The butterfly effect on enterprise data within SCM

After doing a couple of projects where master data was a critical element but unfortunately nothing was done to correct it, I thought that I would reintroduce the post on the Butterfly effect in Master Data. All to often the master data is completely forgotten in projects and in the end costs money to rectify and could lead the software to fail after go live. So to learn more about the butterfly effect and it’s impact on master data read on!

The term “butterfly effect” refers to the way a minor event – like the movement of a butterfly’s wing – can have a major impact on a complex system – like the weather. The movement of the butterfly wing represents a small change in the initial condition of the system, but it starts a chain of events: moving pollen through the air, which causes a gazelle to sneeze, which triggers a stampede of gazelles, which raises a cloud of dust, which partially blocks the sun, which alters the atmospheric temperature, which ultimately alters the path of a tornado on the other side of the world.

Enterprise data is equally susceptible to the butterfly effect. When poor quality data enters the complex system of enterprise data, even a small error – the transposed letters in a street address or part number – can lead to revenue loss, process inefficiency and failure to comply with industry and government regulations. Organisations depend on the movement and sharing of data throughout the organisation, so the impact of data quality errors are costly and far reaching. Data issues often begin with a tiny mistake in one part of the organisation, but the butterfly effect can produce disastrous results.

An ERP or supply chain system focuses on parts, descriptions and inventory data. Since government agencies don’t control the way supply chain and ERP data is defined, you may not have an idea about how the data should look in an ideal state, but it should provide an accurate depiction of the physical warehouse or just-in-time supply chain system. You need to know what is in stock, when it can be supplied and how much it costs.
Holding just the right amount of inventory is crucial to optimising costs. After all, inventory costs are incurred every hour of every day in areas including warehouse storage, heat and electricity, staffing, product decay and obsolescence. With this in mind, consider the impact of a transposed letter in an ERP system. Someone enters part number XL- 56YJM instead of LX-56YJM. Until the error is discovered and corrected, you may hold duplicate parts in inventory or not be aware of parts carrying the slightly different SKU because of the transposed letter.

You also want to take advantage of volume discounts. If the data is unstructured, making it difficult to understand global buy patterns, the company may miss out when negotiating with the vendor.

Because there is no standard format for ERP data, few of the steps for fixing the data are done for you ahead of time. It is critical to establish a methodology for data profiling in order to understand issues and challenges. Since there are few governing bodies for ERP and supply chain data, the corporation and its partners must often come up with an agreed-upon standard, with input from users of the data to understand context, how it’s used, and the most desired representation.

If you have clean data in your supply chain, you can achieve some tangible benefits. First, the company will have a clear picture about delivery times on orders because of a completely transparent supply chain. Next, you will avoid unnecessary warehouse costs by holding the right amount of inventory in stock. Finally, you will be able to see all the buying patterns and use that information when negotiating supply contracts.

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