Riding Complexity.
The Standard for Good Management.

Why FLD33?

In the industrial age, the ideas of Taylor brought success to organizations for almost a century. His scientific management practices taught us, that simplifications are useful in ordered circumstances.

The more volatile our world gets, the less the assumption that a certain level of predictability and order exists in the world, holds true. And humans are good at anticipating linear developments. But in the mean time not only Covid-19 has taught us, that the world is not linear.

Good leadership and management is not a one size fits all and does require technology to successfully deal with complexity, dependencies, change over time and the human inability to understand non-linear developments.

How does FLD33 work?

The methodology allows you to model a value driven view on your organization and create transparency and an objective basis for decision making in complex environments.

Our technology offering is a cloud based service that holds objects and relations in a highly scalable volume and lets you extend and visualize your value streams along our methodology. Various connectors are available to integrate existing platforms and data sources and to publish reports in enterprise dashboards. 

The FLD33 enterprise meta model holds patterns and profiles for an ever growing number of business domains that can be used as templates to model your specific situation. It is managed as an open standard and free to apply.   

FLD33 uses machine learning to identify dependency patterns of success factors.  

What is FLD33?

FLD33 is a complexity management and value engineering solution that helps you optimize your execution efficiency.

It handles individual profiles like software quality, delivery performance  or customer segmentation that are consolidated in domains like digital transformation, marketing performance or customer lifetime value. Profiles and domains can be interlinked and over time you get a digital representation of and data driven value reports for your entire corporation.

The solution lets you simulate and over time predict outcomes over time based on internal and external factors.

Field 33 is the first field on the second half of the chess board.

Why FLD33?

In the industrial age, the ideas of Taylor brought success to organizations for almost a century. His scientific management practices taught us, that simplifications are useful in ordered circumstances.

The more volatile our world gets, the less the assumption that a certain level of predictability and order exists in the world, holds true. And humans are good at anticipating linear developments. But in the mean time not only Covid-19 has taught us, that the world is not linear.

Good leadership and management is not a one size fits all and does require technology to successfully deal with complexity, dependencies, change over time and the human inability to understand non-linear developments.

How does FLD33 work?

The methodology allows you to model a value driven view on your organization and create transparency and an objective basis for decision making in complex environments.

Our technology offering is a cloud based service that holds objects and relations in a highly scalable volume and lets you extend and visualize your value streams along our methodology. Various connectors are available to integrate existing platforms and data sources and to publish reports in enterprise dashboards. 

The FLD33 enterprise meta model holds patterns and profiles for an ever growing number of business domains that can be used as templates to model your specific situation. It is managed as an open standard and free to apply.   

FLD33 uses machine learning to identify dependency patterns of success factors.  

 

What is FLD33?

 

FLD33 is a complexity management and value engineering solution that helps you optimize your execution efficiency in real time. It holds knowledge of relevant existing empiric models that can be populated with your own data. FLD33 allows you to learn based on hypotheses and measured data. It is the most intuitive and efficient measure for organizational learning and un-learning. 

We offer distinct profiles like software quality, delivery performance  or customer segmentation that are consolidated in domains like digital transformation, marketing performance or customer lifetime value. Profiles and domains can be interlinked and over time you get a digital representation of and data driven value reports for your entire organization.

The solution lets you simulate and over time predict outcomes based on internal and external factors.

Field 33 is the first field on the second half of the chess board.

 

What we do

Consulting

We assess, visualize and optimize complex value streams based on existing ontologies and frameworks currently mainly in the areas of digital product organizations, digital transformation, company building, software development and marketing and channel management.

Transformation Analytics

FLD33 is a methodology and technology stack to measure and optimize your (digital) success holistically. We integrate structured and unstructured data from internal and external sources to prioritize your corporate backlog in real time and assess and optimize your performance and execution efficiency over time.

Global Standard

The FLD platform serves as a real time reference for success in a digital world. It connects the most successful corporates, consultancies and research institutions to jointly build and evolve operating models with built-in corporate foresight for the post-digitalization era.

Available profiles

Software Quality & Delivery Performance for Digital Product Organizations (DPO)

  • Gain an overview of your development, build, deployment and operational status in a consolidated way with regard to your organizational structure
  • Have an overview of code quality and hotspots related to architectural shortcomings like coupling and other vertical and horizontal dependencies
  • Detect detailed cause and effect relations over different teams, suppliers and domains
  • Zoom in to hot spots and problematic areas to scrutinize recognized problems
  • Identify the effect of procedural or structural changes in a time series graph
  • Analysis supported by statistical methods and AI based algorithms to detect weak signals and complex hidden characteristics
  • Provide a top-down, bottom-up and level-based analysis of organization, software and processes
  • Supports TISAX(R), ASPICE and various ISO standards
  • Connectors for Jira, Git (lab|hub), Bitbucket, Jenkins, SonarQube, Code Maat, LDAP/Active Directory, Appdynamics, New Relic, Splunk, Adobe Analytics, Google Analytics, Optimizely, Virtual Web Optimizer, Adobe Target, App Store/Play Store ratings, Google reWork research, Unlearn methodologies and Jaeger

