Infosys’ blog on industry solutions, trends, business process transformation and global implementation in Oracle.

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April 28, 2009

Product Data Integration Challenge: Structured Vs Unstructured Data

The two key components of Master Data Management (MDM) are Product Data Management and Customer Data Management.  The customer data consists of mostly structured data like Name, Address etc., whereas product data is highly unstructured. Product Data will have unstructured data like CAD Drawings, Specification Sheets, Images etc.  While creating a product master data in a MDM system – you need to migrate product information from multiple disparate systems into your MDM system. Integration of unstructured product data during migration throws lot of challenges.

Usually the data needs to migrated into the master are maintained in different unconnected sources. Each department maintains and updated data relevant for them & fails to update the data not maintained by them. This often results in disparate data between systems.

In recent times, companies realize the need for a centralized blended record that acts as a single source of truth for their customers and products to improve their profitability and enable cross selling. This need is addressed by Master Data Management (MDM) tools. But the problem still lies in the integration of data from different legacy systems into one common MDM Data Hub. There is a need to check the quality of data from disparate systems, eliminate duplicates and blend the data to have a clean data that acts as the single source of truth and complete in all aspects. Also companies need to integrate record volumes of data in the shortest possible time.

 The traditional tools available for Data Integration (DI) – for mass volume & complex integration scenarios, and Data Quality (DQ) will work for customer data & not for product data. The reasons being; product data is not as predictable as customer data and product data is loaded with highly unstructured data. Some of the product specific data could be mentioned in their own jargon which cannot be understood by others. Also most of product MDM tools use ‘pattern’ based recognition of Inbound data, which again will be useful only for structured data.

The solution for handling unstructured data is addressed by ‘semantic’ recognition of Inbound data (i.e.) the tool should focus on the meaning not the patterns. The system should understand the variations in word-order, punctuation, spelling and character-level parsing. Also the system should continuously learn on the fly to develop its intelligence as it migrates more and more data.

One such system that is recommended by Oracle for ensuring Data Quality of Inbound data into its Product Information System is, Silvercreek’s DataLensTM system. It can map incoming product data into internal product catalog, using ‘natural language processing’ to automatically categorize products and their attributes. For companies with huge product catalogs, such as ecommerce, manufacturing, retail, CPG etc., automation of Product Data integration enables users to process thousands of records of product data with accuracy and minimal human intervention.

April 17, 2009

Retail Order Management - Emerging Trends

Economists continue to come up with new theories around recovery, some talk about an L shaped recovery and off late some are suggesting a V shaped one. One thing that is emerging clearly in this economic climate is the distinct presence of Internet in Retail Sales. The traditional mall based sales in US have seen a decline in the recent years and e-commerce a catch phrase of the 90s is begining to show its effect on the Retail Sales. I focus on some interesting emerging trends in Order Management seen across Retailers who are mixing the power of the internet with the spread of their physical stores.

Consumers have increasingly taken to internet for their discretionary spending and with Retailers putting a lot of dollars into integrating online channels with their store management systems, there is an emergance of some new order management trends.

1. Channel Integration

Traditional model of merchandise delivery has been through distributor warehouses or delivery centers closer to the mall based sales channels. With an increasing presence in online sales, the traditional model saw some challenges as this required presence across a wider geography. Some retailers served the internet sales separately and that inventory was maintained differently. However with the net sales showing increasing growth in the online sector, retailers are integrating the delivery channels.

2. New Sales and Delivery Channels - Buy Online, pick or hold at the physical store, Kiosks at Store

The above integration is also giving rise to lot of new innovating concepts such as Buy Online and pick up at store which utilises the sales channel of internet and the delivery channel of a traditional store delivery. Also lot of retailers are giving increasing visibility into store level inventory which allows customers to buy online and hold the product at a local store for a pick up thereby guaranteeing availability. Retailers are also bringing kiosks into the stores, this way a customer who missed out in a item within the store can purchase it at the store thereby guaranteeing the sale and increasing availability.

3. Direct to Store Delivery

Another concept that is gaining popularity is the direct to store delivery model, which offers vendors to take their items directly to the store and by limiting the number of intermediate delivery centers and warehouses. Unlike the previous two trends, this trend requires physical warehouse capabilities of being able to fulfill requirements directly to store and thereby handle the size of deliveries.

Indeed the true power of internet is only now catching up and revolutioning the retail world.

Procure to Pay for Process Industries

Procure to Pay is a standard business flow in any Organization. In Manufacturing Sector, It is a typical business requirement to pay supplier based on goods finally delivered after inspection. All ERP Systems handles this requirement. This serves Discrete Manufacturer pretty well. But, what about the Process Manufacturer, does it serve well for them too? Let’s see.


