Control Towers Aren’t Meant to Be Just Decorative– Make Yours Work For You

Many companies have poured millions into “state-of-the-art” supply chain planning systems, expecting these tools to surface clear insights on high-impact actions. Yet, despite these investments, businesses still struggle to identify the top five supply chain priorities or actions, at any given point of time, that could truly impact their bottom line. Now, planning system vendors are touting Gen AI as the answer, promising that it will finally bridge the gap between data and insight. But in reality, executives, planners, and business heads still find themselves buried in granular data and staring at pretty dashboards, while missing the big-picture priorities. The result? Missed targets, bloated inventories, unanticipated costs, firefighting to get orders out on time, and a series of reactive adjustments that barely scratch the surface of strategic alignment. And no, no amount of investment into Gen AI is going to solve this problem.

The Struggles Behind the Systems: 3 Stories from the Frontlines

• Demand Planner: Drowning in Data but Missing the Signals Demand planners in chemical companies are often tasked with reconciling complex data inputs across hundreds of SKUs, each influenced by customer demand, sales inputs, and statistical models. One demand planner we worked with was responsible for managing 300 SKUs. Despite having access to a sophisticated planning system, she struggled to identify which forecasts had the biggest impact on revenue, leaving her with no clear priorities. With no mechanism to link forecasts to financial outcomes, the system fell short of guiding her efforts toward the highest- value actions.
• Supply Planner: Stuck in the Weeds, Missing the Big Picture Supply planners face similar issues as they handle detailed editing of planned orders for individual plants, transfer orders between plants and providing MPS guidance plant by plant, SKU by SKU. One supply planner we worked with was adept at adjusting orders, level-loading resources, and balancing loads and was a functional expert in the art of supply planning, but yet lacked a clear view of which of his actions could drive the highest service or cost benefits or would have the most significant impact on the company’s ability to meet its CM targets. Without insight into broader priorities, his work drove operational adjustments but not strategic alignment, often leading to costly bottlenecks or missed service opportunities. This is a common theme we observe even at companies with so-called “high planning maturity” and well established S&OP/ IBM processes: Planners are well-equipped with data but lack a way to see the value impact or tradeoffs associated with their supply chain decisions (without doing some extensive work), making it difficult to optimize across the supply chain.
• Executives: Sleek Dashboards, But No Strategic Insight Executives and business heads often view performance through a series of functional dashboards, which visually display operational data in real-time. While these dashboards are sleek and provide plenty of functional metrics, they lack insight into what matters most. For instance, one business head we worked with struggled to determine which of the ten red-flagged metrics on his dashboard demanded attention or were signaling real risks to contribution margin (CM) or revenue growth. Instead, the data was scattered across silos, and it was unclear which issues had overlapping causes or which actions would address root problems. In effect, he was left to sift through disconnected metrics without guidance on how to prioritize resources or allocate time to make the most financially impactful decisions.

These examples highlight a common reality: despite high-tech planning systems, companies are missing the ability to prioritize high-value actions effectively. Planning vendors are now touting control towers as the remedy, but these often fall short of being the comprehensive, decision- support tools they are marketed to be.

Control Towers: A Remedy with Deep-Rooted Limitations

Control towers, positioned as the ultimate solution for supply chain visibility, frequently fail to deliver actionable intelligence. They fall short in three key areas critical for translating data into impactful decisions.

• ?Insight from Historical Data Most control towers are forward-looking, focusing on resource allocations and future forecasting without leveraging historical data. Yet, past performance data holds valuable insights into patterns, recurring issues, and potential adjustments. Without mining these insights, chemical companies lose the opportunity to learn from previous cycles and optimize future plans.
• Feedback Loop for Planning Policy Calibration Current planning systems lack a closed-loop feedback mechanism. When forecasts miss targets or resource constraints impact plans, there’s no structured way to feed these learnings back into future planning. This limitation keeps planning policies static, preventing the systems from adapting to the realities of execution.
• Integration with Financial and Commercial Data Operational metrics are often isolated from financial data in control towers, making it difficult to connect high-priority issues to financial outcomes. Without aligning operational performance with metrics like CM or revenue growth, companies struggle to prioritize the actions with the most significant financial impact. For instance, a forecast variance on a high-value SKU may require greater attention, but without linking forecast accuracy to revenue, planners are left guessing where to focus.
• The Pitfall of “Pretty Dashboards” Many control towers today offer visually appealing dashboards loaded with metrics. Yet, these collections of functional KPIs often lack context, showing data without guiding actionable insights. This leads to a disconnect between strategic goals and operational actions, leaving companies with a surface-level view of their supply chain performance, but without the clarity needed to make financially impactful decisions.

Unlocking Hidden Value with Insight-Driven Capabilities

For executives in chemical companies looking to extract real value from their supply chain planning systems, the solution doesn’t lie in newer “state-of-the-art” tools or the latest Gen AI features being marketed by planning vendors. Instead, it’s about leveraging the data they already have by building capabilities that turn information into strategic insights.

