A BBC Documentary Narrative

Prologue: Dawn Over the City

The city of Abidjan stirs to life as the first rays of sunlight spill across the skyline, glinting off the glass façade of Aboisso Airlines’ headquarters. High above the city’s pulse, the boardroom is already alive with anticipation. Laptops flicker, branded mugs steam with fresh coffee, and the low hum of conversation is punctuated by the clatter of chairs and the shuffle of papers. Today, the air is charged with more than routine: the agenda is nothing less than the transformation of the company’s forecasting process—a change that could redefine the airline’s future.

Around the table, the company’s key players assemble:
Acka, the CEO, is the visionary, always seeking clarity and commitment. Lawrence, the COO and Advisor, is the data-driven facilitator, marker in hand. Amian, Advisor, is the integrator, bridging strategy and execution. John, SVP of Supply Chain, is the pragmatic operationalist. Aïcha, CHRO, is the synthesizer, the voice of structure and process. A supporting cast of analysts and team leads fill out the group.

The whiteboard is already crowded with diagrams, traffic-light pyramids, and acronyms. This is not just another meeting. It is the crucible in which the future of Aboisso Airlines’ supply chain will be forged.

Act 1: Setting the Stage

Opening Moves

The camera pans across the room as Acka commands attention.

“Let’s get to the heart of it. We already have an 18-month demand planning horizon. What’s the gap we’re really trying to close here?”

Lawrence steps forward, animated, marker poised.

“You do, Acka, but the reality is you’re managing execution in a four-week window. We’re suggesting a three-month horizon—more room for decisions, more agility. It’s about giving the business space to maneuver, not just react.”

Aïcha interjects, her tone clarifying.

“So, the model should do the heavy lifting, right? Sales validates, not guesses. We can use off-the-shelf tools for now—Excel, even some AI. The key is collaboration, not just technology.”

Acka raises a skeptical hand.

“How do we get sales to forecast at the SKU and warehouse level? They don’t have that visibility. And even if they did, do they have the tools or the time?”

Lawrence nods.

“That’s exactly the point, Acka. The model should generate the baseline. Sales’ role is to confirm, clean, and validate—not to predict from scratch. We’re not asking for guesses, we’re asking for expertise to refine what the data tells us.”

John leans in, voice practical.

“Even with the best model, if the sales team isn’t engaged, it won’t work. This is a 180-degree turn for them. How do we get buy-in?”

Acka is firm.

“It’s about belief and commitment. We need a new paradigm, not just new tools. If the team doesn’t see the value, nothing will change.”

Act 2: The Anatomy of Forecasting

The Model vs. Reality

Acka’s frustration is palpable.

“Integration planning—what does that even mean? Who’s on this team? What are they supposed to do?”

Lawrence reassures.

“It’s a cross-functional team—supply chain, sales, customer service, analytics. Sean leads, with executive sponsorship from John and myself. Their job: drive the transformation, answer the tough questions, and keep us on track.”

Amian clarifies.

“This is like an M&A integration team. They connect the streams, ensure nothing falls through the cracks. It’s about project-managing the change, not replacing S&OP meetings.”

Acka leans in, voice low.

“If you all left tomorrow, would this go anywhere? I don’t think so. We don’t even know what questions to ask.”

Lawrence is honest.

“KPIs, we can handle. Forecasting? We’ll make some progress, but without a clear US strategy and better sales intelligence, we’ll stall.”

John is thoughtful.

“Maybe we’re asking sales to do things they can’t. We need a sales intelligence team—people who can bridge the gap between market data and planning.”

Lawrence is decisive.

“Exactly. Sales intelligence is a full-time job. It should report to the head of sales, not marketing. And yes, there will be resistance. We need to show results, fast.”

Act 3: The Debate Intensifies

The Challenge of Granularity

Acka presses.

“Let’s get practical. We look at the last three, four weeks, then jump to 18 months. How do we bridge that? And how do we get sales to forecast at the SKU and warehouse level?”

Lawrence explains.

“Right now, you’re colliding forecasting and planning. You’re optimizing for a month, but that’s not enough for a dynamic market like the US. By extending the S&OE window to three months, you get more room to make decisions. The model uses historical data, current orders, and projects volume mathematically. Sales then validate, not guess.”

