---
title: How to Find the Margin Decisions Your Dashboard Can’t Make
description: Learn why dashboards improve visibility but do not automatically improve decisions, and how industrial companies can close the loop with decision systems.
image: https://www.redsynth.ai/hubfs/AI-Generated%20Media/Images/Overloaded%20Control%20Room%20Dashboard%20Cluster.png
---

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supply chain

# How to Find the Margin Decisions Your Dashboard Can’t Make

We were 90 minutes in and just wrapped up a 110 slide PowerPoint presentation to a global business VP at a major petrochemical firm. This meeting was the chance for us to finally show what we had been working on for the past few months with…

[Christian Hubbs](https://www.redsynth.ai/insights/author/christian-hubbs) RedSynth Insights

Ideas for turning industrial complexity into better decisions.

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We were 90 minutes in and just wrapped up a 110 slide PowerPoint presentation to a global business VP at a major petrochemical firm. This meeting was the chance for us to finally show what we had been working on for the past few months with the leadership team and put it in front of the big boss.

We were working with the business VP’s senior team to upgrade their S&OP to a best-in-class Integrated Business Planning (IBP) process and I was the young data scientist responsible for understanding the data, its provenance, and tying it all together to enable our processes. New demand views, new supply views, new commercial views (almost always tables which remains a stubborn reality that may deserve its own discussion at some point) to facilitate discussions and decisions.

At least, that was the idea as explained by the consultants who trained us and my boss who guided the process.

The business VP saw things differently. His feedback was blunt. We wasted 90 minutes of everyone’s time, plus all the time in preparing for this meeting because there was nothing there to help make a decision. The economics were deteriorating, the business was facing competitive pressure, customers were waffling, and all we did was discuss what had happened so far with a consensus supply and demand plan that was more business as usual.

Over a decade later, that meeting still sticks with me. The VP was right and this pattern plays out time and time again across organizations today. Companies have invested heavily in their digital transformation efforts with upgraded ERP systems, data lakes, control towers, business intelligence tools, and increasingly sophisticated dashboards and reporting systems; teams in Houston can see when a cockroach sneezes in Lake Charles.

Despite these advances in data systems, increased visibility does not necessarily mean improved decision making.

A dashboard can tell you what happened. It can show you what is happening now. A forecast might even tell you what could happen next. None of those things, by themselves, tells you what to do.

## **Instrumented but Uncontrolled Business**

Dashboards are valuable tools. They can reveal trends, highlight deviations, and help teams understand the state of a complex operation. But most dashboards are fundamentally descriptive. They are focused on reporting, displaying information about events that have already occurred: production levels, sales, open orders, prices, and more.

Our industry knows the difference between measuring something and controlling it. A unit can be covered in instrumentation, thousands of tags streaming live to the board, but still be running open-loop if nothing acts on the readings. Without closing the loop, you can see that the pressure is rising without a way to open the valve.

Digital transformation efforts have moved instrumentation into the business, but they have not closed the loop on the decision processes. We still rely on manual interventions every step of the way on some of the highest margin decisions while dealing with more data than ever before.

Some companies have pushed past reporting toward prediction, building models that forecast prices, supply, and demand. It is a step in the right direction, but a forecast is still an open loop. It provides better direction, but does not close the gap to action.

Moreover, most forecasts are treated like reports. A single number is provided, flattening the range of possible outcomes that may radically change the way the business would operate. De-averaging that forecast - trading the single, point estimate for the range and the scenarios it obfuscates - is where the real decisions live. But even this does not make the call for you. It’s still an open loop system, albeit with added realism about your business.

Imagine you have a forecast that shows a key feedstock index will increase. Assume that this forecast is spot on, perfectly accurate and you’re facing an increase in this particular price.

What do you do about it?

Should you change how you run your assets? Shift the product mix? Build inventory ahead of the increase? Reduce production? Move volume between sites? Prioritize some customers and constrain others? Exit an unprofitable segment? Accept lower near-term margins to establish a position in a strategic growth market?

Even our hypothetical perfect forecast doesn’t answer any of these questions. At best, it is just one of dozens of inputs into the decision, not the decision itself.

This forecast, plus all of the other metrics teams are tracking, don’t automatically translate into better run-the-business decisions. Absent a **decision system**, they can become noise, which pushes teams to default to gut feel and doing things the way they’ve always done it.

