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Data Cleansing for Efficient Asset Management and Maximum ERP Functionality

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One of the most common challenges for manufacturing and asset-intensive organizations lies within their material master data and asset management process. In this White Paper, you will learn how MRO Data Cleansing can transform the asset management process, while maximizing ERP functionality.

ABOUT IMA LTD.
IMA Ltd. is an industry-leading Material Master Data Management solutions provider, specializing in Data Cleansing, Governance, Inventory Optimization, and Spend Analysis services. Since 1989, IMA Ltd. has supported manufacturing and asset-intensive organizations worldwide to improve data quality and reduce costs associated with Maintenance, Repairs, and Operations. For more information, please visit www.imaltd.com.

Published in: Data & Analytics
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Data Cleansing for Efficient Asset Management and Maximum ERP Functionality

  1. 1. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. MRO DATA SOLUTIONS WHITE PAPER Data Cleansing for Efficient Asset Management and Maximum ERP Functionality IMA Ltd. 500 HWY #3 TILLSONBURG, ON CANADA N4G 4H3 T. (519) 688-3805 F. (519) 688-3807 E. info@imaltd.com www.imaltd.com Written by: Jocelyn Facciotti
  2. 2. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Key Takeaways • Gain a clear understanding of the MRO data cleansing process • Recognize the Before & After results of a successful implementation • Realize the tremendous benefits and value of quality MRO data • Learn how to calculate a realistic project ROI for your own business case To view the full White Paper, please visit www.imaltd.com
  3. 3. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Introduction As manufacturing and asset-intensive organizations grow and evolve over time, so too does the volume of data housed within their enterprise system(s). Whether growth occurs by natural progression, or via acquisitions and mergers, it adds a new degree of complexity to the already challenging master data management process. Through employee turnover, varying languages, multiple enterprise systems, and little to no standard guidelines for data entry, master data progressively becomes inconsistent, inaccurate, and saturated with duplication. Common effects of low quality item master Data: • Unidentifiable Items • Duplication • Excess Inventory Accumulation • False Stock-Outs • Equipment Down-Time • Inability to Search and Locate Parts • Increased Maverick Purchases (Spot Buys) • Misleading and Unreliable Reporting • Limited Spend Visibility • Compromised Enterprise System Functionality 1
  4. 4. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. The Challenge Item master Data plays a CRITICAL role in the overall maintenance of facilities and operations. When every second of down-time can cost thousands of dollars, it is absolutely crucial for materials data to be consistent, reliable and readily available. With potentially thousands of legacy records housed in the existing item master and new entries being made daily, improving data quality and changing traditional processes can be a laborious and time-consuming task. 2
  5. 5. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. The Solution The ultimate objective and most efficient state for any organization is to operate on one best of the breed integrated ERP to enable visibility and communication across all business units whilst still providing low cost of implementation and high levels of functionality. To accomplish this, legacy data must be merged together to prepare for migration into the chosen ERP system. While many companies are aware of the data quality issues, it often isn’t until legacy data is merged together or systems are integrated that the severity of the situation becomes immediately and avoidably evident. At this point data cleansing is no longer an option, it is a necessity. Data Cleansing is defined as the process of analyzing, correcting, and standardizing corrupt or inaccurate data records within a dataset. Data Cleansing is a niche service that requires specialized software, subject matter expertise, and a strategic implementation plan to manage a mass, ever changing dataset. Without these three ingredients, companies that undertake a data cleansing project internally are at risk of wasting significant time, money and resources, only to end up right where they began. 3
  6. 6. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 1 – Data Evaluation & Needs Assessment • Perform a detailed data evaluation to assess the current state of the item master • Establish a baseline for KPIs and determine project requirements 4 During the evaluation, a data analyst will perform a series of automated reports to assess the current condition of raw legacy data, while identifying duplicate records and providing multiple before and after cleansing samples for review. Pre-Cleanse Data Evaluation Total Number of SKUs 104,531 Overall Data Score 79.50% Nouns 99,897 95.60% Part Numbers 91,856 87.90% Manufacturers 63,150 60.40% Review Items 5,657 5.40% Duplicates 11,705 11.20% Description Quality Avg. Number of Words 8 Average 49 Minimum 0 Maximum 240 Product Group Segmentation Bearings 4,589 4.40% Electrical 12,817 12.30% Hydraulic 331 0.30% Industrial Supply 64,949 62.10% Material Handling 614 0.60% Pneumatic 8,260 7.90% PT 7,601 7.30% Insufficient Information 5,347 5.10%
