Attributes of champion lubricant plants in EMEA

Omer Chowdhury, Project Manager, Global Lubricants and Greases Supply Chain Benchmarking Introduction

Lubricant manufacturers worldwide are facing a multitude of challenges. Mature markets, volatile raw material prices, tariff changes, difficulty of inventory management, suboptimal productivity, poor utilisation rates of equipment, high downtime and associated production capacity loss, energy inefficiency, high maintenance spend and turnover of critical employees are amongst the growing obstacles for global majors as well as independent players. There is an increased emphasis on understanding the drivers of performance that show the largest gap to best practice.

Some companies have a policy to aim for “best quartile” performance. However, this immediately raises difficulties: a. What should be the appropriate measure of success? Should operating expenditure be the sole barometer of success, or should losses, labour productivity, asset efficiency and safety performance be considered?

b. What comparison sets to use? Should this be a country, a region or a global comparison? Should this be versus global majors, independent players or both?

c. Since labour represents the biggest portion of operating expenditure, and pay rates are location dependent, should pay rates be normalised, since a plant cannot change its location?

d. Since complexity is the biggest driver of operating expenditure after labour, and generally intrinsic to a plant’s mission, how to correct for differences in that?

e. Since scale of operation is also a key cost driver, should the performance evaluation correct for that?

f. What about other “givens” such as proximity to suppliers, order sizes, lead times, access to secondary warehouses, etc.?

Over the last 25 years, PIMS has provided lubricants and greases manufacturers with concrete recommendations

to improve their competitive position based on objective evidence. Based on our research and experience, some plants consistently and substantially outperform their regional peers. These regional champion plants have a significant cost advantage versus peers, operate substantially below their expected cost thresholds and attain a superior productivity position.

In this paper, we will explore the profile and key attributes of the champion plants in the EMEA that enable them to have the upper hand versus competition.

Overview: EMEA In the 2016-2017 cycle of the PIMS® Lubricants

and Greases Benchmarking, the average volume of finished lubricants produced by a plant in the EMEA region was 45,000 tons. The median volume in Europe has remained stable at around 47,000 tons per annum, while the Middle-East & Africa (MEA) median has increased by 5% from 2010-2011 to 42,000 tons in 2016-2017. The aggregated complexity index2


54% for the region is 4 percentage points above the global average. Whilst the median complexity for Europe sits around 56%, a median complexity of 48% for MEA brings down the aggregated average.

Profile: Champions versus regional peers The average annual throughput of the champion lubricants manufacturers in EMEA is 120,000 tons, whilst other plants in the region (the “peers”) have a significant scale disadvantage with an average throughput of 45,000 tons.

Figure 1 shows the product portfolio of the champions and peers in EMEA. 72% of the champions’ portfolio comprises motor and hydraulic oil whilst it is almost 90% for the regional peers. Although the gear, marine and hydraulic composition is aligned for the two groups, 20% of the champions’ portfolio is made up of industrial oils (predominantly simple oils with less than 6 components).

1 Mathematical model that quantifies the expected operating cost of a manufacturing plant, given its strategic profile. 2 The PIMS®

complexity index quantifies the difficulty of the job the plant is undertaking and is driven by variety. The higher the variety handled by the plants (i.e. formulations, SKUs, components, etc.) the more complex the operations. The least complex plant worldwide has a complexity index of 0% whilst the most complex one has a complexity index of 100%.


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