According to Fortune Business Insights, the global inventory management software market, valued at $1.98 billion in 2022, will more than double by 2030 and reach $4.05 billion. One of the reasons for such growth is the continuous shift from manual to automated custom inventory management, as the latter is more efficient for modern, often distributed, businesses.
Adopting a custom tool for inventory management is one of the ways to automate critical business tasks, from controlling stock levels to handling orders and sales, resulting in optimized company performance. Companies can also enhance their custom software with AI capabilities to accelerate inventory management transformation and gain additional business advantages.
This article highlights four reasons to empower a custom inventory management tool with AI technology:
1. More accurate demand forecasting
Companies should prevent inventory shortage and surplus as both can negatively affect business performance and service quality, and implementing AI-powered demand forecasting can help solve this challenge. The reason is that AI and its sub-branches, such as machine learning (ML) and deep learning (DL), help companies generate more accurate forecasts, leading to more intelligent inventory control.
Depending on its business specifics and needs, a company can implement an AI-based solution based on various demand forecasting algorithms.
ARIMA
ARIMA (AutoRegressive Integrated Moving Average) is an ML algorithm that combines autoregressive and moving average models to draw forecasts based on historical data. ARIMA is widely used among retailers, relying on it to predict product sales and adjust inventory in advance. However, since the ARIMA model does not take into account temporal trends during the analysis, it might not be suitable for the accurate prediction of seasonal sales spikes.
SARIMA
SARIMA (Seasonal AutoRegressive Integrated Moving Average) is a more advanced version of ARIMA, which additionally takes into account seasonality when analyzing historical data. A retailer can use SARIMA to predict seasonal demand increases for certain goods to optimize inventory before a peak occurs. In turn, a manufacturer can use the SARIMA algorithm to generate ML-based models that can help optimize and improve production plans continuously.
Gradient Boosting
Gradient Boosting is another statistical analysis technique that involves the sequential use of multiple ML models, typically representing decision trees, each of which learns from the mistakes of the previous one. The ability to classify data, high prediction accuracy, and advanced training speed are the main advantages of the Gradient Boosting technique. However, it is worth remembering that training several ML models can be resource-intensive, so Gradient Boosting is not a one-size-fits-all solution either.
2. Improved procurement and supplier management
Many companies are equipping their inventory management solutions with sourcing and procurement functionalities to generate purchase orders and communicate with suppliers centrally, which in turn helps replenish inventory more quickly. Companies can automate and streamline most procurement and supplier management tasks using AI and warehouse management system, resulting in even more advanced business performance.
For example, an AI-powered inventory management tool can automatically generate requests for quotes (RFQ) and request for proposals (RFP) and send them to suppliers based on information about current inventory levels and forecasts with sales trends. When properly configured, ML models can help a company continuously optimize its inventory with little human intervention. This enables in-house inventory management specialists to focus on other critical tasks, from managing shipments to running inventory audits.
In addition, if a software solution is equipped with ML, it can provide procurement teams with recommendations to help select better suppliers. First, AI can analyze supplier profiles, their bidding history, financial health, and other parameters to help companies build long-term and reliable supplier partnerships.
Secondly, AI helps manage supplier risks more efficiently. For example, the system can analyze supplier actions to identify those repeatedly late with delivering on their contracts. After the analysis, a company can consider switching to other suppliers to avoid supply chain disruptions in the future.
In addition, AI can study supplier data (such as transaction history) to help procurement staff tailor their negotiation strategies, which helps build stronger partner relationships and achieve more favorable contract terms. Finally, AI can collect, classify, and analyze procurement spending data, providing recommendations with cost-saving opportunities, such as restructuring existing supplier agreements.
3. Intelligent inventory allocation and storage
AI-based systems can evaluate each product based on its external characteristics, including weight and size, as well as assess it regarding customer demand to recommend the most optimal storage options to a company’s specialists. For example, if equipped with proper functionality, such a system can suggest warehouses located closer to potential customers, enabling companies to streamline their supply chains, manufacturing of custom product boxes, speed up product delivery, and increase customer satisfaction.
4. Enhanced quality control
AI-enabled custom inventory management systems can track the condition of items (including raw materials and SKUs) at rest and in motion, helping companies monitor the safety and health of each inventory unit.
For example, artificial intelligence algorithms can analyze data from IoT sensors installed in containers with perishable goods (these sensors monitor environmental temperature, pressure, and humidity) to promptly identify quality problems and prevent defective products from reaching customers. AI-enabled IoT systems allow companies to monitor the condition of their machinery and equipment and maintain it proactively.
Final thoughts
Companies develop and adopt custom inventory management tools to optimize various business aspects, from inventory tracking to auditing and distribution. By equipping their custom software with AI, companies can gain additional business benefits, including more accurate demand forecasting, automated procurement, and optimized resource allocation.
However, developing AI-powered custom software can be challenging, as it requires specific knowledge and experience to make intelligent algorithms work correctly. One the easiest ways to address this issue is to engage third-party software developers with AI proficiency. The experts can develop a robust custom inventory management tool and supplement it with AI functionality, tailored to a company’s unique inventory management goals.
