Product Life Cycle Management / Master Data Management

During a digital product’s lifecycle, it’s hard to maintain end-to-end transparency and traceability.They often have tech, setup, and operation issues. They must keep prices down since products are getting more complicated and personalised, the time it takes to get to market and come up with new ideas is shortening, and they must fulfil the wants and rules of global customers.

Our Approach for Product lifecycle Management (PLM):

We use digital transformation and process automation across the full value chain in our PLM strategy. We cover all PLM methodologies, processes, and system solutions and can integrate PLM into upstream and downstream processes and IT systems.

Product lifecycle management extends beyond the simple product creation process (PEP). It integrates sales, marketing, and bridge master data concepts into PLM (Product Lifecycle Management), ERP (Enterprise Resource Planning), MES (Manufacturing Execution System), and MRO (Maintenance, Repair, and Overhaul) systems. PLM drives IoT, Industry 4.0, and Digital Twins. PLM delivers transparency, traceability, and the capacity to turn product-describing data into data-driven actions and decisions.

PLM alignment: Procedural and technological framework developed and assessed based on client needs, capabilities, and best practises

PLM Strategy: Tailor-made PLM strategy for the short and long term

Business Process Consulting: Alignment with industry best practises and removal of product development bottlenecks

PLM Return on Investment: Current PLM investment and approach to increase value

Assessing PLM: Selecting the correct technology stack for product innovation

PLM transition plan: Create a roadmap to PLM excellence and a rollout strategy.

Our Approach for Master Data Management (MDM):

Our approach identifies and analyses opportunities within master data management inputs, evaluations, and patterns.

Data Governance: Developing a framework for MDM

Data lifecycle: establishing effective routines and processes

IT Framework: Identifying and selecting integration tools

Data Quality: Implementing KPIs to monitor data quality and provide insight for data cleaning activities