Process Development in Tempo Playbook

Executive Summary

This playbook provides a comprehensive guide for performing process development activities within Apprentice's Tempo MES system. The workflow is designed to provide structure for formulators while maintaining flexibility for iterative experimentation and data collection for long-term modeling purposes.

Process Overview

The Tempo process development workflow consists of four main phases managed by different user groups:

1. Template Creation & Management (Admin Users, Process Engineers, & Protocol Authors)

2. Batch Template Development (Admin Users, Process Engineers, Protocol Authors, Scientists, Formulators & Operations Leads)

3. Batch Execution (Operators & Formulators)

4. Data Analysis & Iteration (Formulators, Project Teams, Data Science & MSAT Groups)

Phase 1: Template Creation & Management

Responsible Users

• Users with template creation privileges (typically process Engineers, protocol authors, senior process experts or system administrators)

Real-World Context

In traditional process development, subject matter experts often create ad-hoc process instructions or use inconsistent documentation formats. Scientists might maintain their own versions of procedures, leading to variability and knowledge gaps. With Tempo, this phase centralizes the creation of standardized, reusable process templates that ensure consistency across all experiments while maintaining the flexibility needed for process development.

ROI and Value vs. Paper/Unstructured Documentation

Traditional Approach Challenges:

• Procedures stored in disparate Word documents or personal notebooks

• Version control issues leading to outdated procedures being used

• Inconsistent parameter definitions across different scientists

• Manual effort to update procedures across multiple documents

• Knowledge loss when subject matter experts leave the organization

Tempo Value:

• Single Source of Truth: All procedure templates centrally managed with automatic version control

• Reusability: Templates can be used across multiple projects and teams, eliminating duplicate effort

• Standardization: Consistent parameter definitions and validation rules across all experiments

• Knowledge Preservation: Institutional knowledge captured in structured, searchable format

• Compliance: Built-in approval workflows ensure procedures meet regulatory requirements

• Time Savings: Significant reduction in time spent creating new experimental protocols

Primary Activities

1.1 Author Parameterized Procedure Templates

Real-World Activity: Subject matter experts document standard operating procedures for processes like blending, milling, and roller compaction. Instead of creating static documents, they build flexible (and parameterized where valuable) templates that others can adapt.

Objective: Create standardized procedure templates for each process (blending, milling, roller compaction, cell harvest, cell culture expansion, purification, filtration, granulation, etc.)

Steps in Tempo:

1. Navigate to Template Management module

2. Select "Create New Procedure Template"

3. Define process name and type

4. Consider duplicating from an existing template

5. Configure procedure steps with parameterizable elements

6. Consider importing already existing steps from another template

7. Set parameter constraints and validation rules

8. Define dropdown options to minimize freeform text entry

9. Save template as draft

Key Considerations:

• Only designated administrative users would be able to create procedure templates

• Template changes and new templates would need to be requested and then created by these authorized users

• Avoid freeform text fields where possible

• Implement dropdown menus with comprehensive option lists

• Ensure all critical process parameters are configurable

• Consider equipment and material constraints

1.2 Define Parameters

Real-World Activity: Process experts identify which variables formulators should be able to adjust (mixing speed, temperature, time, material quantities) versus which should remain fixed for safety and consistency.

Objective: Establish parameters that formulators can specify during batch runs

Steps in Tempo:

1. Access Parameter Definition interface

2. Create parameter groups for logical organization

3. Define parameter types (numeric, dropdown, boolean, etc.)

4. Set acceptable ranges and validation criteria

5. Configure parameter dependencies and relationships

6. Test parameter validation logic

Current Limitations:

• Not all data types are supported as parameters (dropdown functionality missing)

• Parameter screen navigation can be slow and cumbersome

1.3 Make Templates Effective

Real-World Activity: Quality assurance review and approval of new procedures before they can be used in production or development activities. This replaces the typical paper-based approval process with a digital workflow.

