40 % of major corporations rank operational excellence and process efficiency as one of their primary strategic objective.The basis to attain process efficiency relies in observing and analyzing business processes. For more than 20 years IT technology is used to support business processes which are mapped as transactions in the companies’ IT system landscape. This IT landscape sets up a ‘digital mirror’ of the companies’ organization and processes. In almost every company this heterogeneous landscape is continuously growing year after year. Along with this development the complexity of transactions and data is exploding. ‘Big Data’ is only one term which is used to describe this vast amount of produced data and its complexity.
Guiding questions – What to do with all the transaction data we find in our IT systems?
As each physical transaction leaves a digital trace in the respective IT system several question arise about how companies can handle and leverage increasing amounts of transaction data:
- How can we use digital traces to analyze business processes?
- Can we achieve a transparent and holistic performance measurement along the complete supply chain?
- Can we use the existing transactional data to identify inefficiencies within my organization?
The rising complexity and amount of data is truly a big challenge for all companies, but on the other hand it offers great potential.
What is process analytics and why does it have such big potential?
How did companies improve their process efficiency in the past? Usually they collected information for a limited timespan to describe the as-is processes by looking at process documentation or interviewing the employees who perform these processes. By gathering and analyzing the collected information, process weaknesses and bottlenecks are expected to be discovered. Process documentations often aren’t up to date nor exhaustive and interviews are usually biased – from the interviewer as well as from the interviewee. In conclusion, this analytic approach is time consuming and lacks in accuracy.
State-of-the-art process analytics gets rid of all these limitations. The potential of process analytics lies within the ability to discover process improvements fact based. In modern IT landscapes every process is logged in a database and has a timestamp and a unique ID assigned. With the help of process analytics tools this raw set of transaction data is aggregated, visualized and serves as unbiased and profound decision basis. By taking advantage of already existing data, process analytics saves time and money,
Besides fact-based decisions modern process analytics offer three main benefits:
Continuous monitoring and improvement
A once set up analysis can be adapted in many conceivable ways. Changes in the databases either through new processes, changed scope or altered quantities are automatically taken into regard as long as they are executed in already connected databases. If new databases need to be integrated they can be added easily.
The holistic set-up of a process analysis can be done in days to weeks – depending on the amount of databases and complexity of processes within your organization. The analysis itself can be executed ad-hoc. Even ‘real time’ and self-updating dashboards which give you an instant overview on your processes, your process efficiency and self-defined KPIs can easily be implemented with current process analytics tools.
Holistic and transparent perspective
The process analysis approach is holistic and accurate in many ways. Besides the inclusion of all available historic data and all end-to-end processes across divisions, buildings and sites also unknown process deviations are identified. These deviations often result in higher process costs. By applying process analytics every occurred process known or formerly unknown can be visualized, understood and improved.
Why are process analytics tools so promising for future supply chain management?
Of course supply chain management is just one of many application fields for process analytics. Due to complex end-to-end processes supply chains are one of the best application fields to take full advantage of this recently developed technology that allows fast and precise analyses.
Better on time delivery
The overall goal of supply chain management is to ensure the delivery of a specific amount of goods in a certain time span. In order to improve the on-time delivery, the entire supply chain has to be analyzed to discover the most time-consuming process variants. By applying process analytics these bottlenecks can be identified and fast process variants – the so called happy path – can be highlighted.
Optimization of lead time
By now it is very hard to measure and optimize lead times in the supply chain due to a vast variety of involved parties. With process analytics weak spots can be identified by aggregating data of end-to-end processes. Through the analysis of inbound data suppliers can be benchmarked i.e. by comparing the delivery note quality and the completeness. Best practices within different process sequences can be found and proactive insights can be used to react on customer needs more flexible and use case-oriented.
Higher utilization rate
Another major goal of supply chain management is to increase the utilization of company’s assets – usually measured as overall equipment effectiveness (OEE). Process analytics allows a holistic view on the company’s equipment in operation. The insights gained through data aggregation and visualization enable supply chain managers to prioritize actions leading to process improvement that can be continuously monitored. As a result, inventory costs can be reduced and operational excellence is assured.
Do you want to know more about this topic? Then keep following our series on process analytics in Supply Chain Management at the Capgemini Consulting blog
Do you see potential use cases for your company or do you want to get some information from our experts? Feel free to contact us:
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Daniel is Management Consultant for Digital Supply Chain Management at Capgemini Consulting. In his project experience he focusses on projects in Supply Chain Strategy and Digital Transformation
Daniel is a management consultant at Capgemini Consulting. As an engineer he focuses on the fields of digital supply chain management and manufacturing. In his professional career Daniel has gained experience in the IT, energy and automotive industries.