5 x 5 = 2.50
Plant Index (max 100) 75 87.5
(Detailed worksheet in Annex 1)
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Lube blending plant – Operational parameters
Cost
Quality
Time
Impact on plant performance
Valu
e
Tendency is to focus on costs only
60%
25%
15%
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Operational parameters in detail
Parameter Fixed Variable
Quality Additives Base oilBlending process• Level of automation• Batch sizeProduct downgradesProduct testing
Cost MaintenanceProduct testingStaff/Labour
Base oilAdditivesInventoryContainersPackagingProduct lossEnergy consumption
Time Cycle time• Blending• FillingProduct testing
Customer ordering to deliveryProcurement lead time
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Operational metricsParameter Operational metrics Measurement
unitGross weightage
(%) Standalone Weightage (%)
Quality
Base oil quality (VI, stability, fluidity, evaporation) % variation
25
10Additive dosing accuracy % variation 2.5Bulk product downgraded % of total 5Number of filled product containers downgraded % of total 5Product tests done per year number 2.5
Cost
Base oil cost per tonne
60
20Additive cost per tonne 5Raw material inventory cost per tonne 5Work in process inventory cost per tonne 10Maintenance cost per tonne 5R&D cost per tonne 2.5Product loss per tonne 2.5Employee cost per tonne 10
Time
Blending cycle time for ABB per tonne
15
2.5Blending cycle time for SMB/ILB per tonne 2.5Decanting cycle time for DDU per tonne 1.25Filling cycle time for cans per tonne 2.5Filling cycle time for drums per tonne 1.25Procurement lead time per tonne 2.5Customer ordering to delivery time per tonne 2.5
Total 100
Scores to be allocated for each metric with reference to best-in-class blending plant to generate Operating efficiency (%)
Vikram Razdan (vrazdan@plaxgroup.co.uk))
Operating Efficiency example
Two hypothetical Lube blending plants Plant A
• High quality base oil• Low process variation• Low product downgrades• Medium base oil cost• High maintenance cost• High R&D cost• High employee cost• Optimum cycle time• Median procurement lead
time
Plant B• Medium quality base oil• Some process variation• Medium product downgrades• Optimum base oil cost• Low maintenance cost• Medium R&D cost• Low employee cost• Median cycle time• High procurement lead time
Quality 10 x 1.0 = 102.5 x 1.0 = 2.55 x 1.0 = 55 x 1.0 = 52.5 x 1.0 = 2.5
10 x 0.75 = 7.52.5 x 0.9 = 2.255 x 0.8 = 45 x 0.9 = 4.52.5 x 1.0 = 2.5
Cost
Time
Operating Efficiency (max 100%) 83.5 85.31
20.38
44 53.8814.5 11.06
Setting the benchmark best-in-class as reference
would be the main issue in generating blending plant
operating efficiency.
25
(Detailed worksheet in Annex 2)
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Performance Index example
Two hypothetical Lube blending plants
PlantPlant Index
Operating Efficiency (%)
Performance Index
Capacity Utilisation (%)
Net Performance Index
a d c = a x b d c x d
A 75 83.5 62.63 95 59.49
B 87.5 85.31 74.64 85 63.45
Key observations
Plant A, based in an OECD developed country, achieves a good Net Performance Index as compared to Plant B (located in a developing country), in spite of higher operating costs
Plant Index should have minimal variation, and thus scope for improvement lies mainly in increasing Operating Efficiency and Capacity Utilisation
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Performance Index sensitivity (MonteCarlo simulation)
Two hypothetical Lube blending plants
PlantPlant Index
Operating Efficiency (%)
Performance Index
Capacity Utilisation
(%)
Net Performance