By focusing on integrating financial metrics with operational data, embedding predictive analytics, fostering cross-functional collaboration, and establishing a closed-loop feedback mechanism, executives can create a control tower that aligns with strategic business goals. This insight-driven approach elevates the most financially impactful actions for each team, helping demand planners prioritize forecasts that matter, supply planners address bottlenecks with the highest service risk, and executives focus on actions that enhance CM or revenue growth.

A Roadmap: Building Capabilities for Strategic, Insight-Driven Supply Chains

While many chemical companies are data-rich, they often struggle to transform this data into actionable insights. Data itself isn’t the problem; in fact, most companies we work with already have plenty of good data, even if the common complaint is that “the data sucks.” What’s often lacking is not data quality but rather the context that makes data meaningful and the mechanisms to surface insights from it when they’re most needed.

Chemical companies are sitting on valuable information across their supply chains, but these insights are buried in transactional systems, isolated in silos, or embedded in granular metrics that don’t point toward strategic actions. Without a structured approach to link operational metrics with financial impact and organizational goals, data remains just numbers—useful in hindsight but ineffective in guiding forward-looking decisions. This disconnect leads to situations where planners and executives have access to an abundance of data but lack a framework that transforms it into clear priorities or actionable intelligence.

What’s needed is a systematic way to contextualize data across departments and functions, elevating insights that help decision-makers see the big picture. This means creating an insight- driven supply chain capability to identify patterns and flag high-impact actions, a feedback loop to improve future decisions based on past outcomes, and integration with financial metrics so that every action is aligned with the organization’s bottom line. By layering this context over their existing data, chemical companies can shift from simply managing information to strategically acting on it, achieving more proactive and profitable supply chain operations. Here’s how chemical companies can bring structure to their data and turn it into an engine for actionable insights:

Step 1: Begin with Data Integration

Start by consolidating data from all relevant sources—demand, supply, financials, and operations —into a single platform. This goes beyond just combining data; it means ensuring that the information from different functions is aligned and accessible. By connecting data across departments, you build a comprehensive foundation that allows for a unified view of your supply chain, which is essential for identifying high-impact actions across roles.

Step 2: Identify Financially Impactful Metrics

Rather than focusing on every metric available, prioritize the ones that are most directly tied to financial outcomes, like contribution margin (CM) and revenue growth. These metrics should form the core of your control tower, enabling the team to focus on areas that will drive the most significant returns. When financial impact is clear, planners and executives alike can make more informed decisions, targeting the most critical areas for cost savings or revenue enhancement.

Step 3: Implement Advanced Analytics Capabilities

Use predictive and prescriptive analytics to go beyond what’s happening to understand what’s likely to happen—and what should be done about it. By embedding analytics-driven insights, we can identify potential disruptions, bottlenecks, or forecast variances and recommend specific actions. For example, we can highlight which forecast variances would most impact contribution margin variances or cash flow variances or suggest adjustments in production schedules to prevent stockouts, allowing teams to act before issues escalate.

Step 4: Develop a Feedback Mechanism

Establish a closed-loop system that analyzes actual outcomes and continuously refines planning policies. By building a structured feedback loop, companies can learn from past deviations between plan and execution, using those learnings to adjust policies and models. This way, your planning adapts to real-world results over time, reducing the recurrence of similar issues and enhancing your ability to meet financial targets consistently. For example, if recurring capacity constraints impact CM, insights should be offered on what scheduling policies at which plants will have the highest impact on CM improvement.

Step 5: Ensure Accessibility Across Levels

Make insights available and understandable to everyone who needs them, from planners handling daily operations to executives making strategic decisions. This democratization of insights ensures that high-impact actions are aligned across all levels of the organization. By providing tailored views of relevant metrics and trends for each role, the control tower helps everyone—from demand planners to COOs—act in alignment with company-wide financial goals, ensuring each team’s actions contribute to the bigger picture.

With these five steps, chemical companies can take the data they already have and unlock its potential, turning it into a continuous source of strategic insight and high-value decision-making across the supply chain.

Build an Insights-Driven Supply Chain That Unlocks Value of Your Existing Planning Systems

For executives in chemical companies looking to truly extract value from their supply chain planning systems, the solution doesn’t lie in investing in newer “state-of-the-art” systems or the latest Gen AI capabilities being touted by planning vendors. Instead, it’s about leveraging the data they already have by building capabilities that turn raw information into strategic insights.

By focusing on integrating financial metrics with operational data, embedding predictive analytics, fostering cross-functional collaboration, and establishing a closed-loop feedback mechanism, executives can create a control tower that brings real clarity and impact to their decision-making processes.

A genuinely effective insight-driven supply chain isn’t just about impressive tech or visual dashboards; it’s about surfacing actionable insights and high-impact priorities across the supply chain. By unlocking the hidden value in existing data, chemical companies can move from reactive adjustments to proactive, insight-driven decisions that drive profitability and growth.

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