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APL: Report Mining Tool Assembles Costs for More Than 15,000 Container Routes

Published 18 May 2010 | By Datawatch Europe

Of the software applications available for extracting information from the reports generated by the distribution PC systems, one had a proven track record within APL. Monarch from Datawatch was already in use in the company, analysing data from the reports of the accounting systems. The ability to collate information from multiple sources of data is an inherent feature of a report mining tool like Monarch...

Politicians would have us believe that their supreme diplomatic skills lie behind the steady rise in cross-border trade and communications in recent decades. That claim is valid only in part: the principal driver, once any legal barriers have been removed, is the acceptance of international standards. The movement of goods in containers across borders is an excellent example of how that process has been turned to good effect.

It highlights an environment in which standards make it possible to move raw materials and manufactured items cost-effectively across continents on journeys which may involve ships, trains, aircraft, rail, road and barge in varying proportions.

It is just over half a century since the modern container industry evolved, with its standardised dimensions and lifting arrangements. Whether in transit or storage, containers are guaranteed to interface compactly with whatever mode of transport is involved at each stage along its journey. That standardisation increases the efficiency of handling, simplifies transfers between transport modes and increases the degree of automation that is possible. Overheads are reduced in the process, and transport costs become a smaller component in the total value adding chain.

Many logistics companies have made container traffic their principal revenue stream; expertly manipulating standardised container units between the different modes of transport as they move from point to point around the world.

A single container could easily start its journey by road from a factory in Cambodia, to complete that journey – again by road – at a clothing warehouse in the West Midlands. Along the way it will have encountered several rail transfers and at least one sea passage.

Standardisation, as this example of containerisation demonstrates, reduces the transport of goods to something near a level playing field. Competition between logistics companies comes down to price and service.

Knowing the true costs involved at every stage of a journey from Belgrade to Belfast, for example, is the key to one supplier being able to move the same weight of goods in an identical container 10% more cheaply than another while making 15&37; greater profit.

In practice, producing a winning quote for any assignment is a case of accessing accurate, consistent data on costs at each of the stages of a journey and drawing this seamlessly into a single figure from which an acceptable price can be calculated for the customer.

Transportation company

That was the challenge facing the European arm of APL, the container shipping brand of Neptune Orient Lines, a Singapore-based global container shipping, terminals and logistics group which operates a fleet of 124 vessels.

Handling intermodal transport on 15,000 European routes, APL has had to draw together strands of information from an array of spreadsheet 'applications' running on PCs across Europe.

Each system had been built from the ground up as a series of Microsoft Excel macros to reflect local operating conditions, taxes, tolls, surcharges and repositioning costs. No less significant, each of those PC-based systems is supported with information from the company's mainframe based in the US.

According to Sue Evans, the project manager responsible for this challenging task in the UK, Ireland and Scandinavia, integration of data on a pan-European scale could never be a case simply of aggregating the output from these localised resources. "Until we could establish a method of extracting information from those systems to a common set of criteria, the result would have told us very little about the true costs of moving a consignment. Practices necessarily vary from point to point within Europe to reflect how containers are re-located after an assignment, so that we needed to be able to bring the costing process first to a common denominator."

The task would involve extracting or mining information from the output of the local PCbased systems, and drawing in key parameters from the business intelligence (BI) system handling output from the mainframe. The resulting collation would need to be flexible enough to provide the basis on which prices to a customer could be quoted for any journey within Europe, no matter how convoluted the transport practices of the countries through which the container would pass.

Experience of Datawatch Monarch

Of the software applications available for extracting information from the reports generated by the distributed PC systems, one had a track record within APL. The Monarch system from Datawatch was already in use in the company, analysing data from the reports of the accounting systems. Satisfactory experience of the product made it a logical choice for adoption in July 2007 for the intermodal costing system.

Criterion for selection

Sue Evans took up the story. "The reason for selecting a report mining tool was to co-ordinate all the streams of data into a manageable structure while retaining the flexibility for each country to continue with its own costing systems based on local circumstances. A great deal of work had gone into understanding and reflecting local conditions, and Monarch allowed these customised applications to be retained while delivering information that would allow accurate pricing to be achieved for journeys end-to-end across the region.

Once we had established a standard costing framework to which those local factors could be applied, we had the ability to draw down the information and integrate it though Monarch.The complete task has evolved as a set of more than forty separate programme projects which now reflect all of the possible intermodal combinations on which the company could be asked to quote a price in Europe. It was important that we could incorporate in these prices factors such as increases in fuel costs and pricing policy."

Multiple applications of Monarch

The ability to collate information from multiple sources of data is an inherent feature of a report mining tool like Monarch. It has already found applications within APL apart from Accounts and intermodal container costing. Evans again, "We needed to analyse the cost of the mobile phone services contracted to the company. Once the data queries had been created in Monarch, information was extracted from usage records so that we can now determine the cost of calls placed by individual users to a very high level of detail. This illustrates the flexibility of the system: provided that the data exists somewhere within APL, we can use Monarch to extract it and undertake whatever additional processing may be required 'on-the-fly."

Another feature of Monarch means that the system could play a major role in a further project under review at APL. A proposed finance project would see it importing data from PDF copies of invoices, where virtually every location has been accustomed to displaying data in a different format.

"Monarch would allow us to extract the data we required without having to redesign the invoice format in each country. Once configured to recognise where the pertinent data lies in a given invoice format, the process is automated and the data absorbed automatically."

Too specialised a tool?

It could be argued that Monarch, as applied to the complex analysis of intermodal transport costing, is just another tool in the IT specialist's box. The costing model may reflect operating costs and conditions today, but it would surely require additional input by the professionals if there were to be any changes in fuel costs, for example, or in the tariff structure affecting transport services in one of the countries.

Practice within APL would indicate that this is not the case. With the report mining 'infrastructure' created in the first instance by Sue Evans and her colleagues, authorised users of the system are free to make changes to the routines which drive the Monarch application. "Anyone who can create a calculation in Excel can write the routines which source and compute the information in Monarch. It means that requests from users are not held in a queue waiting for the IT department to respond – as they would be with a conventional business intelligence product."

Major savings achieved

"There has been a reduction of around 90% in the man hours needed to maintain the system. Changes can be made instantly so the business is always able to run with the most current costs and build these into prices. We believe that this responsiveness gives the company a real competitive advantage."

The team at APL is clearly of the view that Monarch is the appropriate information mining tool for the business applications at the heart of its operations. But that company is a multi-national business operating in every European country: could the same tool have such an effective role in a much smaller organisation? Nothing about the application in question would appear to limit Monarch to a given set of circumstances. So long as there is a requirement to extract data from the reports of one or more systems, and convert this information into a useroriented document, then the benefits which APL has been securing are there to be derived.