AUTOMATIC MASTER DATA CLEANSING
We will perform fast analysis of the master data and as a result: define the project scope by excluding inactive or irrelevant items, identify potential duplicates, establish categories of cleansing based on identified classes, determine the necessary resources and timeline for project implementation. Typically, this activity does not take longer than 1 week.
Develop templates and attach each record to an appropriate one
We will apply the following 5 steps methodology to define templates
We will scan the dataset with our Synopps platform and correct more than 10 000 errors in a typical catalogue of 60 000 records, including replacing untypable characters, removing excess symbols, correcting syntax and spelling errors, replacing letters incorrectly used as numbers and many more. This activity will ensure better decomposition in the following step.
Recognition of data blocks and records normalization
An adaptive system of algorithms recognizes more than 220 000 significant blocks such as item classes, characteristics, and their values, part numbers and variations, values via keywords, etc. in a typical catalogue. To normalize and clean these recognized blocks, we apply the following steps:
Duplicates search based on recognized blocks
Identify over 7 000 potential duplicates in a typical catalogue of 60 000 records with Synopps algorithms, that compare classes considering synonyms, compare part numbers and their variations, without considering formatting, and compare values of characteristics, rather than their textual representation. Then validate with our expert team and end-users.
Redesign of processes to ensure the quality of master data
We redesign Master Data Management processes, such as new record creation or editing of an existing record, based on the world's best practices and business specifics. The deliverables include gap analysis and to-be process design, suitable IT solution or excel-based tool implementation, teams training and documentation, hand-over and support to ensure sustainable data quality.
Calculation of budget savings and blocking purchase requests
We calculate the forecast of warehouse balances and provide a list of positions that should not be purchased, thereby ensuring a multiple payback of the project. We apply a decision matrix based on a stock position, consumption and purchase requests, in order to identify an invisible stock that should be consumed, and excessing requests that should be blocked to deliver immediate cash savings.
An international mining company with several assets across various countries had a centralized catalogue with more than 75,000 SKUs. The lack of necessary information for existing items and misaligned processes for new item creation had resulted in several duplicated SKUs, inflated warehouse inventories and excessive purchases.
Practically any industry that uses large catalogues (from 20k items) to manage inventory and procurement. Most often clients come from manufacturing, Oil&Gas and mining.
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