September 16, 2026
Çağlar ERHAN
• Head Of Ecosystem Development & International Expansion
Following the 2026 World Cup, the start of European competition qualifiers and domestic leagues has once again brought football back to the center of attention. Throughout the summer, transfer news, tactical systems, squad planning, managerial decisions, players expected to leave, emerging academy talents, and many other football operations topics dominated the headlines.
When we were children, sports newspapers would sell in huge numbers during the pre-season period. Today, the same excitement lives on through television programs, YouTube channels, and social media content. In fact, for many football enthusiasts, myself included, the pre-season planning, transfer strategies, and discussions around how clubs are structured can sometimes be even more fascinating than the matches themselves.
While following this year's football news, I noticed an interesting similarity.
The topics that managers and sporting directors focus on before and during the season are actually not that different from the questions supply chain executives deal with every day.
And when artificial intelligence enters the equation, a rather thought-provoking perspective emerges.
Let's take a closer look.
Every football enthusiast knows that a team's success is not determined solely by the eleven players on the pitch. The real story begins long before the season kicks off.
- Which positions should we invest in?
- Which players fit our playing style?
- How should the budget be allocated?
- How deep should the squad be?
- Which players carry a higher risk of injury?
- Which players will be needed for specific matches?
- How should capacity be managed during a congested fixture schedule?
And perhaps the most important question of all:
Should all these decisions be made independently of one another?
If we simply change the terminology, it becomes clear how closely these questions resemble the challenges supply chain leaders face every day.
The Transfer Window Is Essentially a Procurement Process
Before a football club starts a new season, is it simply looking for "good players"?
Or is it first identifying critical needs, evaluating alternatives, analyzing budget constraints, reviewing player performance and injury histories, assessing future potential, and ultimately deciding which players should be added to the squad?
Not to mention the competitive nature of the transfer market itself.
When we look at the business world, we encounter a very similar picture.
Supply chain leaders constantly seek answers to questions such as:
- How much inventory should we hold?
- Which suppliers should we purchase from?
- When should we place orders and under what conditions?
- Which products are likely to experience increasing demand?
- Which inventory items are tying up excessive working capital?
In other words, the common objective of transfer management and procurement management is matching the right resource with the right need.
This is exactly where artificial intelligence plays a critical role. AI does far more than generate forecasts based on historical data. It helps organizations make better procurement and replenishment decisions by evaluating demand forecasts, lead times, costs, service levels, seasonal effects, and alternative scenarios simultaneously.
Just as it is now perfectly natural for football clubs to rely heavily on data analytics, it feels equally natural to see AI-powered decision-making becoming increasingly widespread across supply chains.
Squad Planning = Product Portfolio and Inventory Optimization
A football team having forty players does not necessarily mean it needs a forty-player squad.
Excessive squad size can create burdens in terms of salaries, management complexity, and overall team balance. However, a squad that is too small introduces different risks. A single injury, suspension, or a demanding fixture schedule can disrupt the entire plan.
The same balancing act exists in supply chains.
Excess inventory is expensive. Insufficient inventory is risky.
The goal is not to keep inventory as low as possible. The goal is to maintain the right products, in the right quantities, at the right locations, and at the right time.
Product portfolio decisions can be viewed in a similar way. Trying to offer every product to every customer is like signing multiple players for every position on the pitch. The objective is not to maximize variety. It is to build the optimal squad that satisfies customer needs while supporting commercial objectives.
A Great Manager Is Like an Effective Integrated Planning System
A football manager does not simply answer the question, "Who will play this match?"
They simultaneously consider the strength of the opponent, the form of their own team, upcoming fixtures, players' physical conditions, suspensions, injuries, and season objectives. They understand how today's decisions may impact future performances.
This is also the essence of integrated planning.
Demand plans, supply plans, inventory plans, production plans, capacity constraints, and financial targets are not isolated processes. They are all parts of the same game.
This is where AI-powered planning systems create real value.
Their contribution is not limited to answering, "What will happen?"
They also help answer a far more important question:
"If this happens, what should we do?"
Production Scheduling = Player Rotation
Over the course of a football season, you cannot rely on the same starting eleven for every match.
Player capacity is limited. Fixture schedules are demanding. Certain players perform better in specific matches. Some need rest. Some matches carry greater importance than others.
Manufacturing environments face similar realities.
Machine capacity is finite. Labor availability is limited. Production times vary. Orders have delivery deadlines. Raw material and work-in-progress constraints must be considered.
Every decision impacts another.
Production scheduling is essentially about putting the right player on the field for the right match. The goal is not to maximize resource utilization at all times. The goal is to optimize overall performance.