Index
a d c = a x b d c x d
MinimumA 74.54 82.86 61.76 95 58.67
B 87.12 84.63 73.73 85 62.67
AverageA 74.95 83.50 62.58 95 59.45
B 87.57 85.31 74.71 85 63.50
MaximumA 75.41 84.20 63.49 95 60.32
B 87.91 85.98 75.58 85 64.25
Standard deviation (SD) of 5% has been assumed for all scores in the example. However, SD should depend on historical data which should give more realistic results
(Detailed worksheet in Annex 3)
Vikram Razdan (vrazdan@plaxgroup.co.uk)
End of presentation
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Annex 1
PLANT INDEX SCORE
ParameterWeightage
(%) Yardstick Level Multiplier Plant A Plant BPlant location (low freight cost, market proximity, duties and taxes, labour costs) 30 Labour costs >$10ph 0.5 0.5 15 1 30
<$10ph 1
Capital investment • Plant size/Economies of scale (high production capacity, low cost per tonne) • Blending/Filling systems for product quality and quantity (high accuracy, low variance) • Storage and Warehousing
25Plant capacity tonnes per annum
>200000 1
0.75 18.75 0.75 18.75100000 to 200000 0.75
<100000 0.5
Blending complexity (formulations/batch size/changeovers/cycle-time) 15 Level of automation
Fully automated 11 15 0.75 11.25Semi-automated 0.75
Manual 0.5
Feedstock availability 15 Base oil manufacturing
Base oil producer 11 15 1 15Non-base oil
producer 0.5
R&D capability 5 Product formulations
>250 10.75 3.75 1 5100 to 250 0.75
<100 0.5
Power and Utilities 5 Captive or Procure
Captive generation 1 0.5 2.5 1 5Procure 0.5
Quality, Safety and Environmental compliance (ISO standards) 5 Level of compliance
ISO9000 0.5 0.5 2.5 0.5 2.5ISO14000 0.5 0.5 2.5
75 87.5
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Annex 2
OPERATING EFFICIENCY SCORE
Parameter Performance metricMeasurement
unit
Gross weightage
(%)
Standalone Weightage
(%) Plant A Plant B
Quality
Base oil quality (VI, stability, fluidity, evaporation) % variation
25
10 1 10
25
0.75 7.5
20.38Additive dosing accuracy % variation 2.5 1 2.5 0.75 1.875Bulk product downgraded % of total 5 1 5 0.8 4Number of filled product containers downgraded % of total 5 1 5 0.9 4.5Product tests done per year number 2.5 1 2.5 1 2.5
Cost
Base oil cost per tonne
60
20 0.75 15
44
1 20
53.88
Additive cost per tonne 5 0.9 4.5 0.9 4.5Raw material inventory cost per tonne 5 0.9 4.5 0.7 3.5Work in process inventory cost per tonne 10 0.9 9 0.7 7Maintenance cost per tonne 5 0.5 2.5 1 5R&D cost per tonne 2.5 0.5 1.25 0.75 1.875Product loss per tonne 2.5 0.9 2.25 0.8 2Employee cost per tonne 10 0.5 5 1 10
Time
Blending cycle time for ABB per tonne
15
2.5 1 2.5
14.5
0.75 1.88
11.06
Blending cycle time for SMB/ILB per tonne 2.5 1 2.5 0.75 1.88Decanting cycle time time for DDU per tonne 1.25 1 1.25 0.75 0.94Filling cycle time for cans per tonne 2.5 1 2.5 0.9 2.25Filling cycle time for drums per tonne 1.25 1 1.25 0.9 1.13Procurement lead time per tonne 2.5 0.8 2 0.5 1.25Customer ordering to delivery time per tonne 2.5 1 2.5 0.7 1.75
83.5 85.31
Vikram Razdan (vrazdan@plaxgroup.co.uk)
Annex 3
NET PERFORMANCE INDEX
PlantPlant Index Operating Efficiency
(%) Performance Index Capacity Utilisation (%)
Net Performance Index
a d c = a x b d c x dA 75 83.5 62.63 95 59.49B 87.5 85.3125 74.65 85 63.45
MonteCarlo simulation (minimum, 5% standard deviation)A 74.54 82.86 61.76 95 58.67B 87.12 84.63 73.73 85 62.67
MonteCarlo simulation (average, 5% standard deviation)A 74.95 83.50 62.58 95 59.45B 87.57 85.31 74.71 85 63.50
MonteCarlo simulation (maximum, 5% standard deviation)A 75.41 84.20 63.49 95 60.32B 87.91 85.98 75.58 85 64.25
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