Objective: Activate templates for use by formulators and operations leads

Steps in Tempo:

1. Review completed procedure templates

2. Validate all parameters and constraints

3. Conduct template testing with sample data

4. Approve template for production use

5. Set effective dates and version control

6. Communicate template availability to end users

Phase 2: Batch Template Development

Responsible Users

• Formulators

• Operations Leads

Real-World Context

This phase mirrors how scientists design experiments by combining multiple unit operations into complete process workflows. Instead of writing experimental protocols from scratch each time, formulators use the standardized building blocks (procedure templates) to create consistent, repeatable experimental designs. This is similar to how a formulator would design a DoE (Design of Experiments) study, but with built-in structure and guidance.

ROI and Value vs. Paper/Unstructured Documentation

Traditional Approach Challenges:

• Each experimental protocol written from scratch in Word documents

• Inconsistent experimental designs across different scientists

• Manual copying and pasting of similar procedures between experiments

• Difficulty comparing results across experiments due to inconsistent documentation

• Time-consuming protocol review and approval processes

Tempo Value:

• Rapid Protocol Development: Substantially faster experimental design using pre-built templates

• Consistency: Standardized experimental structures improve data comparability

• Error Reduction: Built-in validation prevents common experimental design mistakes

• Collaboration: Multiple team members can contribute to experimental design simultaneously

• Traceability: Clear lineage from procedure templates to specific experiments

• Quality: Systematic parameter grouping reduces missing or incorrect experimental conditions

• Resource Optimization: Better planning of equipment and material usage across experiments

User Permissions

• Allowed: Create batch templates using approved procedure templates

• Restricted: Cannot create or modify procedure templates (this is limited to administrative users)

• Access Level: Template modification and parameter configuration only

Primary Activities

2.1 Author Batch Templates per Major Process Sequence

Real-World Activity: A formulator designs an experimental workflow that might include: material dispensing → blending → milling → roller compaction → tableting. Instead of writing this sequence from scratch, they assemble pre-built procedure templates into a complete experimental protocol.

Objective: Create batch templates that combine multiple procedures for complete process workflows

Steps in Tempo:

1. Access batch template designer

2. Create new Batch template or duplicate from existing

3. Select appropriate procedure templates from approved library

4. Sequence procedures in logical process order

5. Configure inter-procedure relationships and dependencies

6. Set default parameter values where appropriate

7. Define material and equipment requirements

8. Save batch template for future use

2.2 Define Collection of Parameter Groups

Real-World Activity: Creating "recipe shortcuts" - grouping related parameters that typically change together (like all blending parameters or all compression parameters) so formulators don't have to set dozens of individual values each time they run similar experiments.

Objective: Create optimized parameter group collections for efficient batch setup

Steps in Tempo:

1. Navigate to batch parameter group authoring

2. Create new parameter group

3. Select relevant parameter subsets for specific process types

4. Define either full parameter groups or partial groups (ex: Partial group for a specific Unit Operation or for all Equipment)

5. Test parameter group functionality

6. Optimize groupings based on user (formulator) feedback and create as many as necessary

2.3 Make Templates & Parameter Groups Effective

Real-World Activity: Peer review of experimental designs before they're used, similar to how scientists review each other's protocols before executing experiments.

Steps in Tempo:

1. Submit batch templates for review

2. Complete validation testing if necessary

3. Obtain necessary approvals

4. Activate templates in production environment by changing their state to Effective

5. Train users (formulators, scientists) on new template availability

Phase 3: Batch Execution

Responsible Users

• Formulators (batch creation and initial execution)

• Operations Leads (batch planning and assignment)

• Operators (execution of unit operations)

Real-World Context

This phase represents the actual experimental work - where scientists move from planning to execution. In traditional settings, this might involve handwritten lab notebooks, verbal instructions to technicians, and ad-hoc documentation. Tempo structures this process while preserving the flexibility needed for process development and unexpected situations that arise during experiments.