One of the major challenges, Process manufacturer face, is handling inherent variability in raw material. The raw material which generally sourced from organic sources varies from lot to lot. In Process Industries, it is common to base the price of material on various technical parameters. Since, these parameters known only after the quality department’s testing, it is really hard to decide in advance the price of material that supplier is going to supply in future. So how does Procure to Pay work in Process industries?


Following is Procure to Pay business process in typical process industry:


  1. Company enters into purchase agreement with supplier. In this agreement, a base price is mutually agreed, which is based on standard values of typical technical parameters applicable to that material.

  2. Supplier supplies the material against this purchase order.

  3. After receipt, Quality Department tests the material, and provides actual value of technical parameters.

  4. Based on standard parameter value in Agreement & actual parameter value, a differential amount is arrived at, after some calculation.

  5. Company applies this differential amount to agreed PO Price and pay the net amount to supplier.

How is it different from Discrete Manufacturing?

  1. It is not just the quantity which affects the Invoice value !

  2. The calculation for differential amount may vary from Industry to Industry, Company to Company & even Supplier to Supplier !!

  3. Generation of Invoice Price Variance (IPV) will become more of routine rather then specific case. In General, IPV point to specific cases and require analysis. Process Manufacturers usually don’t want those IPV which are result of difference in quality from agreed quality in Agreement, as it is considered part of normal business activity.

Typical challenges faced by Implementer in Procure to Pay Cycle for Process Industry are:

  1. Capturing of Technical Parameter Value at the time of Receipt. This requires integration of Receiving module with Quality Module with appropriate real time notifications.

  2. Calculation of Amount to be deducted due to quality differences while paying to supplier

  3. If possible, avoid the IPV generation due to above.

As we can see, Procure-to-Pay Business flow which was fitting so well for Discrete Manufacturer, now looking more complicated for Process Industries. When evaluating any package, we advise process manufacturer to just don’t go by the Vendor Claims or Research Findings, as most of them are done on discrete manufacturing. They would do well by asking vendor about Procure to Pay process mapping in the system beforehand.






April 13, 2009

Shared Service Process in Payable- Road Towards continuous Improvements

IT Shared service offer the potential for significant cost savings through economies of scale,process improvements and standardization. Shared services can help companies reduce costs through greater efficiency

In Oracle world, using shared service process coupled with new features of release 12i, organization can now increase their flexibility and responsiveness to market especially by automating processes and streamlining administrative functions apart from reducing overall costs

Lets explore now, what road Oracle has provided to help organizations to move towards continuous improvements and reduce overall cost in current economic meltdown.

 In typical scenario of accounts payables implementation,earlier organizations used to find following roadblocks during implementation of shared service process 

  • Multiple Roles and Responsibilities
  • Less Automation in Accounting Entries
  • No centralized tax engine
  • Integration of Master Data Management
  • Complicated Reporting
  • Less Control

To address all this roadblocks, Oracle has come with various features in Release 12i to help organizations to further enhance its efficiency and effectiveness in shared service process. Some of key features are

  • Multi Org Access Contro (MOAC) features leading to Improvement in Transactions Processing and Control due to less roles and responsibilities
  • Sub Ledger Accounting (SLA) is a Rule based accounting features leading to more accuracy and control. It helps to generate accurate accounting entry based on data input without any customization and users intervention
  • Third Party Architecture helps in integrating various master data management across differemt systems
  • Centralized tax engine providing more automation in defaulting tax code based on various logics and ensuring error-free transaction as no direct users involvement needed.

In shared service world all above features will further translate into process improvements, better control, enhanced effectiveness and efficiency leading to overall cost reductions  

Illustration has been provided below to further eloborate on how new features will increase efficiency and effectiveness of shared service process 

Before 12i, Users has to navigate in different responsibilities to enter invoices meant for different companies/operating unit. For e.g. to enter invoice for Operating Unit A, he has to login into "Payable Manager Operating Unit A" and to enter invoice for operating Unit B, he has to login into "Payables Manager Operating Unit B". Using MOAC, now users at shared service center are no longer required to switch responsibilities. Based on access provided to him, within same responsibilities, he can enter invoices meant for various operating unit thus leading to improvements in transactions processing and better control.

Earlier, before 12i, there were limitations to have multiple liability accounts or to default accounts based on various parameters etc. Hence in shared service scenario, users has to remember what company and what relevant accounts to choose. Now using SLA, this accounting is fully automated. In SLA accounts are derived based on rules engine giving more control and accuracy in process. In SLA based system, user do not have to worry about which Org/Company to choose, what natural account to select. All this is automated by defining derivation rule using different permutation and combinations of data available in system. Now, users have to just key in invoice amount and distributions and everything else is taken care by system.



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