In the article Why Manufacturers Need Process Mining – A New Type of Data Analytics, the author is extolling the benefits of what he calls a new type of data analytics – process mining. Process mining can be used to reduce inventory costs, identify production bottlenecks, improve on-time delivery, and optimize logistics between production sites, distribution centers, and end clients. It’s hard to justify that process mining is actually a new type of data analytics that can be used for manufacturers, but process mining does follow the a few of the key rules for successful big data usage. First, as the name implies, it concentrates on one process. Implementation of successful big data projects normally requires concentration on one area for improvement. The specificity of big data projects usually allows them to be successful, and the article looks at why process mining, and big data analytics on the whole, can be successful in saving manufacturers money.
Process mining specifically looks at a very important factor in manufacturer’s processes, KPIs or key performance indicators. KPIs are exactly what they sound like, the main factors that measure performance and the overall successfulness of process or project. Process mining’s value lies in the fact that it initially makes one look at KPIs of a process. It also challenges the manufacturer to validate if they are measuring the right KPIs and understand if the data they are gathering can be related back to KPIs. Process mining, as all big data does, uses software do the difficult work of visualizing the processes and highlighting specific variances impacting KPIs. One example used in the article is the examination of throughput times and the ability to identify that specific vendors are not meeting their lead-time commitments. These types of analyses and results is exactly what big data projects are meant to do and achieve.
Another point that the article points out is that process mining encourages manufacturers to identify inefficiencies and problems within the process. It encourages companies to embrace their issues. This ideology is absolutely necessary for continuous improvement, and it is a key to any big data project. Embracing issues can be difficult, especially for older, engrained processes, but it is the only way to eliminate them. It certainly is not the easiest thing to do, and requires a humble mindset entering an improvement project. Process mining has all the key factors of a successful big data software, and it could be very useful for manufacturers that want to embrace the big data revolution.
Do you believe addressing KPIs first is the best way to approach a big data project?
Do you think process mining is actually a new or different type of big data analytics or just a rebranding of basic big data?
Do you think that some manufacturers are reluctant to implement big data projects because they do not want to know their inefficiencies?