Digital Scheduling in Life Sciences: Unlocking Efficiency in Multi-Product Manufacturing
Traditional tools like spreadsheets, whiteboards, or Gantt charts fall short in managing these complex environments. They cannot simulate unplanned disruptions, uncover hidden bottlenecks, or support real-time decisions. In contrast, digital scheduling solutions provide a far more dynamic and intelligent approach.
What Sets Digital Scheduling Apart?
- Digital scheduling tools vary in complexity, but effective systems share key capabilities:
- Detailed granularity to capture the nuances of shopfloor activities
- Real-time data integration for both planning and progress tracking
- Scenario simulation using discrete event modeling for informed decision-making
- Fast rescheduling in response to disruptions
- Ease of access to tailored information based on user roles
At the core of these tools is a constraint-based process model that reflects real-world plant limitations, such as available equipment, piping constraints, and operator qualifications. This ensures that resources are never overbooked and that related operations like maintenance, metrology, and sampling are accurately coordinated.
When connected to ERP, MES, DCS, and supply chain planning systems, digital scheduling becomes semi-automated. Real-time updates from the shopfloor replace delayed manual feedback, improving forecasting accuracy and enabling agile responses to unexpected events.
Streamlining Communication and Collaboration
Once a schedule is approved, it can be shared across departments via a web-based platform, tailored to individual needs:
- Operators see shift-specific tasks
- Shift leaders get a broader campaign overview
- Supply chain teams monitor real-time progress and delivery impacts
- Data scientists access detailed metrics for analysis
This shared visibility reduces or eliminates coordination meetings. All departments—from QC and maintenance to production and planning—access a single source of truth, minimising miscommunication and redundant updates. Alerts and notifications can be configured to flag deviations and their impact on tasks, shifts, or KPIs.
Capacity Gains Through Simulation and Continuous Improvement
The same granular, constraint-based models used for scheduling can also run powerful simulations, based on historical data or expected task durations. This is particularly useful in biologics and cell and gene therapies, where variability (like cell growth) is high. Simulations help forecast:
- Yield and throughput
- OEE (Overall Equipment Effectiveness)
- OTIF (On-Time-In-Full) delivery
- The impact of changes through what-if scenarios
This supports a continuous improvement cycle: each batch is analysed, lessons are incorporated, and workflows are refined with minimal regulatory burden. For larger initiatives, like adding a new product, scaling capacity, or revamping equipment, digital scheduling enables accurate forecasting, ROI evaluation, and risk reduction before committing to changes.
Beyond Supply Chain Planning
While supply chain planning tools focus on long-term alignment between demand and supply, they don’t operate at the granular, real-time level needed for shopfloor execution. Digital scheduling bridges this gap, offering operational precision while exchanging key data (e.g., batch starts, stock levels, release forecasts) with supply chain tools. This integration enables accurate planning and fewer buffer times, unlocking true plant capacity.
Is It Worth It for Less Complex Operations?
Yes. Whether it’s small molecule production with SKU variety, manual ATMP workflows, or complex biologics, every production environment has its scheduling challenges. Even in simpler lines, digital scheduling helps forecast material usage, optimise production order, and manage storage constraints—driving efficiency, capacity, and delivery performance.
Emerson will be exhibiting at NLSDays 2025 – October 13-14, Gothenburg.
