Continue # Infosys Whitepaper 
 or member level, reports against the  inventory booked and bill of landing (BOL)  for a vendor, etc., work both as a deterrent  to malicious activity as well as an enabler  for shrink identification.<|endoftext|>In operational analytics, reduced time to  market is the key factor. It is critical to work  out ways to cut short the requirement- to-validation cycle. It is observed that  the entities involved in the sources and  strategies of theft adapt quickly to the  new security measures, identifying ways  to break the current system. The ability  of IT to respond to this continuously  elevating challenge in relatively shorter  windows, determines the success of this  program and the ability to stay ahead of  the miscreants.  Strategic LP Analytics Strategic analytics  help evaluate the  effectiveness of the LP program as a whole.  These reports provide executive snapshots  into retail shrinkage data with drill-down  capabilities.  The objective is to empower  executive management to make informed  decisions that influence the corporate loss  prevention strategy, return on investment  (ROI), LP budget allocation and formulation  of corporate policies.<|endoftext|>Some examples of the types of reports  obtained through strategic LP analytics  are: trend analysis and comparative  analysis comparing various time periods  such as year-on-year (YoY) shrinkage  for a department, comparing the same  quarter, store YOY, region YOY, etc., types  and locations of warehouses (mall or strip),  merchandise-specific classification (such as  life-time return), data comparisons before  and after changing the LP strategy and  more.<|endoftext|>Strategic analytics enhances the  effectiveness of the overall LP strategy  against industry averages, and helps steer  the course at the executive management  level. Validation for loss prevention system  plays a vital role in ensuring quality and  reliability of data, and accurate reporting.  The level of analytics required in a specific  retail setup is determined by several  factors that are in play such as the line of  business, corporate policies, etc. Further  considerations for analytics can be cultural  diversity in the operating markets and the  climate and economies in which retailers  operate.<|endoftext|>External Document © 2018 Infosys Limited 

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 Validation for loss prevention  system Developing a comprehensive loss  prevention analytics validation strategy  early in the development cycle is critical  to the execution of a successful loss  prevention program. The validation  strategy is defined based on the corporate  loss prevention strategy, the data  warehouse design, key performance  indicators (KPIs), and the business  intelligence reports identified for loss  prevention.  The analytical methods used for loss  prevention need to be qualified and  validated. The key process indicators,  as part of the analytical methods, are  identified and the exact business rules  to be validated are defined. The key  process indicators included in the test  strategy must be examined to establish  threshold values based on historical and  real-time data for operational and strategic  reporting.  Validation for loss prevention system  helps the retailer not only to address their  current issues but also predict any issues  that may arise in the future. Therefore,  decision-making must be open to adopting  different validation techniques for  operational and strategic loss prevention  analytics.<|endoftext|>Strategic LP Analytics and Validation The major strategic challenge in creating  a permanent loss prevention analytical  platform is the uncertainty involved in  the retail industry. Retailers continue  to improve their LP program to remain  competitive and protect their brand.  However, this implies that until the LP  analytical platform is implemented and  the Business Intelligence (BI) reports and  KPIs are in place, the retailer is unable to  confirm the accuracy of the analytical data  to help the company meet its long-term  profit and growth plans.<|endoftext|>Strategic validation for loss prevention  system must focus on the following factors: • Integration of BI with loss prevention  techniques • Design of a scalable Enterprise Data  Warehouse (EDW) and Supply Chain  Integration Operational LP Analytics and  Validation The constrained global economy has led to  reduced operating budgets. Retailers are  today seeking immediate business benefits  from monitoring and analyzing loss  prevention data.  The focus is on tapping  into the large amount of high-quality  cross-functional operational data and  uncovering suspicious activities. This helps  retailers discover problems pertaining  to fraud or retail shrinkage and extract  actionable insights.<|endoftext|>The operational validation for loss  prevention systems needs to focus on the  following factors: • Reduced time to market for the  implementation of LP program through  automated validation  • Continuous integration validation of  incremental features in the LP program  through reusable regression suites Retailers across the world are using BI to  strengthen their loss prevention systems.  Let us see how a pharmacy chain in North  America used loss prevention QA solutions  to fight retail shrinkage.<|endoftext|>External Document © 2018 Infosys Limited 

