01 / R&D CHALLENGES
Long cycles, many variables, cross-role work and sensitive information require efficiency, traceability and access control together.
- Requirements, tasks and experiment records are fragmented
- Formulation, sample, test and version relationships are unclear
- Sales, R&D, lab, quality and production handoffs are manual
- Technical documents are difficult to govern by project, customer and product
02 / R&D VALUE FLOW
Make project execution traceable first, then standardize data objects and knowledge rules.
- Requirement intake, assessment, initiation and priority
- Stages, milestones, tasks, resources, risks and reviews
- Experiment plans, samples, records, results, tests and exceptions
- Documents, conclusions, retrospectives and scale-up handoff
03 / CAPABILITY COMBINATION
Manage project execution in collaboration tools, structured R&D data in low-code apps, and connect existing systems.
- Teambition for projects, stages, tasks, milestones, documents and risks
- Yida for requirements, experiments, samples, tests, reviews and queries
- DingTalk for organization, messages, tasks, approvals and mobile access
- Interfaces to ERP, PLM, LIMS, QMS and document systems
04 / PHASED ROADMAP
Pilot with a real R&D project, then expand process, data and knowledge governance.
- Diagnose project types, roles, workflows, forms and documents
- Build templates, tasks and data apps for one project type
- Connect requirements, experiments, reviews, documents and operations
- Establish data quality, knowledge reuse and project review mechanisms
05 / FIT & ACCEPTANCE
Collaboration does not automatically replace specialized systems; boundaries depend on data, compliance and automation needs.
- Assess controlled formulations, instrument integration, testing and lab compliance separately
- Define codes and versions for projects, samples, experiments, formulations and documents
- R&D managers, specialists and IT jointly govern templates and access
- Accept through the complete requirement-to-project-close flow