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Thursday, May 23, 2019
Case Studies PPA Case Study - Choosing a Rolling Lubricant

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Description of Problem

A steel rolling mill was considering changing suppliers for rolling mill lubricant. Mill lubricants are critical to a variety of product characteristics, but to assess multiple vendors required fully replacing the lubricant on the mill and running trial production through the mill and the remaining processing steps so the sample size was limited. There were two different types of annealing furnaces in use, and the lubricant might very well have responded differently to the two types. For each furnace type, there were two furnace practices tested. Additionally, one of the vendors changed their formulation in the middle of the study to try to improve their ratings.

The critical product requirement was sheet cleanliness at the end of the process as defined by total carbon and top and bottom surface iron residuals. The new lubricants were expected to produce cleaner steel surfaces than the old animal-fat based lubricant. This was needed since the new anneal furnaces were found to produce dirtier steel than the old ones.

Procurement had agreed to the test and presumed that they would only need to prove that all of the vendors were capable of making the cleanliness requirements and that they would then choose the least expensive alternative. The research questions for this portion of the study were:

  • What effects do the four vendor's rolling lubricants have on the cleanliness of both types of annealed steel?
  • What are the capabilities of the different formulations in meeting internal cleanliness specifications?


Effect of New Lubricants

To analyze the data, two approaches were used. An ANOVA was used to determine if there was a statistically significant shift in the average total carbon and top and bottom iron across the new formulations and as compared to the old formulation. To determine the capabilities of the different process streams, Process Performance Analysis was done using ROI Senior Consultant Michael V. Petrovich’s program PERFORM. The analysis took about a week once the four months of data were collected.


The average cleanliness was not greatly affected by using the new oils for old anneal furnace coils (though Vendor 2 was slightly cleaner on average). However, most of the new lubricants did significantly improve the cleanliness of the steel that was annealed in new anneal furnaces. The exceptions were both formulations from Vendor 4 which interacted with the new anneal furnace atmosphere and resulted in a higher average total surface carbon as compared to the other new lubricants.

Vendor 2 showed the best ability to make clean steel for both the old and new anneal furnaces. Vendor 1 was right behind with the same capability in the old furnaces, but barely capable in the new anneal furnaces. Vendor 3 was not capable due to higher variability and two old anneal furnace coils that were out of spec. The first Vendor 4 formulation was not capable of meeting the specification limit in old or new anneal furnaces. The second formulation was better for the old anneal furnace but was even worse than baseline for the new anneal furnace coils.

Below is part of the output from the ANOVA to determine if there was a significant shift in the total carbon (higher means are dirtier). The analysis showed that the total carbon for the old anneal type is statistically the same regardless of lubricant, and that the new furnace type was always dirtier, but that vendors 1, 2, and 3 were all statistically different than the baseline animal fat with the new anneal furnaces and that both of vendor 4’s formulations were even dirtier than the baseline. This general pattern was found with the surface iron as well.

Lubricant Graph

Capability to Meet Specifications

For capability calculations, the cleanliness target is zero. The assumption of one of the capability measures (Cpm) is that a deviation from the target accumulates cost, so the minimum cost is at perfect cleanliness.

To be able to assess the ability of a process to produce product in the future, you must have statistical control (predictability). The small sample sizes in each combination of factors does not allow you to assess control, or is indeed out of control. However, you can assess the potential capability based on the data accumulated. Potential capability merely tells you where the cleanliness ended up and does not predict where it will go in the future. However, if the potential capability indicates that the lubricant is not capable, it is probably a good bet that it will continue not to be. Conversely, if a lubricant has a good capability to meet the specification, unless something changes from where it was during the study, you will likely see similar, but slightly lower performance in the future.

The ability for a process to meet the specifications is commonly measured in three ways:

  • Process variation compared to the specification range (not location) - Cp
  • Process ability to make individual units within the specification limits - Cpk
  • Process variability around target - Cpm

If your capability index equals 1, then you expect to make 99.73% of your product within the specification. Usually in industry you want a little insurance against falling out of spec if the process shifts a bit, so the client decided on the common capability target of 1.33, or 99.9937% within spec. Bigger capability numbers indicate better ability to meet thespecification. If you have a Cpk of 1.33, your average cleanliness can shift by one standard deviation and you will still make at least 99.73% of your product within specification. By changing how you calculate the standard deviation and replacing the C with a P (for potential) , you can assess the process as it has been performing without making assumptions about control or distributional shape. The trade-off is that you can only see where you have been and may not be accurately predicting the future capability.

We determined the potential capability of each lubricant stratified (separated) by lubricant and anneal furnace type. The results are summarized in the table below. Ppk is the potential capability of the designated furnace type to make steel cleaner than the upper spec limit given the two furnace practices and the lubricant. Ppk is the capability metric that should be 1.33 or more for this study. Ppm is the capability to hit target, and the bigger the number, the cleaner the steel. Pp (stream) is what the capability could be if it was on target and the two different furnace practices were made to give you the same average. Cp is the capability you could achieve by eliminating out-of-control points in addition to eliminating differences between practices and getting the average on target.

Process Ppk Ppm Pp (stream) Cp # Out / # Tested
Old Furnace Baseline (Animal Fat) 0.92 0.66 0.97 1.08 4 / 436
New Furnace Baseline (Animal Fat) 0.59 0.54 0.75 1.78 3 / 34
Old Furnace Vendor 1 1.32 0.68 1.38 1.8 0 / 66
New Furnace Vendor 1 1.03 0.56 1 1.05 0 / 22
Old Furnace Vendor 2 1.32 0.78 1.46 2.09 0 / 94
New Furnace Vendor 2 1.84 0.75 2.11 2.11 0 / 14
Old Furnace Vendor 3 0.96 0.71 0.97 1.63 2 / 94
New Furnace Vendor 3 1.02 0.63 1.03 1.32 0 / 20
Old Furnace Vendor 4 Formula 1 0.42 0.51 0.46 1.14 7 / 78
New Furnace Vendor 4 Formula 1 0.17 0.35 0.16 0.19 2 / 9
Old Furnace Vendor 4 Formula 2 1.15 0.59 1.5 1.81 0 / 7
New Furnace Vendor 4 Formula 2 ~ 0 0.26 ~ 0 0.19 7 / 10

End Results

Based on the above results, Vendor 4 (the favorite due to low price and a well-known name) was eliminated from the running. Vendor 3, although marginally capable, was also eliminated, since over a long period of time one would expect to see too many coils go outside of the cleanliness limits. Vendor 2 had the highest cleanliness but did not become the supplier. Vendor 1 was chosen based on a cost-analysis, since its product was significantly less expensive than Vendor 2 and the volume through the hydrogen furnaces was small.

The client estimated that the total increase in downstream defects caused by poor cleanliness had Vendor 4 been chosen would have added up to $70 million a year due to direct scrapping costs. No estimate was made as to the cost of lost customers or missed delivery times the increased incidents of this major process excursion would have caused.

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