M. Erman Türkkan
Food and Feed Industrial Performance & Profit Optimization Consultant
AI-powered analytics is making
inventory management in feed mills more predictable and efficient, helping to
ease cost pressures. Despite the sector’s scale in Türkiye, profitability
remains constrained by currency risk and import dependency, increasing the need
for data-driven decision-making. Leveraging existing ERP data through the right
analytical layer is emerging as a critical lever to strengthen both operational
and financial performance.
From raw material inventories to procurement operations, from operations managers to top management, data is now the most valuable raw material of a feed mill. Türkiye ranks first among EU member states with 29.3 million tons of compound feed production, and seventh globally. (Source: TÜRKİYEM-BİR, December 2025) This scale creates significant pressure on raw materials. Nearly half of the raw materials used in the sector are imported; while critical inputs such as soybean meal, corn, and wheat are priced in dollars, compound feeds are sold in Turkish lira. According to TÜRKİYEM-BİR’s statements, this structural currency risk has led to the sector not having a profitable year in 2025 despite production growth. (Source: TÜRKİYEM-BİR, December 2025)
A PROVEN GLOBAL IMPACT
The impact of artificial intelligence in the food and feed processing sector is also documented with concrete data in global markets. In the international food and beverage market, the size of AI applications was approximately $8.5 billion in 2023 and is expected to expand at an annual growth rate of 39% during the 2024–2030 period. (Source: Grand View Research, 2024, IFT.org)
In inventory management specifically, McKinsey research shows that companies using AI-powered demand forecasting reduce forecasting errors by 20–50% and decrease inventory requirements by up to 30%. (Source: McKinsey & Company, AI-Powered Demand Forecasting Study) The AI-powered inventory management market is also projected to reach $30 billion by 2030. (Source: The Business Research Company, AI in Inventory Management Report, 2024)
According to TÜİK (Turkstat) data shared by İTO President Şekib Avdagiç at the March 2026 “Digitalization and Artificial Intelligence: Producing Value from Data” program, the rate of AI usage in enterprises in Türkiye is still at 7.5%; in large-scale manufacturing companies, this rate exceeds 20%. If the feed sector completes this transition early, it can significantly strengthen its competitive advantage. (Source: Istanbul Chamber of Commerce, March 2026)
MAKING SENSE OF DATA
The vast majority of feed mills in the sector use accounting-oriented ERP platforms. These systems are powerful tools for financial record-keeping. However, when it comes to operational analyses such as inventory aging, anomaly detection, supplier performance, and cost deviations, they fall short of providing sufficient insights. Data is generated, yet it is not transformed into information.
In fact, the large flow of information arising from daily operations such as Production, Procurement and Inventory Management, Accounting, Finance, Logistics, etc. brings along dozens of workloads. This data is tracked by relevant departments and operational teams within certain habits, standards, and rules. However, among many tasks that require attention and concentration, some overlooked issues and human errors can lead to various cost losses for the business.
Within this challenging landscape, a critical question emerges: Why is existing data, already present in ERP systems, Excel sheets, and warehouse records, still not being transformed into decision support? The answer lies not in technological inadequacy but in the lack of a proper analytical layer.
The AI-powered analytics layer fills this gap. Raw data from ERP (SKU-based inventory movements, purchase invoice records, technical material consumption) is systematically processed and interpreted, turning into intelligent reports that feed both operational and financial decision-making processes. This has become essential for building a sustainable competitive structure.
THE ROLE OF AI IN RAW MATERIAL
AND TECHNICAL MATERIAL INVENTORIES
In a feed mill, when a raw material SKU remains inactive for 90 days, it ceases to be merely a warehouse issue and becomes a financial burden: spoilage risk, financing cost, and opportunity cost all at once. The AI-powered aging analysis system continuously scans all inventory items and sends automatic alerts to responsible personnel for products exceeding critical day thresholds (30/60/90 days). This enables proactive action instead of manual checks.
ABC-XYZ analysis plays a complementary role here. By combining turnover-based importance classification (ABC) with demand stability (XYZ), it clearly reveals how frequently and in what quantities each raw material should be procured. This matrix optimizes both procurement planning and warehouse space allocation.
TECHNICAL MATERIAL AND MRO (Maintenance,
Repair, and Operations) MANAGEMENT: THE HIDDEN COST ITEM
Technical items such as spare parts, maintenance materials, packaging, and auxiliary supplies often remain in the shadow of primary production inventories. However, surplus or shortages in these categories directly affect production continuity. AI-powered systems model consumption rates, seasonal patterns, and criticality levels of technical materials, automating shelf-life and consumption forecasts.
Beyond this, duplicate record detection (instances where the same product is entered into the system under different codes) reveals “hidden inventory” within the system. Such data quality issues distort purchasing decisions, leading to unnecessary purchases and budget waste.
This allows procurement teams to shift from urgent purchasing to planned, price-optimized procurement. Net Realizable Value (NRV) analysis identifies inventory valued below cost, providing a basis for accounting adjustments and realistic valuation decisions.
IMPLICATIONS FOR ACCOUNTING
AND FINANCE DEPARTMENTS
The most direct contribution of AI-powered inventory analytics to accounting and finance teams lies in data reliability. During period-end inventory valuation, negative balances, duplicate records, or unrealistic cost discrepancies are detected and corrected in advance through a systematic control layer. Closing cycles are shortened, and audit preparation becomes easier.
FIFO-based cost tracking makes it possible to calculate the impact of older inventory on costs over each period. This data not only improves the accuracy of periodic P&L statements but also enables early anticipation of NRV provisions. Cost deviations between periods decrease, and budget forecasts become closer to reality.
FOR SENIOR MANAGEMENT: COST
TRANSPARENCY AND SUSTAINABLE COMPETITIVENESS
From a top management perspective, the most critical output of AI-powered analytics is cost transparency. Real-time detection of inventory costs and anomalies in ERP records ensures that strategic decisions are built on a much more solid foundation in an environment where cost deviations between periods are minimized.
This transparency and precision generate competitive strength in three dimensions:
- Pricing power: When true inventory costs are known, sales prices can be calibrated to preserve profitability margins.
- Risk management: Raw material positions exposed to currency risk can be monitored in real time, supporting hedging or early procurement decisions.
- Investment analysis: Enterprises that clearly see their net costs are better positioned to make forward-looking decisions.
Defining data as a raw material and positioning artificial intelligence as a strategic partner at the board level, these are the first two strategic steps of AI transformation in business. (Source: İTO Artificial Intelligence Transformation Strategy, March 2026)
CONCLUSION: THE DATA IS
ALREADY THERE
As Türkiye’s feed sector maintains its leadership in the EU and its seventh position globally, it must simultaneously face raw material import dependency, currency risk, and increasing competitive pressure. In this environment, operational excellence is no longer an option but a necessity.
The good news is that there is no need to start from scratch for transformation. The data already exists in ERPs, invoice archives, and warehouse records. What is missing is an intelligent analytical layer that can process, interpret, and turn this data into action. Feed mills that build this layer strengthen decision-making across all levels from operations teams to accounting units, from procurement desks to the boardroom, while increasing cost transparency and minimizing deviations between periods.
Globally, the AI market in food processing is projected to reach $138 billion by 2034. The impact of this wave on the feed sector is inevitable. The question is no longer “should we adopt artificial intelligence?” but rather “how much earlier we adopt it, the stronger we become.” (Source: Towards FnB, AI in Food Processing Market Report, 2025)
Related:
From sensors to silos: How AI is promising an autonomous future for feed production