Just-in-Time (JIT) Simulation
See how a pull system behaves before you build it. This JIT simulation models just-in-time flow, where production is triggered by demand rather than a schedule, so you can study how lot sizes and pull signals affect inventory and flow.
Run JIT Simulation →What is Just-in-Time (JIT)?
Just-in-Time (JIT) is a production philosophy in which materials and products are made or delivered only as they are needed, rather than produced to a forecast and held in inventory. Its aim is to minimize inventory and waste by synchronizing production closely with actual demand.
JIT is a pull system: downstream demand pulls production from upstream, typically signaled by Kanban. A workstation produces only when signaled that its output is needed, which keeps work-in-process low and exposes problems quickly, since there is little buffer inventory to hide them.
Simulating JIT is valuable because the behavior of a pull system depends on the interplay of demand variability, lot sizes, pull signals and process reliability in ways that are hard to predict analytically. A simulation lets you experiment with these factors, observe the resulting inventory and flow, and understand the trade-offs before committing to a physical implementation.
In plain terms: JIT means making things only when they're actually needed, not stockpiling to a forecast. Demand 'pulls' production from the previous step (usually via Kanban cards). It slashes inventory but leaves little cushion, so problems show up fast. Simulating it lets you test how lot sizes and signals behave before you rearrange the real factory.
Key Ideas
Pull, Not Push
Production is triggered by downstream demand, not a forecast schedule. Nothing is made until it is signaled as needed.
Kanban Signals
Kanban cards or signals authorize production and movement, controlling work-in-process by limiting the signals in circulation.
Low Buffers Expose Problems
Minimal inventory means disruptions surface immediately, driving continuous improvement, but leaves little slack for variability.
Key Formulas
What the Simulation Reveals
The simulation shows how inventory, work-in-process and throughput respond to your settings. Smaller lot sizes and fewer Kanbans cut inventory but demand higher reliability and lower variability; too aggressive a setting produces starvation and lost throughput.
JIT trades buffer inventory for responsiveness and problem visibility, which only works when the process is reliable and demand reasonably stable. The simulation makes this dependence tangible, showing how variability and breakdowns ripple through a lean pull system that has little inventory to absorb them.
Assumptions & Validation
Pull-Based Flow
The process is modeled as demand-pulled, not schedule-pushed.
If violated: For forecast-driven production, a push model or MRP fits better.
Reasonable Reliability
Processes are reliable enough to run with low buffers.
If violated: Unreliable processes need buffer or improvement before JIT.
Manageable Variability
Demand variability is within what the pull system can absorb.
If violated: High variability may require level scheduling or more buffer.
⚠️ Check assumptions first
JIT works only when the process is reliable and demand is reasonably stable, because a lean pull system holds little inventory to absorb disruptions. Simulating aggressive settings, tiny lots, very few Kanbans, on an unreliable or highly variable process will show starvation and lost throughput, which is exactly the real-world failure mode. Use the simulation to find settings your actual reliability and variability can support, not just the leanest theoretical ones.
When NOT to Use JIT Simulation
Forecast-Driven Production
For make-to-forecast, push-based planning, MRP or scheduling tools fit better than a pull simulation.
Single-Order Decisions
For a one-time order under uncertainty, use the newsvendor model.
Order Sizing Under Steady Demand
For economical order quantities with stable demand, use the EOQ.
Industry Applications
Lean Implementation
Test pull-system settings before rearranging a physical production line.
Kanban Sizing
Study how the number of Kanban signals affects work-in-process and throughput.
Lot-Size Analysis
See the effect of smaller lot sizes on inventory and flow.
Variability Impact
Observe how demand variability and breakdowns ripple through a lean system.
Frequently Asked Questions
What is Just-in-Time (JIT)?
Just-in-Time is a production approach in which materials and products are made or delivered only as they are needed, rather than produced to a forecast and stored. It is a pull system, where downstream demand triggers upstream production, typically signaled by Kanban. Its goal is to minimize inventory and waste by synchronizing production closely with actual demand, keeping work-in-process low.
What is the difference between push and pull production?
In push production, items are made according to a forecast or schedule and pushed downstream regardless of immediate demand, building inventory. In pull production, as in JIT, downstream demand signals authorize upstream production, so nothing is made until it is needed. Pull systems keep inventory low and expose problems quickly, while push systems buffer against variability with stock.
What role does Kanban play in JIT?
Kanban is the signaling mechanism of a JIT pull system. A Kanban card or signal authorizes a workstation to produce or move material, and production occurs only when such a signal arrives. By limiting the number of Kanban signals in circulation, the system caps work-in-process, controlling inventory directly. Kanban is what operationalizes the pull principle on the shop floor.
Why simulate a JIT system instead of just implementing it?
The behavior of a pull system depends on the interaction of demand variability, lot sizes, pull signals and process reliability in ways that are hard to predict on paper. A simulation lets you experiment with these factors and observe the resulting inventory, flow and throughput before committing to a physical rearrangement. This reveals trade-offs and failure modes, such as starvation, without the cost and risk of live trial and error.
When does JIT not work well?
JIT struggles when processes are unreliable or demand is highly variable, because a lean pull system holds little inventory to absorb disruptions. Breakdowns or demand spikes can quickly starve downstream steps and cut throughput. In such conditions, either the underlying reliability and variability must be improved first, or more buffer must be retained, since pushing a fragile process toward minimal inventory causes frequent stoppages.
How do lot sizes affect a JIT system?
Smaller lot sizes reduce inventory and improve flow, moving the system closer to the JIT ideal of single-piece flow, but they require more frequent changeovers and greater process reliability. Larger lots reduce changeover frequency but build inventory and slow flow. A JIT simulation helps find lot sizes that balance these effects given the real changeover times, reliability and demand variability of the process.
Test a Pull System Before You Build It
Simulate JIT flow and tune lot sizes and Kanban signals. Free during Beta.
Run JIT Simulation →