Replenishment Management = Making Substitutions
Things do not always go according to plan during a football match.
A player may get injured. The opponent may introduce an unexpected tactic. The team may urgently need a goal.
A manager continuously assesses the situation and makes substitutions accordingly.
The same applies to supply chains.
Demand may exceed expectations. A supplier delivery may be delayed. Production capacity may suddenly decrease. A product may begin selling faster than anticipated.
In such situations, a static plan is no longer sufficient.
A high-performing supply chain behaves like a strong football team. It adapts dynamically to changing conditions.
Artificial intelligence supports this agility by evaluating new signals and helping organizations update replenishment decisions faster and more accurately.
Dynamic Pricing = Adapting the Game Plan to the Scoreline
The way a team plays when leading 1-0 is very different from how it plays when trailing 0-1.
Time, score, opponent behavior, and remaining risks all influence decision-making.
Pricing is no different.
Prices do not have to remain static. As demand, inventory levels, competition, seasonality, product lifecycle stage, and customer behavior change, pricing decisions can change as well.
The role of artificial intelligence is to evaluate a large number of variables simultaneously and identify which pricing strategy is most suitable under specific circumstances.
Spare Parts Optimization = Managing the Strength of the Bench
A football team's bench consists of far more than players waiting for an opportunity to enter the game.
What matters is who can step in when a key player gets injured, how versatile those players are, and how the team's performance will be affected by the change.
Spare parts inventory follows a similar logic.
Holding large quantities of every spare part is not economically sustainable. However, the absence of a critical component can halt production or disrupt after-sales services.
As a result, spare parts optimization requires organizations to consider not only consumption rates, but also criticality, failure probability, lead times, service level targets, and inventory costs.
Just as the value of a strong bench comes not from the number of players it contains but from having the right players available.
Route Optimization = Positioning Correctly on the Field
Proper positioning on a football pitch prevents unnecessary running while improving the speed and effectiveness of play.
Logistics faces a similar challenge.
Which vehicle should serve which customers? Which orders should be consolidated? Which route is shorter? How should vehicle capacity be utilized? Which deliveries should take priority?
Among millions of possible combinations, finding the optimal or near-optimal solution manually is extremely difficult.
Artificial intelligence and optimization algorithms can calculate these solutions efficiently and at scale.
Championships Are Not Won with a Single Decision
A football club's transfer budget is not unlimited.
Investing heavily in one star player may mean sacrificing reinforcement in another position.
Therefore, the real question is not:
"Who is the best player?"
It is:
"How can we strengthen the team the most within our limited budget?"
The same principle applies to supply chains.
Should the goal be the highest service level? The lowest inventory cost? Maximum capacity utilization? The fastest delivery times?
In reality, achieving all of these simultaneously is rarely possible.
The true challenge lies in optimizing the trade-offs among competing business objectives.
Considering all these concepts together, it would be incomplete to think of artificial intelligence merely as a chief scout responsible for discovering new talent.
Its real potential emerges when it becomes part of the club's entire decision-making framework: transfers, squad planning, injury risk management, fixture planning, player rotation, match strategy, and real-time in-game decisions.
The same holistic perspective applies to supply chains.
Demand forecasting, inventory optimization, product portfolio management, replenishment, capacity planning, production scheduling, dynamic pricing, route optimization, integrated planning, and spare parts optimization are just some of the key areas involved.
Optimizing each of these individually is important.
But the greatest value is created when they work together as an interconnected system.
Championships are not won through a single decision.
A successful football team does not become champion simply by making transfers. It builds the right squad. It manages its budget. It deploys players at the right moments. It balances capacity throughout the season. It prepares for uncertainty. It adapts its decisions during the game.
And it does all of this with one objective in mind:
To achieve the highest possible performance, deliver on its ambitions, and ultimately become champions.
The objectives of a supply chain are not so different.
Deliver the right product. At the right time. Maintain the right inventory levels. Utilize capacity effectively. Control costs. Use capital efficiently. And above all, adapt quickly to changing conditions.
From our perspective, AI-powered supply chain management is not only about improving the forecasting side of the supply chain. It is also about strengthening its decision-making capabilities.
Because a great supply chain operates much like a great football team:
- The right players.
- The right positions.
- The right budget.
- The right move at the right time.
- And a single game plan that sees the entire season.
Perhaps the most important question in modern supply chains is no longer:
"What will happen?"
But rather:
"Whatever happens, what should we do?"
And that is precisely where artificial intelligence begins to play the role of the true playmaker.