The Infosys global supply chain management blog enables leaner supply chains through process and IT related interventions. Discuss the latest trends and solutions across the supply chain management landscape.

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March 30, 2011

Memory Centric Business Analysis and Supply Chain Visibility: One Complementing the Other

Growing data analysis need of business users was evident when a super user expressed the desire to generate an operational report which will not only help him to know how many units of a certain product did a business unit sell in a retail store in Irving, Texas but would also state how much revenue did that product generate during the last four months broken down by individual months, in the south west territory by individual stores, broken down by promotions and demographics, compared to estimates (previous forecast) and compared to the sales of a previous version of a similar product.

The above user request clearly validated that today's decision managers must be able to analyze data along any number of business dimensions, at any level of aggregation, with the capability of viewing results in a variety of ways. They must have the ability to drill down and roll up along the hierarchies of multiple business dimensions.

 

Memory-centric data management and OLAP (online analytical processing) technologies and tools are the answer. With the cost of memory dropping and speed increasing, the in-memory computing and analytics platform is an efficient way which has helped users carry out complex analysis on enterprise wide data by consolidating, and editing enormous volumes of data across different operational and financial measures.

Memory-centric technologies permits real-time and multidimensional OLAP analysis, with much more speed and computational flexibility as opposed to disk-centric technologies, whose performance is more like "come back after lunch".

The concepts of hyper-cubes and multidimensional domain structure (MDS) presents a special method for representing a data model with more than three conventional business dimensions i.e. product, time and geography. The MDS also is the technological back bone for memory-centric solutions offered in the market place today such as SAP's BI Accelerator/Net Weaver & IBM's Memory-Centric OLAP Platform: COGNOS/TM1-Applix.

These technologies has not only helped users expedite the process of analyzing the voluminous data across product lines, geography, time, demographics, promotions and other business metrics such as direct sales, profit margin, cost of goods sold but also has helped them make key business decisions such as to change their build and buy plans, modify financial and unit forecast and re-position a product inventory to achieve a sell through in another market segment in another part of the world by quickly making an intra-company transfer.

Running multiple cascading what-if simulations, carrying out scenario planning, customer analytics, profitability analysis in a sequence of analysis session using OLAP cubes has helped users accurately and timely gauge the performance of the key metrics of the business and make tactical and strategic decisions, thereby improving visibility across the value chain of a global organization.


 

March 4, 2011

Reminders of Asset Management Challenges in the Railroad and Other Asset-Intensive Industries

Guest Post by

Bob Ferrari is the Executive Editor of the Supply Chain Matters Blog, and a periodic guest blogger on the Infosys Supply Chain Management blog.

Efficient asset management has become a far more critical need for asset-intensive businesses and service providers. The cumulative impact to U.S. and other international railroads during the recent global recession, along with changing customer service models are keen reminders to how important enterprise asset management (EAM) process management capabilities have become.

Within the U.S., the impacts of the sudden downturn in supply chain shipping activity had a rather dramatic impact on railroad assets.  As an example, industry shipments of railcars declined by 64% in 2009.  According to the Association of American Railroads, 2009 was the worst year for freight rail traffic, reaching its lowest levels since 1988.  In that same year, 28 percent of rail fleets were parked in storage, and one estimate noted that there were $43 billion in idle assets representing both leased and owned locomotives and railcars.  As an observer of global supply chain activities, I vividly recall reading news articles indicating that railroads were challenged to find all sorts of storage locations for these idle assets.  The BNSF railroad alone had rail cars that sat idle for up to three years on unused track stretching more than 30 miles in some places.  Similar reports came from other U.S. and European railroads as well.  It seemed like every available rail spur was utilized as a storage location, a virtual real estate warehouse, as it were. As observers, we speculated on how railroads were able to track and account for all of these scattered idle assets. 

Now that global economies have begun economic recovery, these same assets are being re-activated for service.  An important consideration however is the re-deployment plans and current condition of these assets.  Has subsequent damage occurred, and if so, what repairs are needed?  Have scheduled maintenance procedures lapsed or are overdue?  Have leases changed hands or have customer leased assets been reallocated?

In a past Infosys SCM posting,it was observed that EAM, traditionally viewed as an internal or regulatory function of an organization,has now become more customer service oriented with the advent of the service economy.The objective of EAM has always been to increase asset availability, but the evolving post recessionary economy adds new dimensions of procurement, service and repair efficiencies.  Assets, either physical, facility, or IT related, need to classified, tracked and prioritized as to utilization, maintenance, service requirements, probability of failure and customer service requirements. EAM has the potential to be the early warning system for managing asset and overall operational efficiency.  Integration with operational and other business systems increases efficiency and reduces lead times for scheduling. Expensive and highly utilized assets such as locomotives need special attention. Equipment warranty and claims tracking insure that organizations avoid lost opportunities, while the advent of new mobile and GPS technologies add new capabilities for real-time tracking.

Another important consideration is that capabilities included in EAM are often sensitive to specific industry needs, more so than certain other process capabilities.  While we commented on recent developments in the railroad industry, other asset-intensive industries such as utilities, energy services, retail, transportation, process or discrete manufacturing will each have their own unique EAM requirements.  Industry knowledge and experience has special meaning for EAM.

The recent incidents of scaling-down and scaling-up of fleet assets is an indication of the 'new normal' of business volatility.  Customers demand reliability flexibility and up-time, and assets must be managed to yield maximum efficiency. Effective asset management positively contributes to both customer and business responsiveness and bottom line results.

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