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Presentation Lube Oil Blending Plant Performance Evaluation

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Lube Oil Blending Plants (LOBPs) have a key role to play in the manufacture, sales and distribution of bulk and packed lubricants. This study gives an overview of the Global lubricants market and proposes a methodology for Performance evaluation of Lube Oil Blending Plants.

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  • thanks for this good presentation
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  • You can reach me at vrazdan@yahoo.com
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  • Vikram, have been trying to reach you. john@ifluids.com
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  • Charles, thanks for the comments
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  • Hi Vikram, A few thoughts and comments. A number of companies have been benchmarking. Kline have done so on a macro level business level and PIMSMalik on a micro (plant) level. The Kline benchmarks is rather general and gives insight to major changes in the business, whereas the PIMS benchmarks provide more detailed comparisons. PIMS have been benchmarking plants since 1998 and have since developed their methodologies to a good standard …. but more importantly with a wide number of companies. Ultimately the benchmarks should be used by the plant manager, or regional manager, to make decisions about how to operate the plant, assuming that there is a business case for the plant to exist in the first place. As such some of the items you include is not within remit of the plant manager or regional manager. R&D capability for instance is often managed by those who manage the product, and tend to be centralised at a research facility. Also not all plants are new, and some were built in the 1950s and as such the degree of sophistication varies wildly from automated to manual blending and will impact the number of personnel and consequently the costs of running the plant. Good that you have included formulation complexity as this will make it easier or difficult to blend the product. Modern formulations have ever increasing additive viscosities and have rendered the older blending plants obsolete due to their low shear mixing capabilities, even assuming in the meantime pumping/flow capabilities have not been affected. There are many other items you included which merit further discussion. I do like proposals for benchmarking with non-oil companies however, otherwise you could be comparing between those considered to be a bad bunch and not the leading the companies. I’m not sure however whether FMCG companies are the example, as lubricants are “not so fast” as compared with the Unilevers of this world. Good luck
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Presentation Lube Oil Blending Plant Performance Evaluation