Aïcha probes.

“But to model, you need technology. What if we don’t have the right tools?”

Lawrence responds.

“You don’t need a perfect system to start. Off-the-shelf tools like Excel can be used temporarily. The key is to get the process right—model, validate, refine. Technology can be upgraded as the process matures.”

Acka asks.

“And when we had SN99 running, did it work?”

John answers.

“Not really. It was a black box, set up for the Canadian market, not the US. The settings didn’t match our new reality.”

Lawrence concludes.

“Exactly. Tools are optimized for specific markets. The US is more dynamic, so the model must be tailored to that volatility. It’s not about the tool—it’s about how you use it and who’s involved.”

Act 4: The Product Mix Puzzle

The Limits of Historical Data

Acka challenges.

“Even if we can forecast total kilos, the product mix is a moving target. How do we forecast at the SKU level when the mix keeps changing?”

Lawrence explains.

“You have to forecast the product mix before you forecast at the SKU level. The model should use historical data to predict the likely mix, but sales input is crucial to adjust for new realities—new customers, new markets, changing demand.”

Aïcha asks.

“So, if we have the data, can’t we just analyze the last few months and project forward?”

Lawrence replies.

“Yes, but only to a point. The model can provide a baseline, but it needs to be validated by those closest to the market. That’s where sales intelligence comes in—bridging the gap between data and reality.”

John questions.

“And if the model is wrong?”

Lawrence is candid.

“Then it’s a learning process. The more cycles you run, the better the model gets. It’s about continuous improvement, not perfection from day one.”

Act 5: The Organizational Challenge

Building the Right Team

Acka asks.

“Who’s on this integration team? What do they actually do?”

Lawrence details.

“It’s a cross-functional team—supply chain, sales, customer service, analytics. Sean leads, with John and myself as executive sponsors. Their job is to drive the transformation, answer tough questions, and keep the process on track.”

Amian adds.

“Think of it like an M&A integration team. They connect the streams, ensure nothing falls through the cracks, and project-manage the change.”

Aïcha clarifies.

“So, we’re not replacing S&OP meetings. This is about managing the change process.”

Acka presses.

“If you all left tomorrow, would this go anywhere?”

Lawrence is realistic.

“Not without help. We need external support to bridge capability gaps and accelerate progress.”

Act 6: The Human Factor

Resistance and Realism

Acka is blunt.

“Let’s be honest. If you all left, nothing would happen. We don’t even know what questions to ask.”

Lawrence agrees.

“KPIs, we can handle. Forecasting? We’ll make some progress, but without a clear US strategy and better sales intelligence, we’ll stall.”

John reflects.

“Maybe we’re asking sales to do things they can’t. We need a sales intelligence team—people who can bridge the gap between market data and planning.”

Lawrence is emphatic.

“Exactly. Sales intelligence is a full-time job. It should report to the head of sales, not marketing. And yes, there will be resistance. We need to show results, fast.”

Amian summarizes.

“We’ve diagnosed the issues. Now we need to design the solution and map the implementation. This isn’t a five-week fix—it’s a journey.”

Lawrence sets expectations.

“Let’s be clear: this is about optimizing cost, growing revenue, and improving service. We need to lay out the benefits, set expectations, and keep everyone engaged.”

John requests.

“Integration planning—pictograms are always good. Send me a one-pager, Lawrence.”

Lawrence winks.

“Expect it in your inbox. And, as always, payment in December.”

Act 7: The Demand Accuracy Wall

Introducing the Demand Accuracy Wall

Lawrence stands, drawing a thick black line on the whiteboard.

“Everyone, let me introduce what I call the ‘Demand Accuracy Wall.’ This is the invisible barrier that separates what we know from what we can only predict. On one side, we have hard data—confirmed orders, contracts, historical trends. On the other, we have uncertainty—market changes, new customer behaviors, competitor moves.”

Aïcha interjects.

“So, the wall is where our models start to break down?”

Lawrence nods.

