Solutions
Decision Intelligence for Supply Chain and Logistics
Know how supplier delays, demand swings, and logistics disruptions change the odds before you commit. Incertive models supply chain uncertainty across thousands of scenarios.
Supply Chain Plans Built on Averages Fail When Averages Do Not Show Up
Supply chain planning relies on estimates: average lead times, expected demand, projected transportation costs, forecasted capacity needs. These averages are useful for steady-state planning, but supply chains do not operate in steady state. They operate in a world of variability -- as Gartner's supply chain research consistently highlights -- with demand that spikes and drops, suppliers who deliver early or late, shipping lanes that congest without warning, quality issues that trigger re-orders.
The consequence of planning on averages is a supply chain that works well when everything goes according to plan and fails when it does not. Safety stock formulas based on average demand and average lead time systematically underestimate the inventory needed to handle the combination of high demand and late delivery - which is exactly the scenario that causes stockouts. Capacity plans based on expected growth rates leave no room for the demand surge that wins a major contract or the demand drop that loses one.
Incertive approaches supply chain planning differently. Instead of asking "what is the average lead time?", it asks "what is the range of lead times, and how does that range interact with demand variability and other supply chain factors?" The result is a Monte Carlo simulation that shows the probability of different supply chain outcomes - stockouts, excess inventory, delivery delays, cost overruns - across thousands of realistic scenarios. For more on how this applies across logistics operations, see our logistics use cases.
Supply Chain Decisions Under Uncertainty
Supplier Reliability Assessment
Evaluate suppliers not just on quoted lead time and price, but on the full range of their delivery performance. A supplier who quotes 14 days but delivers in 10 to 25 days has a very different impact on your operations than one who quotes 16 days but consistently delivers in 14 to 18 days. Incertive quantifies this difference so you can make sourcing decisions based on total cost of variability, not just unit price.
Demand Forecasting Under Uncertainty
No demand forecast is exact. The question is how wrong it might be and what that means for your supply chain. Incertive models demand as a range and shows how different demand scenarios affect inventory levels, capacity utilization, and service levels. You see the cost of under-forecasting (stockouts, expedited shipping) and over-forecasting (excess inventory, markdowns) across the probability spectrum.
Warehouse and Distribution Capacity
Warehouse capacity decisions involve long-term commitments - leases, equipment, automation investments. Incertive models throughput requirements under demand uncertainty to show the probability of capacity constraints at different points in your planning horizon. You can evaluate fixed capacity versus flexible options (3PL overflow, temporary labor) based on the likelihood of needing additional capacity.
Route Optimization Under Disruption
Optimal routes in normal conditions may not be optimal when disruptions occur. Incertive models transportation variability - transit time ranges, congestion probability, carrier reliability - to evaluate routing strategies that perform well across a range of conditions, not just the expected case. A route that is 5% more expensive in the base case but 30% more reliable under disruption may be worth the premium.
Inventory Risk Management
Inventory is a buffer against supply chain uncertainty, but carrying too much inventory ties up capital and introduces obsolescence risk. Incertive models the interaction of demand variability, supply variability, and lead time uncertainty to find the inventory levels that achieve your service level targets at minimum cost. You see exactly where safety stock adds value and where it is wasted.
Vendor Selection and Dual-Sourcing
Should you single-source for lower prices or dual-source for reduced risk? Incertive models both strategies under supply disruption scenarios. You see the probability and cost of a supply disruption under single-sourcing versus the ongoing cost premium of maintaining a second supplier. The analysis often reveals that the break-even point depends on disruption probability, which you can estimate from historical data.
Lead Time Variability Management
Lead time variability is often a bigger driver of safety stock requirements than average lead time. A supplier with a 14-day average lead time and a 10-to-20-day range requires more safety stock than a supplier with a 16-day average and a 14-to-18-day range. Incertive makes this explicit, helping you invest in lead time reliability where it matters most - often saving more than negotiations on unit price.
Seasonal Demand Preparation
Seasonal demand patterns are partly predictable and partly uncertain. You know Q4 will be higher than Q2, but not by exactly how much. Incertive models both the seasonal pattern and the within-season variability to help you build inventory, reserve capacity, and staff up at the right levels. The simulation prevents both the under-preparation that causes lost sales and the over-preparation that causes post-season markdowns.
Example: Should We Dual-Source a Critical Component?
A manufacturer sources a critical electronic component from a single supplier in Southeast Asia. The supplier offers competitive pricing and has been reliable, with lead times typically ranging from 4 to 6 weeks. However, in the past year, two shipments were delayed by 3 and 5 weeks respectively due to port congestion and a component shortage at the supplier's own supplier.
The procurement team is evaluating a second source - a domestic supplier with higher unit cost (roughly 15% more) but shorter and more consistent lead times (2 to 3 weeks). The question is whether the reliability premium is worth paying, and if so, what split between the two suppliers minimizes total supply chain cost including the impact of disruptions.
Incertive models the decision across thousands of scenarios with realistic variability in lead times (including low-probability but high-impact extended delays for the offshore supplier), demand fluctuations, and the carrying cost of safety stock. The analysis shows that single-sourcing with the offshore supplier has a lower expected cost under normal conditions, but a 20% probability of disruptions that cost more in expedited shipping, production delays, and lost sales than the entire annual price premium of dual-sourcing.