ROI and Value vs. Paper/Unstructured Documentation

Traditional Approach Challenges:

• Handwritten lab notebooks difficult to read and search

• Verbal instructions lead to miscommunication and errors

• Manual data transcription introduces errors

• Difficult to track parameter changes and their impact

• Time-consuming to recreate successful experiments

• Poor visibility into ongoing experiments across the team

• Less sophisticated experience for technicians and operators

Tempo Value:

• Digital Documentation: All experimental data captured electronically and searchable

• Real-time Visibility: Management can monitor experiment progress across all projects

• Error Prevention: Parameter validation prevents out-of-specification conditions

• Rapid Iteration: Cloning successful experiments significantly reduces setup time

• Communication: Clear assignment of tasks eliminates confusion about responsibilities

• Data Integrity: Timestamped, electronic records eliminate transcription errors

• Regulatory Readiness: Built-in compliance features reduce audit preparation time

• Knowledge Sharing: Structured data enables learning across experiments and teams

Primary Activities

3.1 Create New Batch Runs

Option A: Create from Parameterized Standard Batch Template
Real-World Activity: Starting a new experiment using a proven protocol with standard conditions. This is like using a validated analytical method for the first time on a new sample.

Objective: Generate new batch run using standardized parameters

Steps in Tempo:

1. Navigate to Batch Creation interface

2. Select appropriate batch template

3. Choose standard batch parameter group as relevant or necessary or fill out required parameter values manually

4. Lock and send batch to execution

5. Assign operators to specific procedures if needed

Option B: Create from On-The-Fly Batch Template
Real-World Activity: Starting a new experiment using a proven protocol with standard conditions but potentially different order of operations or significant workflow differences

Objective: Generate new batch run using standardized parameters

Steps in Tempo:

1. Follow the steps in Phase 2 for quickly piecing together a new batch template from scratch and make it effective

2. Navigate to Batch Creation interface

3. Select newly-created batch template

4. Choose standard batch parameter group as relevant or necessary or fill out required parameter values manually

5. Lock and send batch to execution

6. Assign operators to specific procedures if needed

Option C: Create from Previous Run (Major Changes)
Real-World Activity: Taking learnings from a previous experiment and designing a follow-up study with significant modifications. For example, after a blending study shows poor uniformity, the next experiment might add a milling step and change multiple process parameters.

Objective: Create new batch based on previous run with multiple changes

Steps in Tempo:

1. Identify previous batch run in system template and parameters should be pulled from

2. Navigate to Batch Creation interface

3. Select appropriate batch template

4. Select Apply parameters from previous run and select the identified previous run

5. Modify required procedure-level or unit operation changes

6. Update parameter values as needed

7. Add or mark unit ops or procedures as N/A if necessary

8. Lock and send batch to execution

9. Assign operators to specific procedures if needed

Option D: Create from Previous Run (Parameter Changes Only)
Real-World Activity: Making small adjustments to a promising experiment - like increasing blend time from 10 to 15 minutes or changing compression force while keeping everything else the same. This is the most common type of iteration in process development.

Objective: Clone previous run with minimal parameter modifications

Steps in Tempo:

1. Identify previous batch run in system template and parameters should be pulled from

2. Navigate to Batch Creation interface

3. Select appropriate batch template

4. Select Apply parameters from previous run and select the identified previous run

5. Update parameter values as needed

6. Lock and send batch to execution

7. Assign operators to specific procedures if needed

Option D: Create from Previous Run (Parameter Changes Only)
Real-World Activity: Making small adjustments to a promising experiment - like increasing blend time from 10 to 15 minutes or changing compression force while keeping everything else the same. This is the most common type of iteration in process development.