  1. 1. Lube Blending plants Global market study and Performance evaluation Feb 2016 Vikram Razdan (vrazdan@plaxgroup.co.uk) Vikram Razdan Business Consultant Plax Ltd, UK
  2. 2. 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
  3. 3. 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. Vikram Razdan (vrazdan@plaxgroup.co.uk) 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 million tonnes China 6 million tonnes India, 1.7 million tonnes 2012
  4. 4. Top 20 countries in 2012 by lubricants consumption Global consumption: 35 million tonnes Vikram Razdan (vrazdan@plaxgroup.co.uk)
  5. 5. 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)
  6. 6. 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 Vikram Razdan (vrazdan@plaxgroup.co.uk) 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
  7. 7. Key players in the global lubricants market Manufacturers 130 major oil companies (IOCs and NOCs) 590 independent manufacturers Volume mix Top 10 manufacturers ~ 50% Rest 710 manufacturers ~50% Top 15 (2012) 1. Shell 2. ExxonMobil 3. BP 4. Chevron 5. Total 6. PetroChina 7. Sinopec 8. Idemitsu 9. Fuchs 10. Lukoil (1.3 MMTPA) 11. JX Nippon Oil 12. Petronas 13. Petramina 14. Gulf/Houghton 15. Valvoline (Ashland) (source: Fuchs) Vikram Razdan (vrazdan@plaxgroup.co.uk) • 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
  8. 8. Lube manufacturing/blending ABB: Automatic Batch Blender SMB: Simultaneous Metering Blender ILB: Inline Blender DDU: Drum Decanting Unit Vikram Razdan (vrazdan@plaxgroup.co.uk) Plant complexity depends upon type and number of formulations / grades
  9. 9. Lubricants formulations are technically complex Engine Oils Base 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-oxidant Polymethacrylate or Olefin Copolymer • VII (Viscosity Index Improver) Other additives • Friction Modifiers • Dispersants • Detergents • Pour point depressants • Anti-foam agents Grease Base 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-wear Molydisulphide or Graphite • Solid lubricants Other additives • Oxidation inhibitors • Friction Modifiers • Tackifiers • Corrosion and Rust preventives • Metal deactivators Gear Oils Base 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)
  10. 10. 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 ExxonMobil BP Top3Independents
  11. 11. 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 MTPA Kolkata: 90,000 MTPA Silvassa: 30,000 MTPA Taloja: 20,000 MTPA Asaoti: 60,000 MTPA 7th blending plant in Sri Lanka (18,000 MTPA) Other local key players: BPCL. 3 blending plants, 4 filling plants HPCL. 7 blending plants TideWater: 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)
  12. 12. Lube blending plant – Benchmarking possibilities Vikram Razdan (vrazdan@plaxgroup.co.uk) 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
  13. 13. 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)
  14. 14. Lube blending plant – Strategic parameters Vikram Razdan (vrazdan@plaxgroup.co.uk) 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 25 Plant capacity in tonnes per annum > 200,000 1 100,000 to 200,000 0.75 < 100,000 0.5 Blending complexity (formulations/batch size/changeovers/cycle-time) 15 Level of automation Fully automated 1 Semi-automated 0.75 No automation 0.5 Feedstock availability 15 Base oil manufacturing Manufacturer 1 Non-Manufacturer 0.5 R&D capability 5 Product formulations > 250 1 100 to 250 0.75 < 100 0.5 Power and Utilities 5 Captive or Procure Captive generation 1 Procure 0.5 Quality and Environmental compliance (ISO standards) 5 Level of compliance ISO9000 0.5 ISO14000 0.5 Scores to be allocated for each parameter to generate a Plant index
  15. 15. 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)
  16. 16. Vikram Razdan (vrazdan@plaxgroup.co.uk) Lube blending plant – Operational parameters Cost Quality Time Impact on plant performance Value Tendency is to focus on costs only 60% 25% 15%
  17. 17. Operational parameters in detail Parameter Fixed Variable Quality Additives Base oil Blending process • Level of automation • Batch size Product downgrades Product testing Cost Maintenance Product testing Staff/Labour Base oil Additives Inventory Containers Packaging Product loss Energy consumption Time Cycle time • Blending • Filling Product testing Customer ordering to delivery Procurement lead time Vikram Razdan (vrazdan@plaxgroup.co.uk)
  18. 18. Operational metrics Vikram Razdan (vrazdan@plaxgroup.co.uk) Parameter Operational metrics Measurement unit Gross weightage (%) Standalone Weightage (%) Quality Base oil quality (VI, stability, fluidity, evaporation) % variation 25 10 Additive dosing accuracy % variation 2.5 Bulk product downgraded % of total 5 Number of filled product containers downgraded % of total 5 Product tests done per year number 2.5 Cost Base oil cost per tonne 60 20 Additive cost per tonne 5 Raw material inventory cost per tonne 5 Work in process inventory cost per tonne 10 Maintenance cost per tonne 5 R&D cost per tonne 2.5 Product loss per tonne 2.5 Employee cost per tonne 10 Time Blending cycle time for ABB per tonne 15 2.5 Blending cycle time for SMB/ILB per tonne 2.5 Decanting cycle time for DDU per tonne 1.25 Filling cycle time for cans per tonne 2.5 Filling cycle time for drums per tonne 1.25 Procurement lead time per tonne 2.5 Customer 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 (%)
  19. 19. 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 = 10 2.5 x 1.0 = 2.5 5 x 1.0 = 5 5 x 1.0 = 5 2.5 x 1.0 = 2.5 10 x 0.75 = 7.5 2.5 x 0.9 = 2.25 5 x 0.8 = 4 5 x 0.9 = 4.5 2.5 x 1.0 = 2.5 Cost Time Operating Efficiency (max 100%) 83.5 85.31 20.38 44 53.88 14.5 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)
  20. 20. Vikram Razdan (vrazdan@plaxgroup.co.uk) Performance Index example Two hypothetical Lube blending plants Plant Plant 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
  21. 21. Vikram Razdan (vrazdan@plaxgroup.co.uk) Performance Index sensitivity (MonteCarlo simulation) Two hypothetical Lube blending plants Plant Plant Index Operating Efficiency (%) Performance Index Capacity Utilisation (%) Net Performance Index a d c = a x b d c x d Minimum A 74.54 82.86 61.76 95 58.67 B 87.12 84.63 73.73 85 62.67 Average A 74.95 83.50 62.58 95 59.45 B 87.57 85.31 74.71 85 63.50 Maximum A 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)
  22. 22. End of presentation Vikram Razdan (vrazdan@plaxgroup.co.uk)
  23. 23. Vikram Razdan (vrazdan@plaxgroup.co.uk) Annex 1 PLANT INDEX SCORE Parameter Weightage (%) Yardstick Level Multiplier Plant A Plant B Plant 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 25 Plant capacity tonnes per annum >200000 1 0.75 18.75 0.75 18.75 100000 to 200000 0.75 <100000 0.5 Blending complexity (formulations/batch size/changeovers/cycle-time) 15 Level of automation Fully automated 1 1 15 0.75 11.25Semi-automated 0.75 Manual 0.5 Feedstock availability 15 Base oil manufacturing Base oil producer 1 1 15 1 15Non-base oil producer 0.5 R&D capability 5 Product formulations >250 1 0.75 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 5 Procure 0.5 Quality, Safety and Environmental compliance (ISO standards) 5 Level of compliance ISO9000 0.5 0.5 2.5 0.5 2.5 ISO14000 0.5 0.5 2.5 75 87.5
  24. 24. Vikram Razdan (vrazdan@plaxgroup.co.uk) Annex 2 OPERATING EFFICIENCY SCORE Parameter Performance metric Measurement 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 20.38 Additive dosing accuracy % variation 2.5 1 2.5 0.75 1.875 Bulk product downgraded % of total 5 1 5 0.8 4 Number of filled product containers downgraded % of total 5 1 5 0.9 4.5 Product 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 4.5 Raw material inventory cost per tonne 5 0.9 4.5 0.7 3.5 Work in process inventory cost per tonne 10 0.9 9 0.7 7 Maintenance cost per tonne 5 0.5 2.5 1 5 R&D cost per tonne 2.5 0.5 1.25 0.75 1.875 Product loss per tonne 2.5 0.9 2.25 0.8 2 Employee 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 1.88 Decanting cycle time time for DDU per tonne 1.25 1 1.25 0.75 0.94 Filling cycle time for cans per tonne 2.5 1 2.5 0.9 2.25 Filling cycle time for drums per tonne 1.25 1 1.25 0.9 1.13 Procurement lead time per tonne 2.5 0.8 2 0.5 1.25 Customer ordering to delivery time per tonne 2.5 1 2.5 0.7 1.75 83.5 85.31
  25. 25. Vikram Razdan (vrazdan@plaxgroup.co.uk) Annex 3 NET PERFORMANCE INDEX Plant Plant 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 85.3125 74.65 85 63.45 MonteCarlo simulation (minimum, 5% standard deviation) A 74.54 82.86 61.76 95 58.67 B 87.12 84.63 73.73 85 62.67 MonteCarlo simulation (average, 5% standard deviation) A 74.95 83.50 62.58 95 59.45 B 87.57 85.31 74.71 85 63.50 MonteCarlo simulation (maximum, 5% standard deviation) A 75.41 84.20 63.49 95 60.32 B 87.91 85.98 75.58 85 64.25

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