Article
For years, downstream leaders have pursued digital transformation. Yet many still find themselves asking: Where is the step-change in performance? Despite significant investment in tools and pilots, the ambition of an intelligent, self-optimizing downstream system remains largely unrealized. Even among leading global firms there is no off-the-shelf solution that integrates real-time monitoring, optimization, and decision support across the full value chain.
The next wave of value in downstream won’t come from more tools but rather from integration: replacing fragmented systems with coherent digital architectures, aligning control room decisions with commercial strategy, and empowering people—not just platforms—to act on insight.
What makes this so hard?
Refining and downstream operations are inherently complex. Building a truly intelligent digital system means overcoming technical, organizational, and commercial barriers. Historian data, advanced process control (APC), manufacturing execution systems (MES), planning tools, and spreadsheets rarely speak to each other in real time, and software often varies across a portfolio. Most APC and real-time optimization (RTO) solutions operate at the unit or asset level, limiting the ability to connect and optimize across the full value chain. Proofs of concept often fail to scale beyond single assets or functions due to data quality issues, model drift, weak integration, or sponsor fatigue.
Tensions between commercial and operational priorities further complicate things. Operations tend to prioritize safety and stability, while commercial teams push for price realization or, ideally, margin improvement. Without a neutral, central optimization unit, these perspectives often clash rather than converge.
Meanwhile, legacy constraints and outdated assumptions often get locked into the planning process, limiting flexibility and innovation. Feedback loops are frequently absent or ineffective, leading to repetition of poor inputs and failure to capitalize on potential lessons learned. And, despite growing investment in digital, organizations often fail to adjust decision rights, governance, and accountability mechanisms to match, leaving different stakeholders with divergent objectives.
What good looks like
Our work with clients reveals that successful digital enablement in downstream is grounded in seven core traits.
- It begins with an integrated data architecture that brings together historian, APC, energy trading and risk management (ETRM), enterprise resource planning (ERP), and external signals into a unified knowledge graph.
- It requires a digital twin of the full value chain, not just individual assets. This twin simulates multiple scenarios of refining, logistics, trading, and customer systems from end to end, enabling planning amid uncertainty and identifying pinch points in real time.
- Optimization must be dynamic and decision-relevant. Rather than optimize a static case, leading systems incorporate reinforcement learning and surrogate linear programming (LP) models to continuously re-optimize as conditions change, keeping in mind switching costs and impacts to physical systems.
- Best-in-class sales and operations planning (S&OP) systems connect seamlessly to execution, allowing decisions to update weekly, daily, or even intra-day as constraints or opportunities shift or macro events unfold.
- Governance must be crisp. A clearly defined operating model is essential, including a separate optimization unit that can frequently challenge constraints, pilot new models, and balance trade-offs between commercial, operational, and logistical priorities.
- Organizations must standardize the concepts and data they rely on—demand and price projections, market elasticities, turnaround schedules, yield curves, tankage availability, etc.—so that LP models can accurately incorporate this information.
- Successful efforts embed post-mortems, performance reviews, and learning cadences to continuously improve how decisions get made.
A future-back approach
The most ambitious digital leaders don’t start with a dashboard—they start with a destination. Their North Star is a system that is always optimized and always anticipating. Planning is no longer a monthly ritual but a continuous process that incorporates learning and feedback cycles. LPs are not run in isolation but integrated and orchestrated across the value chain. AI augments planners and traders, recommending actions before the market shifts—and explaining why—so humans in the loop have full authority, data access, and rationale.
Imagine an integrated value chain twin that spans crude selection, refining and petchem, logistics, marketing, trading, and end customers. Plans update as new information arrives, not weeks later but in minutes, making the most of the system even considering the existing physical restrictions, so that changes are implementable and value accretive. AI engines nowcast demand, detect arbitrage windows, and simulate supply disruptions before they hit. The planner becomes a supervisor of the system—not a spreadsheet jockey—intervening where human judgment is needed and validating machine-generated scenarios.