Objective: Clone previous run with minimal parameter modifications

Steps in Tempo:

1. Identify previous batch run in system template and parameters should be pulled from

2. Navigate to Batch Creation interface

3. Select appropriate batch template

4. Select Apply parameters from previous run and select the identified previous run

5. Update parameter values as needed

6. Lock and send batch to execution

7. Assign operators to specific procedures if needed

Option E: Create from Template with Formulator-Driven Execution
Real-World Activity:  Starting a new experiment using a proven protocol with standard conditions. This is like using a validated analytical method for the first time on a new sample. The difference is formulators act as execution users for initial setup procedures, avoiding complex batch management interfaces. This mirrors how a lead scientist might set key experimental parameters at the bench, which then automatically flow through to guide technician activities downstream.

Objective: Generate batch runs where formulators execute initial parameter-setting procedures rather than managing complex batch configuration screens

Steps in Tempo:

1. Navigate to Batch Creation

2. Select specially designed batch template with formulator execution procedures

3. Assign formulator as execution user for first several procedures

4. Lock and send batch to execution

5. Formulator executes initial procedures to set critical parameters. Examples:

   1. Execute "Set Experimental Conditions" procedure

   2. Fill out key process parameters during execution

   3. Define material quantities and specifications

   4. Set equipment and process targets

6. Parameter values automatically populate downstream procedures

7. Assign operators to remaining unit operation procedures

8. Continue with normal batch execution process

3.2 Batch Planning and Assignment

Real-World Activity: Scheduling lab work and assigning technicians to specific tasks. This includes making sure the right equipment is available, materials are prepared, and the right people with the right skills are assigned to each step.

Steps in Tempo:

1. Review batch configuration and parameters

2. Enable redlining and batch modification settings as needed

3. Assign specific operators or formulators to specific procedures

4. Set execution schedule and priorities

5. Lock and plan batch for execution

6. Send batch to execution

3.3 Batch Execution

Real-World Context: This is the hands-on experimental work where materials are processed, samples are taken, and observations are recorded. It includes both planned activities and the inevitable "things that go wrong" that require real-time decision making and documentation.

Typical Activities:

• Ad Hoc Procedure Execution Changes (Formulators/Operations Leads)

  • Real-World Activity: The scientist makes key decisions about process flow, handles unexpected situations, and guides the overall experimental direction

    • Execute setup to establish batch plan

    • Handle complex navigation and decision points

    • Document any procedural modifications

  • Activities in Tempo:

    • Perform batch modification changes

    • Review and approve Overrides

    • Add process notes via A-Notes

    • Co-execute the procedures

• Unit Operation Execution (Technicians/Operators)

  • Real-World Activity: Technicians perform the actual processing steps - weighing materials, running equipment, taking samples, making observations

    • Execute assigned procedures on designated equipment

    • Record process data and observations

    • Handle material additions and equipment setup

    • Create samples and intermediate materials as needed

  • Activities in Tempo:

    • Leverage web and ipad execution

• Real-time Monitoring and Adjustments

  • Real-World Activity: Responding to unexpected results or equipment issues during processing - the kind of problem-solving that happens in every real experiment

    • Monitor process parameters against specifications

    • Document any deviations or exceptions

    • Implement corrective actions as needed

    • Record all process modifications and redlining

  • Activities in Tempo:

    • Perform batch modification changes

    • Review and approve Overrides

    • Add process notes via A-Notes

    • Co-execute the procedures

    • Help operators add substeps

    • Leverage Mark as N/A

Phase 4: Data Analysis & Iteration

Responsible Users

• Formulators (immediate review and iteration)

• Project Teams and Operations Leads (intermediate analysis)

• Data Science & MSAT Groups (long-term modeling and analysis)

Real-World Context

After experiments are complete, scientists need to quickly understand what happened, what worked, what didn't, and what to try next. This phase transforms raw experimental data into actionable insights for process improvement. It ranges from immediate "did this work?" assessments to sophisticated statistical modeling for process optimization.

