Good data for e-commerce – some new ideas
Feature Articles, May  29  2009 (Digital Energy Journal)

- OFS Portal, the e-commerce service for oil and gas, gives some new ideas about how to make e-commerce work – and make sure your data is good enough

Looking at companies that have capitalised on e-commerce it is clear that they have invested on getting the processes right as well as managing the master data.

By Paul Mayer of Eiro Consulting, writing on behalf of oil and gas e-commerce thought leaders, OFS Portal

The vast majority of master data management activity is focussed on the quality of information that flows within a company. This internal attention, whilst essential, may fail to recognise the opportunities and risks associated with the exposure of and to external data.

The quality of information that flows between trading parties directly affects the confidence in products, services and value for money.

If the information provided is wrong or misleading the operational and financial implications can be devastating.

Meanwhile organisations need to consider strategies that can handle data volumes that double every 18 to 36 months. Working with the rapidly increasing volumes of data increases the need for the data to be high quality.

Approaches to master data quality management

Fit for purpose

In order to realise all the benefits of e-commerce data needs to be fit for purpose.

An engineer’s data requirement is quite different from procurement professionals.

A cohesive data strategy is required to fulfil the different needs across the organisation and supply chain.

Getting data to work effectively for you is a balancing act. The need for quality and detail must be assessed against the costs, risks and benefits.

Getting the data to align, and stay aligned so that each element drops into place seamlessly is often more of a challenge than anticipated.

A clear understanding of the processes involved allows the flow of Master Data to be mapped as it traverses the supply chain.

E-commerce

E-commerce has the ability to impact so many business processes; these benefits will be multiplied with high quality data.

For example: optimisation of the sales process, inventory visibility, operation tracking, project management, seismic and survey data, service levels and making documentation and drawings rapidly available.

One of the much anticipated steps in E-Commerce is the ability to set up purchasing of goods and service from an electronic smorgasbord. Indeed much of the talk about E-Commerce and Business to Business (B2B) exchanges post Y2K was exactly this scenario.

The difficulties experienced in doing this were underestimated and thus far the majority of success has been with simple to purchase commodity items like Maintenance Repair and Operations (MRO) items.

Goods and services in the upstream oil and gas arena are often specialised. This increases the importance of providing clear and specific information in order to differentiate products.

Providing the appropriate level of information in a digestible format is the key consideration when setting the most appropriate master data management strategy.

One of the realities recognised early in the evolution of e-commerce is that one solution does not fit all scenarios. Some vendors are niche and supply to a sole vertical market sector, for example: drilling fluid suppliers. Whilst other vendors are cross sector and provide products that are used widely, for example, bearings.

This relativity is a factor to be considered when designing the data strategy. It is, for example, unrealistic to think that the oil industry is going to have any impact on the way that V-belts are described.

Many of the vertical markets have subscribed to the concept of using a standardised method for the transmission of data. However gaining agreement on the depth and detail of content being exchanged is a more complex matter and has far reaching implications on how data is to be managed.

Industry standards

A sustainable approach to ensuring data can be used by different departments is the adoption of industry standards. This approach aligns data from different companies so that it can be used more effectively.

It lets organisations focus on the content and quality of the information and not worry about the format, taxonomy and classification being employed.

This strategy is being embraced by many operators who are using the standard to describe their material master data and suppliers who are using it to describe their products. Data will be provided to buyers by suppliers using the same PIDX classification standards.

Therefore when the buyer purchases a product or service they can automatically receive the information electronically with the associated metadata. This may include the attributed descriptions used to describe the item in the material master data and the relevant photographs, drawings, manuals and technical documents that can be made available as attachments or hyperlinks.

An additional benefit associated with standards is that of classification hierarchies. These are available pre-mapped and standardised .

The most widely adopted classification hierarchy in upstream oil and gas industry is the United Nations Standard Products and Services Code , commonly abbreviated to UNSPSC.

The allocation of a standardised classification code to products and services allows similar items to be grouped together for many purposes including: spend analysis, business intelligence, inventory reporting and supplier identification.

The UNSPSC system has a 4 level hierarchy and has been designed to allow everything and anything to be classified.

Each segment, family, class and commodity is given a two digit code. (See below) There are 2 segments that were developed specifically for the oil and gas industry. Segment 20 Mining and Well Drilling Machinery and Accessories and segment 71 Mining and oil and gas services.

Any item starting with 2012 falls within the segment “Mining and Well Drilling Machinery and Accessories” and family “Oil and gas drilling and exploration equipment” This allows items to be grouped together which is particularly useful when reporting.

Below the hierarchy there may be several Noun Modifier pairs. Each pair has a set of attributes allocated to be captured to describe the item as seen below.

Connecting suppliers to buyers

To realize the full benefits the data should flow to where it is required.

Typically sellers offer a large number of products and or services to their assorted customers. Each buyer is unlikely to be in a position to purchase the entire range on offer and therefore will need to set up agreements with the seller to specify the range, terms and price. This may then be distilled into a buy side catalogue.

If and when a purchase is made only the items that are purchased are created in the buyers Enterprise resource planning (ERP) application.

The ideal scenario is one where the relevant master data drops into the appropriate location as a buyer selects an item from a catalogue and places an order. This streamlined process facilitates the rapid identification of items across the supply chain as well as reducing errors that might otherwise be introduced by manual data entry.

The crux of the matter is understanding which data is relevant - deciding which data to use and adopt and what to discard.

In some industries the sellers’ descriptive data is simply mirrored in the buyers’ systems. Whilst this method is elegant in its simplicity it just does not hold up as a viable methodology to be applied, universally, in the oil and gas industry.

There are many reasons why this approach does not work including:

Commodity items are purchased from multiple sources and need generic meta data to specify them

Specialised items are often highly configurable and may be uniquely configured each time they are purchased. These items are unlikely to be catalogue purchases however it is essential that they are accurately described.

Single source items need to be classified and described to match the basic data requirements of the purchasing organisation.

Taxonomy and Nomenclature differ from company to company in order to avoid double standards of data within an organisation. Standardisation and normalisation is required, this in turn should enable standardised classification to be achieved.

The lesson that has been learnt through experience is clear. A thorough analysis of the data is needed right at the start of the planning process. If the in-house experience to do the analysis is not available then find people that do have the experience to help guide the organization through the process.

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Written by Paul Mayer of Eiro Consulting, writing on behalf of oil and gas e-commerce thought leaders, OFS Portal.

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