How agilesTrade helps you to manage article lifecycles
If you are familiar with product master data you probably know the following problem: a completely new product is to be included in the master database, but not all features or quality test reports are yet known. So what should be done? Does it make sense to enter the data into your software? Or should you wait until all the data of the product is available?
Or maybe this scenario is familiar to you: An item should be removed from the assortment, but the remaining stock should still be sold. How do you identify such products in your software so that items are not mistakenly purchased again? These are some of the typical scenarios of ever-changing market requirements or customer needs that require customizations of the product range.
Your company may even develop and launch products before they go through the sales process in various phases – and eventually be removed from the active product range. Seasonal demand and temporary exceptional situations (e.g. regarding quality) are other factors that can influence the distribution of a product.
Decisive for the successful handling of scenarios of this kind is your software’s data quality. As a solution for the consumer goods trade, agilesTrade offers a master data extension that allows you to easily assign products to different life cycles (e.g. new, in planning, QA block, discontinuation, etc.). The software helps you to keep your assortment data up to date – and to avoid mistakes in the purchasing or sales process chain.
The establishment of these product life cycles includes the possibility of entering control elements. For example:
Each lifecycle change also offers the option of checking master data fields for correctness (for example, validity dates or mandatory fields). In addition, certain master data fields can be filled by the software with default values that are blocked, if necessary, for subsequent processing.
These and many other functions help you to better use and maintain master data. Due to their long-term validity and interdepartmental relevance, master data is a high intangible asset. Last but not least, long-term data quality is the basis for successful work.
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