Enterprise Optimizer® Decision Engine
EXAMPLES
EO Decision Engine: Real-World Business Outcomes
FOOD & BEVERAGE MANUFACTURING
$5M in profit McKee Foods didn't know was on the table.
Five linked planning models, from long range capacity down to daily truck loading, upended the assumption that every plant should make every product close to its customers. Program ROI grew from 1,000% to 2,000%.
GLOBAL MANUFACTURING
Philip Morris International found over $500M in savings within six months.
A ten year Digital Planning Twin of PMI's full value chain, spanning 40+ manufacturing sites, turned an annual planning cycle into a monthly one and cut scenario turnaround from weeks to days. “We are talking payback in a couple of hours,” said the company's then Director of Global Manufacturing Capacity.
HEALTHCARE / HOSPITAL OPERATIONS
Jewish General Hospital closed 24 beds and saved $2.3M CAD, without turning away a single patient.
A prescriptive model of patient flow across oncology, surgery, medical, and the ED found which beds could safely close, and which popular ideas, like a weekend drop-in clinic, weren't worth the cost. The same model pointed to a path for up to 84 more beds and 1,000 additional elective procedures a year.
WHERE TEAMS START
Three ways teams arrive at EO Decision Engine.
Spreadsheets, custom optimization code, or a point SaaS tool: whichever one your team is running today, here is what changes.
SPREADSHEET USERS
FP&A, supply chain planning, network design
“Your 47 tab Excel model produces an answer. The Decision Engine produces the best answer, optimized across every constraint, with a full P&L, in hours instead of weeks. It's an easy transition that also saves significant grunt work down the road, and you don't need a data scientist to run it.”
CUSTOM APP / CODE OWNERS
IT business app owners, analytics teams
“Your optimization code works until someone asks a different question, then it's a rebuild. Business users also want a modern experience driven by AI. The Decision Engine lets the business user change the question and get a new optimized answer the same day, using AI not only to evolve the model and run scenarios, but also to help interpret results and make recommendations. No code changes. No OR specialist in the loop.”
POINT SAAS OPTIMIZATION USERS
Network design, pricing, scheduling, blending, financial planning tools
Every point SaaS optimization tool has the same solver underneath, LP or MILP. What differs is what sits on top. The Decision Engine is better in three ways.
Better outcomes
Point tools use standard costs and narrow scope. The Decision Engine computes costs from operational physics and models end to end, so the answer reflects the reality of the full business rather than one slice of it. That's why the financial results differ.
Better UX
In most SaaS tools, the LLM is an agent sitting alongside the application: a chatbot bolted on. In the Decision Engine, the LLM and the model are intertwined. You interact with the model through natural language. The engine understands the question, builds the math, and explains the answer.
One platform, many use cases
A point SaaS tool supports one or two decision types: network design, or pricing, or scheduling. Each one requires its own training, its own budget, its own IT integration. The Decision Engine deploys across supply chain, pricing, marketing, personnel allocation, scheduling, financial planning, blending, and more: one platform, one skill set, one IT footprint.
Point tools it displaces: network design, supply chain planning, production planning, personnel allocation, and vertical-specific optimization tools in mining, agribusiness, energy, and manufacturing.
CREDIBILITY
Not a pitch. A track record.
REFERENCES
WHAT CAN YOU OPTIMIZE?
Any industry. Any function. If it's a decision under constraints, it shines.
The Decision Engine works in any industry and any function, from a daily trading desk to a ten-year capital plan, from a single hospital to a global pharmaceutical supply chain.
The examples below are just a starting point, a way to see the pattern. Once you recognize the anatomy of a decision use case, you'll start seeing it in every planning meeting, budget review, and strategy session in your own organization.
THE ANATOMY OF A DECISION USE CASE
Every complex business decision has a structure that makes it solvable, not with guesswork, but with mathematical proof.
The Decision
What are you trying to determine? Which facilities to invest in. How to allocate a fleet. Where to deploy capital. What to produce, when, and for whom.
The Objective
What does “best” mean? Maximize margin. Minimize cost. Maximize throughput. Often multiple objectives compete: highest return and lowest risk, maximum revenue and best customer service. The decision engine finds the optimal balance.
The Constraints
What are the boundaries of reality? Budgets have limits. Plants have capacity. Contracts have terms. Regulations must be met. Quality specifications can't be violated. These aren't obstacles: they're what make the answer trustworthy. Any recommendation that ignores them is just a suggestion. One that respects all of them simultaneously is a decision you can act on.
The Scenarios
What if the world changes? Commodity prices shift. Demand spikes. A facility goes offline. A regulation tightens. The same model that finds today's optimal answer can test tomorrow's uncertainties, turning strategy from a debate into an analysis.
This is the pattern. It repeats across every industry and every function, at every level of granularity, from operational decisions made weekly to strategic decisions that shape the next decade. The decision changes. The objectives change. The constraints change. The math doesn't.
The following examples span a deliberately wide range of industries and decision types. They aren't a catalog of what the Decision Engine does. They're meant to get your imagination going. If any of these problems feel familiar, yours is solvable too.
THE LIBRARY
Twelve decisions. Twelve industries. One pattern.
These are twelve examples from a library of 50+. If your organization makes complex decisions under constraints, and every enterprise does, there's a use case here.
THE PATTERN IS THE POINT
These twelve decisions span mining and pharmaceuticals, hospitals and commodity trading desks, consumer goods and defense. The industries couldn't be more different. But look at the structure:
- →Every one supports a specific decision, not a report, not a dashboard, a decision.
- →Every one has clear objectives: what “optimal” means, stated precisely enough to solve for.
- →Every one operates within real constraints: the budgets, capacities, regulations, and physics that define what's actually possible.
- →Every one can be tested across scenarios, because the model that solves today's problem can solve tomorrow's “what if.”
This is what separates decision optimization from analytics. Analytics tells you what happened. Optimization tells you what to do, and proves it's the best answer given everything you're up against.
River Logic's Enterprise Optimizer® encodes 25 years of this pattern into an engine that any enterprise system, including AI, can use to make decisions that aren't just smart, but provably right.
Watch how the EO Decision Engine Works
GET STARTED
Contact us and watch how the EO Decision Engine works.
Bring us a decision to solve, and see for yourself how quickly you can get a result. No cost, and nothing to lock you in.