Prescriptive Decision Automation: A Smarter Way to Optimize Logistics
There’s a lot of confusion around predictive analytics, prescriptive analytics, and prescriptive decision automation — and it matters, because the difference determines whether your supply chain just tells you what’s happening or actually fixes it. This article breaks down the three, contrasts manual planning with automated prescriptive execution, and walks through a real case study of a consumer products company that made the switch using LevelLoad.
Understanding Supply Chain Analytics: Predictive vs. Prescriptive
What is Predictive Analytics? Predictive analytics forecasts future events using historical data, correlations, and trends. It uses statistical models, machine learning, and data mining to identify patterns and predict outcomes — in supply chain terms, that usually means forecasting demand (e.g., predicting sales of 500 units next week). Common use cases: demand forecasting, customer behavior prediction, risk assessment.
What is Prescriptive Analytics? Prescriptive analytics goes a step further and recommends the action to take to reach a desired outcome. If demand is rising at a specific distribution center, prescriptive analytics recommends sending more inventory there. It combines predictive analytics with optimization and simulation to identify the best course of action. Common use cases: optimizing inventory levels, streamlining supply chain processes, improving resource allocation.
| Aspect | Predictive Analytics | Prescriptive Analytics |
|---|---|---|
| Focus | “What might happen?” | “What should we do about it?” |
| Decision-making | Requires human intervention to act | Machine recommends the action (a human still executes it) |
| Optimization | Limited | Optimized for actionable outcomes |
Prescriptive Decision Automation: The Next Step
Prescriptive analytics becomes more powerful once it’s automated. Prescriptive decision automation reduces the need for human intervention by applying recommendations automatically, based on predefined rules and constraints.
Features of prescriptive decision automation:
- Automation — applies recommended actions automatically, based on predefined logic
- Real-time decisions — adjusts operations as conditions change, not on a delayed planning cycle
- Integration — connects with ERP, TMS, and WMS systems to keep visibility and execution in sync
- Optimization — uses AI-driven algorithms to find the best outcome given real-world constraints, goals, and historical trends
Use cases:
- Inventory management — automatically adjusting stock levels based on forecasted demand and supply chain conditions
- Carrier management — assigning loads to carriers to optimize cost and service
- Dynamic pricing — real-time price adjustments based on demand and market conditions
Automating prescriptive analytics reduces manual planning workload, cuts costs, and improves the consistency of day-to-day decisions. For more on this shift, see Revolutionizing Supply Chain Planning: The Power of Automation.
Case Study: From Manual Planning to Prescriptive Decision Automation
The old way: manual planning challenges
A large consumer products company generated 2,000–4,000 stock transfer shipments weekly. Its APS (Advanced Planning and Scheduling) system created replenishment requirements based on inventory needs and availability — but day-to-day volume on each lane fluctuated dramatically.
Those fluctuating requirements were organized into truckloads using a standard load-building tool that considered cube, weight, and pallet positions. When entered into the ERP to create stock transfer orders (STOs), inventory availability was rechecked — and because the APS and ERP calculated Available to Promise differently, discrepancies showed up here too.
Once entered, the STO triggered the TMS to request a carrier (a tender), usually a few days before pickup. When lane volume spiked, the first carrier — typically the lowest-cost, best-service option — often rejected some loads, and the rest were pushed to less desirable carriers, sometimes requiring a transportation analyst to call around when contracted brokers couldn’t cover demand. Sending tenders earlier would have helped, but planners were already starting before sunrise — there wasn’t more runway to give.
Between tendering and departure, a few things regularly went wrong:
- Inventory problems, when production didn’t deliver the required quantities on time
- Demand changes, so plans made days earlier ended up shipping excess inventory while urgently needed product sat at the plant — sometimes recoverable with last-minute adjustments, but often not
- Staffing strain at shipping and receiving sites, where fluctuating volumes caused backlogs when labor or dock space ran short
The net result: stressed staffing across planning and site teams, added transportation cost and degraded service, detention charges, overtime, and inconsistent order fill.
A better way: LevelLoad prescriptive decision automation
The company adopted LevelLoad, ProvisionAi’s automated prescriptive analytics platform. LevelLoad integrates data from APS, TMS, ERP, and WMS to build a live model of the network and optimize operations against it. (See also: The Role of Digital Twins in Modern Supply Chain Optimization.)
Here’s how the process changed:
The APS still generates requirements, as before — but now they feed into LevelLoad along with data from the other systems. Using optimization and AI, LevelLoad calculates truckload flows for every lane across the entire network — full truckloads, because that’s how large CPG companies move product between sites, and network-wide, because adjusting one lane can affect several others (if a plant warehouse is nearing capacity, for example, product may need to move to a different site instead).
The output is a carrier capacity requirement by day and lane for the upcoming month. Because LevelLoad accounts for expected fluctuations in advance, it smooths those requirements out over time instead of reacting to them as they hit.
Five days ahead of pickup, the system automatically signals the TMS through the ERP to request the trucks needed for each lane. At this point, the carrier only needs mode, origin, destination, and ship date — the shipment’s exact contents haven’t been finalized yet. And because day-to-day volume swings have already been smoothed out five days in advance, the carrier has a high probability of accepting the tender the first time.
Just before the ship date, using the latest inventory data, AutoO2 (ProvisionAi’s load-building optimization engine) fills each already-tendered load with the most needed product.
Human intervention in this process is minimal. LevelLoad runs unattended early in the morning; by the time planners arrive, the TMS has already tendered the loads for the week. The only regular manual step comes later — on Thursday, after the load builder runs, if a shipment doesn’t have enough inventory to fill it to target. That happens on a small minority of loads, and a planner either cancels the load or finds inventory to fill it out.
Results and benefits
- Transportation savings — carrier tender acceptance rose to 97% on the first attempt, reducing reliance on spot capacity and last-minute tendering costs
- Reduced detention — smoother, more predictable volume at shipping and receiving sites cut detention fees
- Better customer service — shorter lead times and more consistent load optimization improved order fill and reduced unexpected shortages
- Higher planner productivity — planners shifted from manual firefighting to strategic work, on normal hours instead of overtime
Automated Prescriptive Analytics: A High-Value Approach
Automated prescriptive analytics platforms like LevelLoad deliver measurable results — cost savings, operational efficiency, and better customer service — in a process that’s scalable and repeatable, not dependent on any one planner’s manual effort.
By combining predictive insight with real-time optimization and automation, supply chains can run with a level of consistency and resilience that manual planning can’t match.
Ready to reduce costs, improve service, and automate more of your supply chain decision-making? Contact us today.