ROI and Value vs. Paper/Unstructured Documentation

Traditional Approach Challenges:

• Data scattered across lab notebooks, Excel files, and various instruments

• Manual data compilation for analysis is time-consuming and error-prone

• Difficult to compare results across multiple experiments

• Knowledge trapped in individual scientists' notebooks

• Inconsistent reporting formats make trending impossible

• Weeks or months to compile data for regulatory submissions

Tempo Value:

• Automated Data Compilation: All experimental data automatically aggregated for analysis

• Rapid Analysis: Dramatic reduction in time to compile experimental results

• Trend Analysis: Structured data enables statistical analysis across experiment series

• Knowledge Preservation: All experimental insights captured and searchable

• Regulatory Efficiency: Automated report generation significantly reduces submission preparation time

• Decision Speed: Faster access to results accelerates development timelines

• Cross-Team Learning: Standardized data formats enable knowledge sharing across projects

Primary Activities

4.1 Batch Review and Approval

Real-World Activity: Scientists review their experimental results, lab notebooks, and any issues that occurred to determine if the experiment was successful and what they learned. This includes looking at both quantitative data and qualitative observations.

Steps in Tempo:

1. Access completed batch run

2. Review execution data and process parameters

3. Analyze A-notes, images, and documentation

4. Review exception reports and deviations

5. Close exceptions and document resolutions

6. Upload or attach any additional raw data or documents

7. Approve or reject batch run based on results

4.2 Data Extraction and Analysis

For Immediate Iteration (Formulators):
Real-World Activity: The scientist reviews their experimental results to answer "What should I try next?" They compare current results to previous experiments and identify the most promising parameters or conditions to explore further.

Steps in Tempo:

1. Generate batch reports with key process data or download steps data via JSON or CSV on individual procedure runs

2. Leverage the Exceptions filtering to review critical process parameters and results

3. Compare results against previous runs

4. Outside of Tempo or Add as A-Notes: Document lessons learned and improvement opportunities

5. Plan next iteration

For Long-term Modeling (Data Science/MSAT):
Real-World Activity: Data scientists and process engineers perform sophisticated statistical analysis across multiple experiments to identify trends, build predictive models, and optimize entire process workflows. This work might happen weeks or months after individual experiments.

Steps in Tempo:

1. Call Tempo Public API to extract procedure run data or leverage Tempo Events to automatically send step data out during execution

2. Structure data for modeling and simulation requirements at the destinations

3. Perform comparative analysis across multiple runs

4. Generate insights for process optimization

4.3 Process Iteration and Improvement

Real-World Activity: The continuous cycle of experimentation that drives process development - taking learnings from each experiment to design better ones. This is the scientific method in practice: hypothesis, experiment, analyze, refine hypothesis, repeat.

Steps in Tempo:

1. Review previous batch results and identify improvement areas

2. Modify procedure templates, batch templates, parameter groups based on learnings

3. Follow the steps in phase 2 and 3

4. Continue iteration cycle until process optimization achieved

Best Practices and Guidelines

Overall ROI Summary

Implementing structured process development in Tempo versus traditional paper-based or unstructured digital approaches typically delivers:

• Time Savings: Significant reduction in experimental setup and documentation time

• Quality Improvement: Substantial reduction in experimental errors and rework

• Compliance Efficiency: Faster regulatory submission preparation

• Knowledge Retention: Complete capture and preservation of experimental knowledge

• Collaboration Enhancement: Real-time visibility and coordination across teams

• Decision Acceleration: Faster time from experiment to actionable insights

Conclusion

This playbook provides the framework for effective process development within Tempo MES. Success depends on proper template management, structured parameter configuration, thorough execution documentation, and systematic data analysis. Regular review and refinement of these processes will ensure continued improvement in efficiency and data quality. It also requires a significant mindset shift and change management. See our value proposition guide for more on this.

For additional support or questions regarding specific Tempo functionality, consult with your system administrators or the Apprentice support team.