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 Case study  Implementing Integrated EDW  and BI Reporting for Effective Loss  Prevention Customer: A leading pharmacy chain in  North America decided to replace the  existing semi-automated loss prevention  system with an integrated EDW and BI  reporting solution  to enhance their LP  data analysis and reporting capabilities.  The end-user group was the loss  prevention finance department.<|endoftext|>Business Context: The existing system  was based on fragmented data sources  and relied on multiple satellite databases   or flat files with multiple hops/layers. This  involved significant manual intervention  and effort in data extraction, cleansing,  transformation, and report generation. The  inventory of reports was extremely large  and redundant. It provided merely different  views of the same data, and lacked drill- down and drill-across capabilities. The  existing system failed to track action items  on the basis of data reported.  Analysis of the ‘as-is’ state of the system  revealed the opportunity to consolidate  the existing inventory of reports to one- third of the current size. This would allow  the company to reduce the maintenance  costs for the existing reports and business  process refactoring.  The new system needed to be based on a  platform with EDW as the back-end and  Oracle Business Intelligence Enterprise  Edition (OBIEE) as the front-end. The future- state solution envisaged robust analytics  and reporting to increase data visibility and  support enterprise level BI goals for loss  prevention.  Loss Prevention QA Solution: The loss  prevention QA strategy was defined  considering the nature of the project and  in-line with the overall loss prevention  strategy for the customer. The QA team  was engaged in stages ranging from  requirements and design validation to  data source consolidation and retirement  strategy definition. This helped identify the  high risk areas and build a risk-based test  approach with optimal time to market.<|endoftext|>An automated test approach was adopted  for data warehouse (DW) testing using  proprietary in-house solution accelerators  for Extract Transform and Load (ETL) testing   which greatly reduced the regression test  effort. This was the key differentiator for  ensuring test coverage and reducing the  time to market. Data quality testing, system  scalability and failure recovery testing were  performed to ensure that the DW solution  was sufficiently robust to accommodate  future enhancements as well.<|endoftext|>BI report testing was conducted based on  the KPIs/threshold identified for the loss  prevention strategy. The QA strategy called  for automated testing using an in-house  open source-based tool that reduced the  execution time for test cycles and formed  the foundation for the regression pack. This  enabled the regression for future releases  to be conducted in comparatively smaller  timeframes.<|endoftext|>Enforcement of role-based access to data  and data restriction by user on a need- to-know basis were ensured by security  testing.  Overall, the loss prevention QA solution   enhanced the test coverage to almost  100%. This enabled early detection of loss  and ability to prevent it, thereby saving  an additional $1.5 million every year. The  annual maintenance and support costs  were reduced by $60,000.<|endoftext|>External Document © 2018 Infosys Limited 

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 End notes 1 National Retail Security Report 2011 by Richard C Hollinger and Amanda Adams Conclusion In the current market scenario the ability  to optimize profitability by reclaiming  leaked revenue allows retailers a greater  opportunity to enhance profits. Today  the ability to reduce retail shrinkage is  more critical than ever. However, the  ‘one-size-fits-all’ strategy does not work  in loss prevention. Loss prevention  programs need to be tailored to address  the challenges of the retail category or  department based on the impact and  sources of loss. The ability to predict retail  shrinkage proactively, identify it and  react as soon as anomalies surface is the  key determining factor for the success of  the loss prevention program. IT, the key  enabler for this business objective, plays a  central role in empowering business with  innovative solutions and reduced time to  market. Automation, innovation and re-use  are the three pillars to achieve effective  loss prevention.<|endoftext|>External Document © 2018 Infos