Data Analytics & Process Mining

We guide you to data-based decisions.

ifp Data und Process Analytics

Data Analytics, Process Mining, Data Mining, Big Data

In a modern supply chain, numerous data is generated and stored. All machines generate process data and share their status. Products provide status and location messages, and logistics generates time stamps for goods postings and shipments.

Taken in isolation, only singular conclusions can be drawn from these data. The exponential increase in the amount of data (general rule: 90% of all data was created in the last two years) ensures that data is collected and stored faster than it is systematically used to make correct decisions. To gain far-reaching insights, the data must be linked and analyzed using novel methods and tools.

Data Analytics

Data analytics and process mining are increasingly used to transform complex data into valid information. We support you in the areas of data collection, data analysis and data interpretation.

ifp consulting has sophisticated tools to get more out of your data. The latest user knowledge from our innovation.lab as well as our many years of cross-industry experience in the SME environment make it enables us to gain:

  • overarching contexts
  • deep-rooted causes and
  • future-proof findings

about your production, logistics, products, suppliers and customers.

Analyze process data

Process Mining

Collect digital traces in IT systems

Find causes of deviation

Corporate processes are often confusing and complex.

Errors, inefficiencies or process loops can creep in, resulting in lost time, rising costs or loss of quality and ultimately dissatisfied customers. Process mining specifically involves collecting and storing process data based on digital traces in IT systems and translating it into a model of the real process.

In this way, undocumented process flows can be quickly documented and documented process flows can in turn be compared with reality. Process mining thus offers itself well as a data-driven alternative or complement to interviews, multi-moment surveys, and manual data analysis in consulting projects.

The results can now be used to optimize processes or train employees in the use of the documented processes. Unlike process KPIs, which may indicate that, for example, the optimal lead time has not been achieved and how high the deviation is, process mining can find and eliminate the cause of the deviation.

We support you with Big Data Analytics

Big Data Analytics has established itself as a standard at ifp consulting in recent years and is used in more than 80% of all consulting projects.

  • Planning
    Checking data quality, data availability and database homogeneity. Definition of the required evaluations and evaluation strategy
  • Data selection and -extraction
    Listing of the data required for the evaluations and support in extracting it from the ERP system, with or without live data connection
  • Data transformation
    Data cleansing (correction or filtering of incorrect data) and data reduction (determination of the relevant aggregation level)
  • Data- or Process-Mining
    Pattern recognition with big data software such as Power BI, Tableau, Celonis or in-house software
  • Interpretation & Evaluation
    based on already 4,000 prepared solutions for our customers.

Big Data Analytics Tools

Proven knowledge generation

The use of analysis tools is nowadays demanded by the customer in every second consultation. Ifp consulting has been carrying out all projects with the use of Big Data analysis tools for years, provided that the customer data quality and structure allows it. The ifp approach to Big Data Analytics is based on proven scientific knowledge generation processes.

What are the advantages of process mining?

A major advantage of process mining is objectivity. This is because process mining is based on data collected via IT systems and not on assumptions or subjective assessments by individuals.

In addition, process mining can be repeated regularly for individual processes, enabling iterative process optimization. This ensures agility in a rapidly changing corporate world. As with Big Data analyses, repeatability can be realized via a direct link to the ERP system.

Typical process mining use cases at ifp consulting are:

  • Bottleneck analyses
  • Lead time analysis and lead time reduction
  • Identification and definition of the “Happy Path”
  • Search for undesired deviations in the process and determine the causes

Cooperation with ifp consulting

  • Systematic Data Analytics
    Standard use of data analytics and process mining in all projects
  • Internal expertise
    Consultants with data analytics specialization, mathematicians and IT developers
  • ERP system data competence
    In-depth knowledge of data structures. Support possibility during data preparation
  • Standardized tools
    Use of widely available tools such as Tableau, Power BI, RapidMiner or Celonis
  • High adaptability
    Use of custom applications and methods as needed
  • Transparency
    Provision of all source files and evaluations in the original format

Factors of success

You need the right methods and tools for big data analysis. Our team helps you analyze complex process and product data.


Take away

After the interpretation and evaluation of your data, new supplementary evaluations are prepared if necessary or existing evaluations are optimized.

These can be, for example:

  • Automatic capacity planning based on raw SAP data and sales forecasts
  • Cluster analyses, parts list analysis & optimization
  • Make-or-Buy Analysis
  • Master data cleansing and preparation