  7. 7. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 2 – Establish Standard Operating Procedure 5 Next, a corporate Standard Operating Procedure (SOP) must be established. The SOP Should encompass all data components, including naming convention, abbreviations, commodity codes (product groups) and formatting requirements. At this stage, the service provider will offer industry expert consulting support, as well as best practice recommendations using an internally developed Noun-Modifier Dictionary, Abbreviation List, and standard formatting templates. Understanding that each company, industry and enterprise system are vastly unique, the SOP and project plan should be customized and tailored to provide the foundation for long-term quality data.
  8. 8. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 3 – Clean, Standardize, Enhance and Validate 7 Unless otherwise instructed, items will be cleansed to the highest service level possible.
  9. 9. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 3 – Clean, Standardize, Enhance and Validate 6  Legacy data will pass through a series of automated programs, which will identify, segregate, and standardize the Manufacturer Name and Manufacturer Part Number  Using the pre-defined Noun-Modifier dictionary, each record will be assigned a Noun- Modifier pair and associated attributes  Existing attribute information will be standardized and validated  Items will be researched and enhanced with additional attribute information (where possible) using from an internal library and pre-approved sources  Each record will be assigned a commodity code (product group) as requested  Potential duplicate and review items will be identified and compiled in a separate file MFG Name & Part Number Assign Noun, Modifier &Attributes Standardize & Validate Legacy Data Enhance Item Descriptions Assign Valid Commodity Code Identify Duplicate & Review Items
  10. 10. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 4 – Quality Control Review The Quality Control Review process is performed by a dedicated team of subject matter experts who possess strong part knowledge and a high attention to detail. During this phase, each product group, manufacturer name, part number and description is carefully reviewed and analyzed to ensure accuracy, completeness, and compliance to project standards. A series of software programs further validate attribute information and check for outstanding spelling mistakes or inconsistencies. Upon review and approval, each item is signed off and deemed “Clean” 8
  11. 11. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 5 – Duplicate & Review Items Duplication represents one of the greatest inefficiencies with any given dataset. During the cleansing process duplicates are identified by direct match, and fit-form-function equivalent. Direct Match: Items items that possess the identical Manufacturer Name and Part Number. 9 Fit-Form-Function Equivalent: Items that possess a different Manufacture Name and/or Part Number, yet are equivalent based on specifications (size, material, description). In addition to duplicity, 10% of a typical item master is generally found to be review items. Review Items are items lacking critical information for accurate part identification such as Manufacturer Name or Part Number. These items are flagged and compiled to a review list, then returned to the customer. Duplication often represents 10-15% of a given dataset.
  12. 12. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. Data Cleansing STEP 6 – Enterprise Formatting & File Delivery Once the entire item master has been cleansed and reviewed by quality control, it is deemed complete and transferred to a team of IT specialists. At this stage, software programs apply abbreviations according to project standards and full descriptions are concatenated into their respective fields in order to comply with ERP specifications. Proper data formatting and configuration at this stage will enable seamless uploading into the new ERP system, while ensuring maximum search and reporting functionality. Once data has been formatted to the ERP it is exported into a load-ready file and returned to the customer via electronic file transfer. 10
  13. 13. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. The Results As a result of cleansing, the item master now maintains one consistent format and nomenclature across the enterprise, part descriptions beam with attribute-rich descriptions, duplication is nonexistent, and data is properly formatted to meet the configuration requirements of the chosen enterprise system. More valuable than the aesthetic appearance is the extensive search and reporting functionality that has now been unlocked within the ERP system. With confidence, maintenance and procurement can now perform efficient part searches, generate useful reports, conduct detailed spend analytics and identify idle inventory. Key Benefits of Data Cleansing include: • Efficient Part Search Ability • Maintenance Time Savings • Accurate Reporting Capabilities • Identification and Removal of Duplicate Items • Excess Inventory Reduction • Elimination of Maverick Purchases (Spot Buys) • OEM to MRO Conversion Opportunities • Maximum ERP/ EAM/ CMMS Functionality 11
  14. 14. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. About IMA Ltd. IMA Ltd. is an industry-leading Material Master Data Management solutions provider, specializing in Data Cleansing, Inventory Optimization, Spend Analysis, and Strategic Procurement services. Since 1989, IMA Ltd. has supported manufacturing and asset- intensive organizations worldwide to improve data quality and reduce costs associated with Maintenance, Repairs, and Operations. For more information, please visit www.imaltd.com. 11 Contact Us 500 Hwy #3 Tillsonburg, ON Canada N4G 4G8 t. (519) 688-3805 f. (519) 688-3807 e. info@imaltd.com www.imaltd.com No Cost, No Obligation Data Evaluation Learn the current condition of your item master and cost savings opportunities hidden within, while receiving detailed before & after cleansing samples. Get Started
  15. 15. www.imaltd.comMaster Data | Collect. Clean. Govern. Apply. THANK YOU To view the full White Paper, please visit www.imaltd.com

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