The simulation also evaluates different allocation splits. Sourcing 70% from the offshore supplier and 30% from the domestic supplier captures most of the cost savings while dramatically reducing disruption risk. The domestic supplier provides a reliable baseline that keeps production running during offshore disruptions, and the 30% allocation is enough to maintain the relationship and surge capacity when needed.
Sensitivity analysis reveals that the value of dual-sourcing depends primarily on the frequency and severity of offshore disruptions. If disruptions occur more than once per year, dual-sourcing is almost certainly cost-effective. If disruptions occur less than once every three years, single-sourcing is likely cheaper. The current rate of two disruptions per year strongly favors dual-sourcing. For more on quantifying this type of business risk analysis, explore how Incertive models complex trade-offs.
Supply Chain Network Design Under Uncertainty
Network design decisions - where to locate facilities, how to configure distribution, which lanes to use - are among the most consequential and least reversible supply chain decisions. A warehouse lease lasts years. A distribution center investment takes years to recoup. These decisions are typically made using optimization models that assume known demand, costs, and service time requirements.
But demand shifts. Transportation costs fluctuate. Service time expectations change. A network optimized for today's demand pattern may be suboptimal for tomorrow's. Incertive adds a layer of uncertainty analysis to network design by simulating how different network configurations perform across a range of demand and cost scenarios. A network that is optimal under expected conditions but fragile under disruption may be a worse choice than a slightly less optimal but more robust configuration.
This approach is particularly valuable for organizations facing demand uncertainty driven by e-commerce growth, market expansion, or channel shifts. The network that serves today's channel mix may not serve tomorrow's. By modeling the range of possible channel and demand evolutions, Incertive helps you design a network that performs well across the scenarios most likely to occur, not just the one you are planning for. Compare this to static spreadsheet-based network analysis.
Frequently Asked Questions
How does Incertive model supply chain disruption risk?
Incertive models supply chain disruption by treating key variables - supplier lead times, transportation times, demand levels, quality rates, and costs - as probability distributions rather than fixed numbers. You describe the ranges you observe or expect, and the simulation runs thousands of scenarios showing how different combinations of disruptions affect your supply chain performance. You see the probability of stockouts, delivery delays, and cost overruns under realistic conditions.
Can Incertive model multi-tier supply chain risk?
Yes. Supply chain risk often originates not with your direct suppliers but with their suppliers. Incertive lets you model multiple tiers of supply chain dependency, so you can see how variability at the raw material or component level propagates through intermediaries to affect your operations. This is particularly valuable for identifying hidden single-source dependencies deep in your supply chain.
How does this differ from supply chain visibility platforms?
Supply chain visibility platforms show you what is happening now - where shipments are, which orders are late, which suppliers have issues. Incertive shows you what could happen in the future. It is a planning tool, not a monitoring tool. You use visibility platforms to react to current disruptions and Incertive to prepare for future ones. They are complementary: visibility data can inform the uncertainty ranges you input into Incertive.
Can I use Incertive for vendor selection decisions?
Yes. Vendor selection is one of the most common supply chain applications. You model each potential vendor with their own performance characteristics - price, lead time range, quality variability, capacity constraints - and Incertive shows how each vendor choice affects your overall supply chain performance. A vendor with a lower price but higher lead time variability may cost more in the long run when you account for the downstream impact of delays.
How does Incertive handle seasonal demand variability?
Seasonal demand patterns add a layer of predictable cyclicality on top of random demand variability. Incertive models both - the seasonal shape and the uncertainty within each seasonal period. You might know that Q4 demand is typically twice Q2 demand, but the exact level in any given Q4 is still uncertain. The simulation captures both the seasonal pattern and the within-season variability, which is critical for inventory and capacity planning.
Can Incertive model the trade-off between inventory cost and service level?
Yes. This is a core supply chain trade-off that Incertive handles well. By modeling demand variability and supply variability together, the simulation shows you the inventory level required to achieve different service level targets. You can see that going from 95% to 99% service level might require doubling your safety stock, making the cost of that incremental service level explicit rather than hidden in a formula.
How does this work for global supply chains with multiple disruption types?
Global supply chains face geopolitical risk, currency fluctuation, customs delays, port congestion, and regulatory changes in addition to standard supplier variability. Incertive lets you model any of these as uncertain variables that affect your supply chain performance. You can model scenarios like "what happens if transit times from Asia increase by 2 to 4 weeks due to port congestion" and see the cascading effects on your inventory and customer delivery commitments.
Can Incertive help with network design decisions?
Yes. Network design decisions - where to locate warehouses, which distribution centers serve which customers, whether to use direct shipping or hub-and-spoke - involve significant uncertainty about future demand patterns and transportation costs. Incertive models these decisions under demand and cost uncertainty to show how different network configurations perform across the range of likely scenarios, not just the expected case.
Model Disruption Before It Happens
Simulate supplier delays, demand swings, and logistics disruptions across thousands of scenarios. See the probability of supply chain failures and size your contingencies based on quantified risk, not rules of thumb.
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