Task-Based Scheduling Models

Critical Chain Isn't Agile (But May Be Useful) I keep seeing pitches for "Critical Chain" (CC) and thought I'd write an explainer. Let's start with what Critical Chain isn't: It isn't Agile. CC assumes fixed scope, upfront planning, and centralized scheduling. We know Agile embraces dynamic scope, continuous delivery, and team autonomy. What Is Critical Chain? CC is a scheduling method introduced by Eliyahu Goldratt (1997, Theory of Constraints). It focuses on: Resource constraints (who's doing the work, not just the work itself) Keeping resources levelled (requiring flexible start dates) A shared project buffer at the end to absorb delays Feeding buffers to protect upstream chains Minimizing task-switching and WIP Tracking progress using buffer consumption instead of percent complete The promise is fewer delays, better focus, and higher likelihood of finishing on time - without sandbagging estimates. CC May Work When: You're coordinating a fixed-scope, fixed-date project Work is predictable with tight dependencies across teams or components You're integrating across systems or disciplines (e.g., software + hardware) You need realistic schedules based on resource availability Teams struggle with context switching or WIP overload Avoid CC when: Work is emergent, experimental, or dynamic Value already flows well (e.g., via Kanban or Scrum) You can't define the full scope or task sequence in advance Org culture won't accept aggressive (realistic) estimates or shared buffers (to absorb delays) Dependencies are fluid (e.g., team coordination shifts each sprint) How CC Works Planning Phase 1) Build Network: Identify tasks, assignments, durations, dependencies 2) Estimate Durations: Tasks with 50% confidence (equal chance early/late) 3) Identify Critical Chain: Longest path through schedule given dependencies 4) Add Buffers: Protect schedule without padding tasks - Project Buffer: End of critical chain (~50% of critical chain duration) - Feeding Buffers: Where non-critical paths feed in (~50% of feeding path) - Resource Buffers: Alerts ahead of critical tasks for key resources 5) Capture Baseline: Freeze initial plan to measure performance 6) Lock Network: Work structure doesn't change midstream Execution Phase 7) Forbid Multitasking: Work one task at a time to reduce switching 8) Track Buffer Consumption: Use Fever Chart to visualize progress (green, yellow, red zones) CC works best when coordination and predictability matter more than flexibility (e.g., ERP rollouts, aerospace, defense, R&D, and integrated systems with hard release dates). CC is less useful for Agile teams where backlogs evolve and delivery is continuous. Critical Chain Isn't Agile CC isn't Agile, but it is a disciplined way to finish complex work with limited resources and high coordination needs. It won't fix bad planning or dysfunctional culture, but it will surface constraints, focus teams, and give you a fighting chance to hit deadlines.

After thinking deeply and practically as an operations research practitioner working on transforming Toyota North America’s supply chains and logistics, here’s how I’ve come to think about yard scheduling and sequencing in real-world operations. In yard logistics, deciding how to handle scheduling and sequencing often comes down to a fundamental design choice: do you build one end-to-end optimization model, or break the problem into a hierarchical structure? Both approaches have merit, but the right choice depends on the maturity of your operation, your data quality, and your need for responsiveness. An end-to-end model treats the entire yard as a single optimization problem. From the moment a vehicle arrives at the gate to the point it exits after fueling, staging, and processing, every step is scheduled together. The advantage is clear: this global view can yield truly optimized flows, minimize bottlenecks, and align all decisions toward throughput and efficiency. However, this approach is heavily reliant on high-fidelity data across all zones of the yard. If there are unexpected delays (like a worker calling in sick, a fueling station going offline, or a shuttle arriving late) the whole plan can become fragile. In practice, this kind of system also tends to be computationally heavier and slower to react in real time unless reoptimization is thoughtfully designed. A hierarchical model breaks the yard down into zones or functions. High-level scheduling might determine when a shuttle should move workers, or when a batch of cars should be released to fueling. Then, each zone locally decides how to sequence its tasks. This makes the system more resilient, local disruptions can be handled without having to re-optimize everything. It’s often easier to implement and more forgiving of messy, real-world variability. But the downside is that decisions made independently in one zone may not be optimal when viewed from the yard’s perspective as a whole. You may reduce wait time in one area only to create backups downstream. In my experience working in yard scheduling, the best path forward is staged. If your data systems are still maturing, start with a hierarchical model. It’s practical, easier to maintain, and more robust to the day-to-day volatility of yard operations. As your visibility improves and your systems become more integrated, evolve toward an end-to-end approach, or better yet, implement a hybrid system. In this setup, global optimization runs periodically throughout the day, reoptimizing as new information comes in (whether it’s a backlog at a station, a no-show worker, or a delay in vehicle arrivals). Meanwhile, local zones retain autonomy to adjust sequencing based on real-time conditions. This balance between strategic coordination and operational flexibility is where the real performance gains are unlocked. #YardScheduling #Optimization #SupplyChainExecution #OperationsResearch #DecisionIntelligence #RealTimeOptimization #Logistics

Resource-constrained activity scheduling by means of finite capacity scheduling in project and manufacturing environments: With long experience in resource-constrained activity scheduling mostly in manufacturing environment, I see something common in the scheduling of project activities on site (at least over short periods like a few weeks) and production operations on shop floor. As I understand, the scheduling exercise is quite similar in both cases. Generally speaking, it is to schedule a set of related tasks with precedence relations subject to limited availability of resources and some other relevant constraints including temporal constraints on task start times. CPM is not found to be useful for resource-constrained #scheduling of project activities even over short periods. People depending on CPM-based software may do manual resource leveling in the software or resource-constrained scheduling over short periods separately in Excel. I believe this major weakness of CPM partly contributed to the development of Last Planner System (LPS). LPS adopted in #leanconstruction involves "manual" activity scheduling by a group of people involved in the project over a few weeks but this approach is not convenient for quick revision and what-if analysis and for quick correction, if necessary. Based on my long experience with solutions for detailed, operations scheduling without resource overloading in complex job shops, I feel that finite capacity scheduling can greatly help with activity scheduling subject to resource constraints and other relevant constraints. Project management people seem to be mostly unaware of the power of finite capacity scheduling. There are affordable software tools to implement it in project and manufacturing environment. Using such tools, we can revise a resource-constrained activity schedule and do what-if analysis of the schedule quickly, easily and confidently. I have been promoting the applications of scientific finite capacity scheduling in manufacturing and remanufacturing industries for 25 years. A lot of manufacturing industries have been regularly using production scheduling software based on finite capacity scheduling. If anybody is curious to know how effectively finite capacity scheduling can be helpful for resource-constrained activity scheduling in project and manufacturing environments, I can freely demonstrate it over web (just for knowledge sake). Scientific scheduling approach is more effective, flexible and reliable in project and manufacturing environments. Without undermining the scheduling knowledge and expertise of #projectmanagement people, I venture to say that they still have something to gain by understanding how production is scheduled, controlled and managed in complex manufacturing systems. #cpm #lastplannersystem #schedulingsoftware