## **The Missing Layer in Digital Transformation**

A decision system is a repeatable process that converts data, forecasts, constraints, and objectives into a recommended action. In short, it connects information to action.

For example, imagine that the spread between an ethylene index and a polyethylene index is a major driver of profitability. Through experience and analysis, the business determines that when the spread exceeds a particular threshold, certain combinations of customers, grades, and production sites become attractive.

When the spread falls below that threshold, those combinations should generally be avoided, unless there is an explicit strategic reason to accept the reduced margin, such as entering a growth market or securing a critical account.

That is a basic decision system driven by a spread and the understanding of a business's economics. It may be a heuristic rather than a mathematically optimized model, but it already does something a dashboard cannot, it converts market and operating data into guidance:

- When these conditions exist, take these actions.
- When the economics cross this breakpoint, change the plan.
- When we choose to violate the rule, make the strategic reason explicit.

Building even this type of system requires a deep understanding of the business. You need to know how assets behave, what truly drives cost and margin, which constraints are binding, where commercial commitments limit your options, and which strategic considerations should override short-term economics.

The value does not come from displaying more data. Instead, it arises from encoding the understanding of the business and its economics into data-driven rules to make decisions.

## **From Heuristics to Optimization**

Heuristic rules can be powerful and are a great starting point, but companies that are serious about improving their decision making capabilities will quickly run into walls.

Industrial businesses contain millions of possible combinations of production rates, product grades, sites, customers, transportation modes, inventory positions, and time periods. A decision that improves one part of the system can create costs or constraints somewhere else.

That is where a more advanced decision system becomes valuable.

Using optimization, simulation, and other industrial decision science techniques, a company can build a model of how its business actually operates. The model can incorporate:

- Available assets and operating limits
- Production costs and conversion yields
- Demand and customer commitments
- Product qualifications and substitution options
- Inventory and storage capacity
- Transportation costs and constraints
- Pricing and market forecasts
- Commercial and strategic priorities
- Uncertainty across supply, demand, operations, and markets

The system can then recommend the best available plan under a defined set of assumptions and objectives.

It can show how to run each asset, what product mix to produce, which customers to serve from which locations, where to position inventory, and which tradeoffs produce the greatest overall value.

More importantly, it can explain when the answer changes.

What happens if a price index moves by 10 percent? If an asset loses 20 percent of its capacity? If a supplier declares force majeure? If demand in a high-growth market exceeds the forecast? If a critical transportation lane becomes unavailable?

Instead of asking teams to interpret another set of charts, the system recalculates the consequences and identifies the best response.

This is the difference between business intelligence and decision intelligence: the first helps you understand the system, the second helps you operate it.

## **Does Your Team Have a Decision System?**

Successful digital transformations have left companies in a powerful position, but they need to push beyond the open-loop dashboards with the occasional predictive analytics overlay to unlock the full potential of their efforts. Expecting these tools to seamlessly translate into better decisions and increased margin alone is counting on them to do a job they were never designed to do. That’s asking your instruments to control.

Dashboards provide visibility. Forecasts describe possible futures. Human judgment establishes objectives, priorities, and strategic exceptions.

A decision system connects all three to action.

The next stage is the creation of systems that understand how the business works down to its economics, the choices it has in front of it, the objectives of the organization, and determine the best course of action to achieve these.

That is the goal of industrial decision science: closing the loop by leveraging your data for decisions.

If your team has invested heavily in visibility but still relies on spreadsheets, recurring meetings, and institutional knowledge to make its most important operating decisions, the missing piece may not be another dashboard, it’s the loop.

It may be a decision system.

We created the **Decision System Diagnostic** to help leaders rigorously evaluate their processes. The step-by-step assessment will help you:

- Determine whether a process is producing decisions or merely reporting information.
- Identify where critical decision logic remains trapped in spreadsheets or institutional knowledge.
- Score the maturity of your processes.
- Find the most important gap to address next.

The diagnostic is free and takes approximately 20 minutes.

Written by

[Christian Hubbs](https://www.redsynth.ai/insights/author/christian-hubbs)

[supply chain](https://www.redsynth.ai/insights/tag/supply-chain)[decision science](https://www.redsynth.ai/insights/tag/decision-science)[industrial decision science](https://www.redsynth.ai/insights/tag/industrial-decision-science)[prescriptive analytics](https://www.redsynth.ai/insights/tag/prescriptive-analytics)

RedSynth Insights

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