Consulting Machine Learning and Analytics in development

Data Analytics and Machine Learning (ML) have become very prominent in many areas of product development. From digital assistants in smartphones and smart home applications, to assistance and security systems, to convenience functions in various software packages - for example, in the creation of time and resource planning.

In other areas, e.g. production and the entire IoT environment, data analytics and approaches based on it, such as predictive maintenance, have long since found their way into everyday life and have shown how great the potential is for increasing efficiency and quality.

Analytics and machine learning to enable the development processes

In product development, this is not yet the case on a broad scale. The majority of companies do not yet use data on their products, product sales and product use to optimize their development. Similarly, the data from their development processes are not yet used to optimise them on an evidence-based basis.

What is certain, however, is that machine learning and analytics approaches will have a massive impact on the way all companies work in the coming years and will enable otherwise unattainable business advantages in competition. Foreseeable and partly already in use are e.g:

• The partially automated analysis of customer requirements and market changes,

• the partially automated development of new product features,

• Optimization of product structures and variants,

• Tailoring or support in the execution of business processes,

• Support and acceleration of decision-making processes.

In order for these to be realised, however, some basic organisational conditions must be in place:

• Entrepreneurial and consistent target systems,

• Coordinated data and IT structures and consistency of tools & systems,

• Models and sources of extensive, consistent and qualitatively valuable data on products and processes,

• entrepreneurial understanding in the organization of possible, realistic applications and

• If necessary, the establishment of "Analytics" or "AI teams", which have an orientation as entrepreneurial service providers.

Challenges in the introduction

On the one hand, the slow application of these concepts to date is certainly due to the nature of product development, which is per se much less predictable than in highly automated production processes. Associated with this is the difficulty of collecting process and application data of products, which presupposes adequate networking. Questions of data protection law also contribute to this.

When applied to one's own concepts, it often hinges on the large number of tools used in the development process, which nevertheless cannot provide the essential data and information about the development processes and their effects on costs, adherence to deadlines and the quality of products and services.

Similarly, for a number of services that could be automated and thus simplified and significantly accelerated today on the basis of machine learning and data analytics, there are still many employees who could use their training and experience to perform higher-value and more value-added activities. Standard support requests and problem resolution and automated problem identification and elimination in systems are among the prominent examples in this category.

ROI-EFESO helps you to overcome these challenges within the scope of consulting projects and thus to realize the potentials step by step in your company. This includes:

• Business Diligence:
  • Development & clarification of essential questions and fundamental goals, which you want to solve in your development with Analytics
  • Assessment of your tol and data landscape for initial evaluation of the possibilities
  • Assessment of the maturity of processes and organization
• Information Architecture and Analytics Design:
  • Determination of relevant use cases
  • Analysis and conception of the data and tool landscape including adaptation and automation of development processes
• Entrepreneurial realization:
  • Data-driven product development: Upgrade product management and portfolio management based on customer and usage data
  • Data driven process improvement in development:
    Process optimization and implementation of improvement mechanisms based on development process data, e.g. ML-supported effort estimation
  • Support in the creation of an organizational capability, e.g. building up AI teams