Who we are

Sebastian Wohlrapp

Sebastian Wohlrapp

Founder & Managing Director

Sebastian Wohlrapp, born in Berlin (1975) is the founder and managing director of the commercial entity Field 33 GmbH, that orchestrates the grooming of the FLD33 standard. He studied mechanical engineering and business administration in Berlin, Zurich and Aberdeen. With more than 20 years of professional experience, Sebastian has enabled numerous global organizations to benefiting from innovative business and transformation approaches. He is a serial company builder and thought leader in business innovation and transformation. He worked for Deutsche Post, Nestlé, Swisscom, Hugo Boss, Procter & Gamble, Mercedes-Benz, Porsche and many other global brands.

 

Sebastian is leading contributions to the FLD33 standard in the areas of
  • Corporate Digital Strategy & Company Building
  • Organizing for Digital
  • Change vs. Transformation
  • Platform Business
  • Management and Leadership
  • Product Owner FLD33
Prof. Dr. Marcus Schögel

Prof. Dr. Marcus Schögel

Co-Founder & Lead Contributor to FLD33 Standard

Marcus Schögel, born in Berlin (1967) is head of the competence center for Marketing Channels and Alliance Management at the Institute for Marketing and Retail Management at the University of St. Gallen. He studied business administration at the Free University of Berlin. He earned his doctoral degree from St. Gallen University in 1997 and is since then lecturing different courses in marketing as well on bachelor, master and executive level. His research focuses on current issues in marketing strategy, channel management and trend management in marketing. In his research he works closely together with different companies from consumer goods as well as from service and IT-industries (e.g. BMW Group, McKinsey&Co, Microsoft, Henkel KgaA, Procter & Gamble etc.)

 

Marcus ist the leading contributor to the FLD33 standard in the areas of

  • Channels, Touchpoints and User Experience
  • Customer Value Methodology
  • Trend Prediction
Abhijeet Gupta

Abhijeet Gupta

Data Scientist & Machine Learning Engineer

Abhijeet Gupta is a researcher working at the intersection of Language Technologies, Machine Learning (ML) and Data Science. Starting as a Software Engineer in India, he found is calling in Artificial Intelligence and went on to obtain his MS at IIIT-Hyderabad and PhD at the University of Stuttgart. He has over a decade of research experience on data processing and analysis, semantic feature extraction from data and developing heuristic and statistical language models through supervised and un-supervised machine learning methods. His interests are in the areas of semantic knowledge extraction and representation.

 

At FLD33, you’ll find him working on:
  • Domain specific and organization specific ontology developement
  • Large scale data transformations to event-driven graphs
  • Graph based event driven services to the FLD33 standard frontend products
  • Developing quantitative and qualitative predictive models for event-driven data
  • Identifying scalable and robust methods for efficient informational access on big-data

Thomas Seibert

Thomas Seibert

Tech Lead FLD33 & Profile Owner

Thomas Seibert, born 1967 in Stuttgart, Germany, has more than 25 years of professional experience in the software industry. During his career Thomas has built, coached and managed teams in agile and lean contexts, defined architectural concepts for distributed large system landscapes, programmed countless applications and supported medium to large companies on their way to true digital leadership. He worked for companies like Kodak, diconium, Zürcher Kantonalbank, ETH Zürich, ING-DiBa, Allianz and Mercedes-Benz. Furthermore he is a speaker on conferences such as JAX and SpringOne where he shares his thoughts on productivity, software quality, architecture and product development with the larger software community.

 

Thomas’ work with Field 33 involves:

  • Product development of the FLD33 platform
  • Definition and implementation of agile and lean methodologies
  • Architecture of the FLD33 platform
  • Tech lead for the FLD33 platform
  • Software Quality Assurance

Jobs

Data Masseur / Masseuse

We are looking for an expert for data acquisition, transformation and integration. If you are familiar with ETL and it’s automation, this job might be for you.

Apply Now

Graph Database Expert

We love graph databases. Our current implementation runs on neo4J and we are part of the neo4j startup program. In order to enhace our graph capabilities, we are looking for a graph database expert. If you like graphs, APOC and GDS, this opportunity might be for you.

Apply Now

Working Student

If you are a student enrolled in computer or data science or equivalent and modeling a value driven view on organizations for transparency and an objective basis for decision making in complex environments is your future dream job, this opportunity might be right for you.

Apply Now