Challenges in Inventory Management Analysis

Today’s companies, operating in a dynamic market environment, face many challenges related to inventory management. Effective management of this area is key to ensuring operational liquidity, minimising costs and maximising profits. One tool that supports this process is indicator analysis. Despite its potential, there are many difficulties in building an effective indicator analysis system for inventory management.

Challenges in building an effective indicator analysis

1. Data complexity Inventory management generates huge amounts of data, including information on orders, deliveries, stock levels or seasonality. Transforming this data into useful information requires sophisticated analytical systems and properly trained staff.
2. Lack of standardisation Different companies, or even departments within the same organisation, may use different indicators and methods of analysis. Lack of standardisation leads to difficulties in comparing results and making accurate decisions.
3. Systems integration Inventory data often comes from multiple sources, such as ERP, SCM or CRM systems. Integrating them into a unified analytical system can be costly and time-consuming.
4. Changing market conditions Market dynamics require flexibility in indicator analysis. Companies must be ready to quickly adapt their analysis model to changing conditions, such as changes in demand or supply chain disruptions.
5. Decision-making Even the most precise indicators do not guarantee success if they are not properly interpreted. Decision-makers must be competent enough to make accurate decisions based on indicators.

Summary

Effective indicator analysis in inventory management is a key element of the operational strategy of today’s companies. Although the process presents many challenges, such as systems integration and market volatility, the right indicators allow companies to make better decisions, optimise costs and improve customer satisfaction. The proper use of analytical tools and indicators is the key to successful inventory management in an increasingly demanding business world.

DATURE ENTERPRISE software uses artificial intelligence and machine learning in the process of demand forecasting and inventory optimization. The Dature system allows information about the supply chain to be collected and analysed and used to calculate optimal stock control parameters. The system provides indicators that measure the effectiveness of stock management from various points of view, so that the current and future situation of the company can be properly assessed.

The system provides methods for forecasting seasonal demand and demand influenced by calendar days. Inventory management methods allow for both pre-season inventory building approaches, dynamic safety stock control and JIT.

TheDATURE application ENTERPRISE can also use expertise in the demand forecasting process. Authorized users can enter expert forecasts and adjust statistical forecasts with them. The process is fully auditable in terms of who changed the forecast when and how. This makes it possible to track the accuracy of both statistical and expert forecasts. As a result, the organization learns how to forecast more accurately and improve process efficiency.