“Exactly. Up to the wall, our forecasts can be highly accurate because we’re dealing with facts. Beyond it, we rely on assumptions, market intelligence, and scenario planning. The key is to push that wall further out—extend the zone of accuracy as much as possible by integrating better data, smarter models, and real-time market feedback.”

John asks.

“And if we don’t recognize the wall?”

Lawrence warns.

“We risk overpromising, underdelivering, and making costly mistakes. The wall is a reality check. It tells us where to focus our improvement efforts and where to be humble about what we can’t control.”

Acka concludes.

“So, our job is to build ladders over the wall—tools, processes, and teams that help us see further and act faster.”

Lawrence smiles.

“Perfectly said, Acka. That’s the spirit of this transformation.”

Act 8: The Blueprint for Transformation

Immediate Priorities

The team agrees to mobilize a cross-functional program team led by Sean, sponsored by John and Lawrence. The team will include representatives from sales, supply chain, customer service, and analytics. Their mandate: drive the forecasting transformation, ensure integration, and manage change. Roles, responsibilities, and governance for forecasting, planning, and data stewardship are to be clarified. Accountability mechanisms for input quality and process adherence will be established. Collaboration and communication will be enhanced through regular, structured interactions. The “traffic light” maturity assessment will be used to prioritize focus areas. A three-month forecasting horizon pilot will be launched by extending the current S&OE window from 4-6 weeks to 12 weeks. Existing data and off-the-shelf tools (Excel, temporary AI/ML) will be leveraged to model historical trends, product mix, and regional demand.

Medium-Term Actions

The group will design and implement a sales intelligence function reporting to the Head of Sales, build a dedicated team focused on market intelligence, customer insights, and competitive analysis, and integrate this function with demand planning to improve forecast accuracy and responsiveness. Forecasting models and tools will be refined, transitioning from legacy systems to solutions tailored for the US market’s volatility. Models will be developed to forecast at the SKU and warehouse level, not just product group. A capability assessment and training plan will be created to identify skill gaps and provide targeted training or recruitment. KPIs and performance tracking will be established, aligning on definitions and measurement of key metrics such as fill rate, FSFR, and forecast accuracy. KPIs will be used to drive continuous improvement and validate the impact of changes. A detailed roadmap for integrating new processes, systems, and teams will be created, and the “why” and “how” of changes will be communicated to all stakeholders, addressing resistance proactively.

Act 9: The Organizational Implications

Structure

The company expects changes in team composition, with new roles in sales intelligence, analytics, and integration planning.

Culture

Success depends on breaking down silos, fostering open dialogue, and building trust—especially with sales teams historically resistant to change.

Leadership

Executive sponsorship and visible commitment are critical. Leaders must champion the new process and model desired behaviors.

Act 10: The Expected Benefits

Improved forecast accuracy will lead to better alignment of inventory with demand, reducing stockouts and excess inventory. Enhanced customer service will result from more reliable product availability and responsiveness to market changes. Inventory and costs will be optimized, lowering working capital requirements and improving margins. There will be stronger strategic alignment to support growth in the US market with data-driven decisions.

Act 11: The Path Forward

Next Steps & Alignment

By the end of the meeting, all participants agree to form the cross-functional program team immediately. They align on a phased approach: immediate pilots, followed by medium-term system and process changes. There is a commitment to transparent communication and active participation in the change process. The need for external support to bridge capability gaps and accelerate progress is recognized. It is understood that this is a journey—success will require sustained effort, learning, and adaptation.

Epilogue: The Room Settles

The debate has been sharp, the skepticism real, but the path forward is clearer. The whiteboard is now filled with action items, names, and deadlines—a blueprint for transformation. There’s a sense of cautious optimism as the team gathers their things. The journey is just beginning, but the foundation is set.

As the camera lingers on the whiteboard, the voices of the team echo in the empty room—a testament to the power of honest debate, cross-functional collaboration, and the relentless pursuit of excellence.

Character Dynamics: A Closer Look

Acka, the CEO, is the executive anchor, always pushing for clarity, realism, and commitment. Lawrence, the COO and Advisor, is the facilitator, keeping the group focused on data-driven change and cross-functional collaboration. Amian, Advisor, is the integrator, clarifying the need for project management and cross-stream connection. John, SVP Supply Chain, is the pragmatist, grounding the debate in the realities of supply chain execution and the need for tangible results. Aïcha, the CHRO, is the synthesizer, ensuring the group doesn’t lose sight of structure, process, and project management.

