In the B2B context, where there are multiple pieces of equipment the issue is rather different. Maintaining a record of the equipment, in a mine or a constantly changing fleet is one task. Trying to ensure that the minimum number of products are used, and at the right time is another. Increasingly dynamic monitoring systems may be used. But these are not always available and there is a need for systems to capture the equipment, whether handheld devices (survey phone-app?) or some other means.
Making the right products Another aspect of perfection is which products to make in the first place? Ideally one product would do absolutely everything! Well we know that doesn’t work. So perfection is the minimum number of products which could service that market or group of machinery.
This assumes that the Marketer knows the type of equipment in the market, the installed base or Parc, plus the recommendations for those pieces of equipment. Integrating different sources of data is a real challenge as the data is frequently inconsistent in structure and classification.
Example: evaluation of Car Parc in a European country An organisation wants to know what is the demand for a specific Quality-Viscosity combination. Is it justified to create a new product? Or could another product be ‘stretched’ to meet the requirement? Perfection would be to ‘prove’ that demand exists with accuracy and confidence.
Then tying this up with the specifications that are required you can estimate the market size. Based on the number of vehicles of a specific type, with an oil change once a year and known sump size it is practical to calculate the market size by OEM, Model and even engine displacement.
Using just this sample of Fords, looking at the requirements for a high end 5W-20 and a 5W-30, an Oil Co can see that on this sample the demand is 6.9m litres per year. With the 5W-20 alone, this sample showed demand falling to 0.4m litres. But could that demand be met with a 5W-30? If so does it makes sense to stock both viscosity products? The tunes that a keen researcher can play are endless.
Drowning in Data
In the early 1800s Thomas Beaufort realised that the British Navy had extra-ordinary records of the weather. 1000 ships with each captain having to record details several times a day. But to make it useful it had to codified. Beaufort created the Beaufort scale for classifying winds and a notation that allowed those records to be standardised. They could then be analysed and used as a basis for prediction. This relied on Constable’s depiction and Howard’s classifications of clouds.
Many Lube companies have the same issue. They have millions of oil analyses, but no real way of combining, analysing the usage that allows them to combine experience.
In future the real advantage will be gained by those organisations that can build integrated systems that permit effective analysis of their internal and externally generated data. By combining oil analyses with the actual equipment performance, organisations can identify real lubricant performance which may allow them to extend drain intervals well beyond ‘the recommendation’ but which may be fully supported by real performance data.
The problem is that Parc data is often collected on a different basis in different countries; also vehicle data can be too fine in detail to allow the equipment databases to be aligned easily. Using data management techniques a reasonably accurate assessment of total market demand for say a 0W-20 engine oil can be determined, or could it be covered by a 5W-30?
Illustrating the example with selected Ford models from the UK Parc.
Estimated Annual Engine Oil Requirement in UK for selected Ford vehicles
Having the facility to process this extensive data and draw reliable conclusions will be really powerful. Furthermore, having identified best practice in one area to be able to re-apply that for other customers with similar equipment profiles. A mine in one region may have similar equipment to a mine in another region. By drawing together the oil analyses from different areas, real advantage can be created which could lead to significant customer cost savings.
To do this again requires consistency of data. Often the equipment data is captured in the manner that makes sense to the local organisation. What is needed is the ability to combine the disparate data structures in a way that allows the patterns to become visible. The skills of ‘fuzzy logic’. This really will deliver advantage or is it perfection?!
For a global business this can mean trying to do all of the above, in a uniform way so that the customer experience is the same in all markets!
Sebastian Crawshaw Chairman OATS Ltd
media@oats.co.uk
LINK
www.oats.co.uk/en/contact-oats
LUBE MAGAZINE NO.128 AUGUST 2015
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