Smoothly functioning logistics is an important competitive factor and significantly determines a company's efficiency. Ideally, logistics is lean, well-conceived and seamlessly integrated into all processes. However, reality paints a different picture in many companies: historically grown workflows, scattered data and a fragmented system landscape lead to delays, unnecessary costs and inefficient use of valuable resources.
Logistics optimisation is therefore a business necessity. It is the lever through which companies can shorten lead times, reduce sources of error and regain operational flexibility. We take a look at central processes along the logistics value chain and show how these can be specifically analysed and optimised using Process Mining.
Logistics processes connect purchasing, production, warehousing and distribution into a functioning value chain. These are closely interlocked workflows that influence each other. Delays in goods receipt affect production supply, inefficient warehouse strategies lead to bottlenecks or excess inventory, faulty order picking delays deliveries and increases the return rate.
The main problem here is the lack of transparency about the actual workflows. In most cases, delays arise from small inefficiencies that are barely noticeable in day-to-day business but accumulate in combination. Anyone who wants to optimise processes must know where these friction losses occur, which paths materials and information actually take and how much individual process variants differ from each other. For logistics optimisation, exactly this understanding of the current state is crucial. Process Mining tools can deliver valuable added value here by drawing an objective picture of the actual process flows based on existing data traces.
Process Mining is a data-based analysis method that makes real process flows visible based on digital traces. These traces, called Event Logs, are created whenever an action is performed within an IT system. They contain valuable information such as timestamps, executed activities and process instances that enable detailed insights into the actual course of business processes. Process Mining evaluates these Event Logs, links them across process instances and assembles them into a complete, visually comprehensible process model. In principle, Process Mining distinguishes between 3 phases:
Process Mining offers enormous added value especially for logistics optimisation. Because processes are fragmented in hardly any other area as much as in logistics: goods receipt, warehouse management, shipping or returns processing usually run via different IT systems that, viewed in isolation, only map a limited section of the overall process. Process Mining in logistics brings the scattered data together and thus makes visible how the individual logistics workflows actually interlock. This overall view makes it possible to bring about holistic logistics optimisation.
The logistics chain consists of various sub-processes. We examine the most important sub-areas and show how Process Mining enables logistics optimisation.
Procurement logistics forms the starting point of the logistics chain and encompasses all activities to ensure material availability. Here, inefficiencies often arise through delayed orders, unclear demand forecasts or long reaction times to delivery delays. Missing feedback or media breaks between purchasing, supplier and warehouse are also typical weak points.
Process Mining ensures logistics optimisation here by completely reconstructing these workflows. This allows delays between order release and dispatch, bottlenecks with certain suppliers or deviating process variants to be transparently identified and specifically optimised.
Goods receipt encompasses the acceptance, inspection and booking of incoming deliveries as well as their forwarding to the warehouse or directly to production. Here, inefficiencies manifest in long idle times between delivery and booking, incomplete goods receipt controls or parallel recordings in multiple systems.
Process Mining visualises the actual goods receipt flow and identifies bottlenecks and delays. The technology creates transparency about how long goods remain in goods receipt before they are inspected, booked and stored, which enables targeted logistics optimisation. Process Mining in production creates additional added value here and uncovers, for example, potential backups at the interface to manufacturing.
Order picking encompasses the compilation of items according to customer orders and represents an important interface between warehouse and dispatch. Inefficient picking routes lead to unnecessary walking distances and considerable time losses that significantly reduce employee productivity.
Process Mining provides a remedy here by analysing picking times and routes and thereby identifying bottlenecks. The insights gained enable targeted logistics optimisation. Process Mining also offers great potential in the consumer goods sector, where large quantities of variant-rich products often have to be picked.
Dispatch encompasses the loading, transport and delivery of goods to customers or intermediaries. Inefficiencies arise, among other things, through unbooked goods issues, faulty route planning or media breaks between warehouse and transport service provider.
Process Mining visualises the entire dispatch process from order release to delivery and identifies delays and bottlenecks. It shows how long shipments remain in individual process steps and uncovers connections between certain process variants and delivery delays. The insights gained enable data-based logistics optimisation.
In many companies, the use of Process Mining does not fail due to the idea, but due to implementation. The implementation of Process Mining is sometimes hampered by technical hurdles, insufficient data quality or lack of know-how. Especially in logistics, the integration of various data sources represents a challenge due to the fragmented system landscape.
With Process.Science, the implementation of Process Mining is surprisingly straightforward. The decisive difference: Our solutions are integrated directly into your existing Business Intelligence platforms such as Microsoft Power BI or Qlik Sense. You can therefore use Process Mining where your data already converges anyway. Implementation takes place with minimal effort and delivers meaningful results within the shortest time. With our solutions, you can immediately begin with logistics optimisation.