Assessing Your Full Potential: Four Key Areas of Focus
Assessing Your Full Potential: Four Key Areas of Focus
Pricing and spread scenarios
- Crude pricing and crack spread fluctuations
- Oil products pricing
Boundaries definition
- Market forecast and rational allocation
- Base case and sensitivities
- Operational, logistical, and tankage commercial constraints
Optimization
- Productivity and reliability improvement
- Production optimization (LP model)
- Optimize margins and reduce risk
Review
- Refine average product price
- Final validation
The potential gains are substantial. Planning cycles can shrink by 50% to 80%. Gross margin can improve by 1% to 3% through better slate selection and pre-emptive positioning. Inventory levels can fall while service and schedule adherence improve. Losses from demurrage, off-specs, and changeovers decline materially. This is not speculative—early movers are already capturing portions of this value.
The transformation doesn’t need to be implemented as a “big bang,” given that improvements can be released in steps, leveraging a test-and-learn approach, unlocking value early and progressively in the process.
Typical pitfalls (and how to avoid them)
Many digital efforts stumble because they start with tools rather than the decisions that matter. Without a clear view of which value levers to pull, the initiative becomes a tech upgrade rather than a business transformation. Others optimize only for steady-state performance, ignoring the volatility and complexity of real-world operations. A system that doesn’t flex isn’t truly intelligent.
Governance failures are equally common. When decision rights aren’t clear—or worse, when they are duplicated across silos—coordination breaks down. Many organizations run multiple LPs in parallel, without an orchestration layer to align them. And even where good tools exist, they often go unused because they aren’t embedded in workflows or backed by incentives.
Finally, too few efforts build in structured feedback loops. Without post-mortems and constraint validation, planning processes degrade into inertial copy-paste exercises, with no accountability for the inputs and outcomes. Constraints are carried forward without challenge. Key performance indicators (KPIs) get misaligned across functions. Time and effort are spent optimizing plans that target the wrong outcomes, fail to consider new inputs, or challenge its own constraints. These issues are not technical—they are leadership and execution problems.
Leaders can begin to turn things around by taking three steps:
- Prioritize for value: Identify flagship use cases—integrated LP solutions, crude selection, market anticipation, arbitrage capture.
- Assess readiness and challenge inputs: Evaluate systems, data, decision rights, and constraint realism. Implement feedback loop.
- Build a future-back roadmap: Define the stack: unified data layer, model library, orchestration engine, digital twin, decision cockpit. Start where value and feasibility meet.
How we can help
In the past decade Bain has helped Energy and Natural Resources leaders to drive transformations, optimize their supply chain, and implement state-of-the-art S&OP models.
We bring a strategy-first approach to digital enablement, rooted in client context, focused on value creation, and designed for use. Rather than starting with tools, we begin with a clear understanding of where value lies and build an architecture that your organization can actually adopt. Our aim is to create systems that remain flexible as markets evolve, and usable as organizational needs shift.
We understand the technical complexity behind these transformations and the real-life challenges of running crude, refining, and petchem operations. Our deep experience with operationally intensive sectors allows us to design solutions that work not just in theory but also in live, high-stakes environments.
We also understand how technology is evolving and unlocking pioneering solutions once beyond reach, at a pace not seen before. Our AI, Insights, and Solutions team routinely delivers custom-built digital platforms and AI-enabled optimizations designed to work with messy, real-world data. These are not black boxes; they are decision engines designed to make the right thing easy to do.
We also know we can’t—and shouldn’t—do it all alone. That’s why we work with a network of ecosystem partners, including some of the leading software and analytics providers in this space. These partnerships let us scale quickly, bring the best of what the market offers, and apply our unique expertise where it matters most.
Finally, we make sure your change effort takes hold by emphasizing Results Delivery®. Digital transformations only succeed when people embrace new ways of working. We collaborate with you to embed change, build conviction, and ensure that new tools don’t simply generate answers but shape better decisions.
The Bottom Line
Digital enablement in downstream seemed to have hit a plateau. Now, technology is creating new opportunities for innovation. The tools are available, but integration, intelligence, and impact remain elusive. A future-back approach—grounded in business logic and delivered through cross-functional execution—can change that. Leading companies will move from reacting to anticipating, from monthly to continuous, and from disconnected systems to intelligent interconnected operations. And they’ll do it with governance and clarity, not just code. This is not just another hype. The time is now; we’re here to help.