Our last post explained how to quantify the value of improving your Material Master data by reducing stock-outs related downtime. Today’s post will help you estimate improved materials-related personnel productivity. Improved materials-related personnel productivity: Total number of man hours = Total number of engineers * 1600 Total number
In our previous post, we provided a calculator to help you estimate the value of improving your Material Master data by avoiding inventory cost. Today’s post features an ROI Calculator to help you plug in your numbers and estimate inventory holding cost reduction. Inventory holding cost reduction:
Our last post talked about the Operations issues that arise from poor Material Master data quality. Let’s take a look at purchasing and spend analysis impacts of poor data. Sourcing Sourcing projects and the make-versus-buy decision process in general require a view of what exists already in the approved
Master Data Governance for Materials is unique. The workflow is more multi-domain than any other master data object, the timelines for completion are tight, and the need for accuracy is dire. We’ve included two charts below from an earlier survey which outline the make-up of Material Master Data Governance
Data Governance represents the symbiotic existence of IT and business–aligned to improve the overall business process. It unifies the compartmentalized information across various departments in an organization to boost data sharing by all concerned. The information sharing results in advantages such as: • Revenue growth • Cost rationalization •
In our personal life as well as professional ones, we are being inundated with an increasing amount of data. While this might just prove to be a minor inconvenience in most cases, if such huge amounts of data is not managed properly at the business level, the long term
Data quality, data governance, and material master data management are what I generally talk about in these posts, because that is what I know (duh!). However, in the past year, one term related to data has become extremely popular and has become a part of our lexicon. It is being
Data quality is no longer something that any organization can avoid or put on the back burner. With all data being considered as assets, it is critical to ensure data credibility at every stage of processing. Today we tell you about the errors that can creep in your data and
In the last post, we talked about data quality, specifically as it relates to purchased parts and products. One important dimension to data quality, which is being talked about these days is data credibility. Let us see what it means and entails and how you can ensure it becomes
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