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The logistics industry is facing increasing customer demands as well as efficiency targets in procurement and warehousing. Customers demand accurate and timely deliveries, while companies strive for lower inventories and cost efficiency.
An often still unused aid in the optimization of logistics processes is the extensive data from transport management systems and peripheral systems available in the company and with partners, which describe a large part of a logistics process from the offer through planning, execution and completion.
With iVIEW .Process Mining, the process perspective becomes available in the context of proven BI-Tools as Power BI and Qlik Sense, eliminating the need for employees to learn new tools and keeping the effort required to connect to source systems low.
With the help of process mining, each individual job sequence with all its events is reconstructed from this and is thus available for analysis. This can be used to answer the following questions, among others:
High storage costs
Errors in sorting and inventor
Identification of problem area
Incorrect capacity planning
High transport cost
Monitoring of complex supply chain
WithiVIEW.process mining, we now provide you with a function library to turn your existing business intelligence suite – Qlik Sense or Microsoft Power BI – into an intuitive tool for professional process analysis. In this way, you supplement your business-relevant key performance indicators with the process perspective and can thus optimally use the digital transformation to shape your competitive advantages.
If changes to the procedure within the company are initiated on the basis of the analyses, the effects can be visualized immediately in the updated evaluations and compared with the initial and target picture.
In addition, process mining offers ideal support for the automation of processes via Robotic Process Automation (RPA). For this purpose, Process Mining is first used to identify the processes and process steps suitable for RPA. Then the optimal scope and process flow for automation is determined, also taking into account the existing systems.
With process mining, a continuous success control is now possible, in order to be able to readjust at short notice if necessary.
Companies know their business processes - but usually rather theoretically. In fact, there is often a lack of full process transparency.
The reasons are manifold - work processes are perceived subjectively, steps are added unconsciously. And employees usually only know about sub-processes related to their work. In addition, processes change, for example due to new compliance requirements or changed customer needs. And finally, the intended process flow does not always fit, for example in the case of special, non-standard production requirements.
And this is exactly where process mining comes into play, the automated analysis of business processes based on digital traces in IT systems.