The Journey Ahead

Transforming the forecasting process at Aboisso Airlines is both a strategic necessity and a significant opportunity. By following this structured approach—grounded in data, collaboration, and clear accountability—the organization can build a forecasting capability that supports growth, resilience, and customer excellence. The leadership team’s alignment and commitment are the foundation for success.

The debate may have been intense, but it has surfaced the real challenges and the path forward. The journey will be long, but with the right team, tools, and mindset, Aboisso Airlines is poised to lead the way in forecasting excellence.

The Demand Forecast Accuracy Wall: A Shared Roadmap

As the debate in the boardroom intensifies, Aïcha, now clearly introduced as the CHRO, draws everyone’s attention to the slide displayed on the screen—a structured visual of the Demand Forecast Accuracy Wall.

“Before we go any further, let’s ground ourselves in this framework. What you see here is the Demand Forecast Accuracy Wall. It’s not just a metaphor—it’s a practical map of what separates reliable, actionable forecasts from guesswork and noise.”

She gestures to the three colored layers on the slide:

At the foundation, there are the fundamentals—the raw materials of any good forecast. This includes historical data, commercial and market intelligence, macro-economic data, data cleansing, metrics and reports, customer data, competitor insights, and the right forecast level. Without these, nothing above the wall can stand.

John adds, “These are the basics—if we don’t have clean, relevant data, or if we’re missing competitor insights, we’re building on sand.”

Above the fundamentals are the performance drivers. These are the processes and practices that turn data into insight and action. Cross-functional communication, consensus planning, scenario planning, product lifecycle management, collaboration, tracking and measurement, demand sensing, and customer feedback all live here.

Lawrence notes, “This is where the organization’s muscle is built. If we want to push the accuracy wall forward, these drivers are where we need to invest—especially in consensus planning and cross-functional collaboration.”

Finally, at the top, are the excellence drivers. These are the cultural and strategic elements—ownership and responsibilities, business strategy alignment, continuous improvement, S&OP, S&OE, annual forecasting review, and employee training. Excellence drivers are what make the process sustainable and scalable.

Amian concludes, “This is where change sticks. If we don’t align our strategy and ownership, the best tools and data in the world won’t help.”

Aïcha continues, “The wall itself is the line between what we can forecast with confidence and where uncertainty begins. Our job is to keep moving that wall forward—by strengthening our fundamentals, investing in performance drivers, and embedding excellence drivers into our culture.”

Acka sums up, “So, if we’re weak at any layer, the whole structure is at risk. If we want to be world-class, we need to excel at all three levels.”

Lawrence agrees, “Exactly, Acka. And this is why change can’t be siloed. The wall reminds us that forecasting isn’t just a technical problem—it’s organizational, cultural, and strategic. Every function in this room has a role.”

Aïcha, as CHRO, emphasizes, “And as CHRO, I want to stress: employee training and ownership are not optional. If our people don’t understand their role in this wall, or if they’re not equipped to contribute, we’ll never get past the basics.”

John concludes, “Let’s use this wall as our blueprint. For every initiative, we ask: which layer are we strengthening? Where are the gaps? That’s how we’ll make this transformation real.”

The group nods, visibly more aligned. The Demand Forecast Accuracy Wall is now the shared language and roadmap for the journey ahead, anchoring both the ambition and the practical steps required for Aboisso Airlines to lead in forecasting excellence.

Closing Reflections

As the documentary draws to a close, the camera lingers on the now-empty boardroom. The whiteboard, once a battleground of ideas, is now a roadmap for change. The voices of the team echo in the silence—a testament to the power of honest debate, cross-functional collaboration, and the relentless pursuit of excellence.

Aboisso Airlines stands at the threshold of transformation. The journey will be long, the challenges real, but the foundation is set. With clarity of purpose, a shared vision, and a commitment to continuous improvement, the airline is poised to lead the way in forecasting excellence—one decision, one debate, and one breakthrough at a time.

Sources