Inventory management is crucial for organizations to achieve operational efficiency, financial stability, and customer satisfaction to gain a competitive edge in today’s business environment. Proper inventory management helps organizations meet customers’ demands while minimizing costs and reducing the risk of stockouts. Organizations need to leverage data in order to maximize the efficiencies available to them.
However, a lack of knowledge about how to derive data-based insights may lead to suboptimal outcomes. AI analytics can be a factor in unlocking key insights for optimizing inventory.
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Understanding inventory management
Inventory management is the practice of overseeing and controlling the flow of goods and materials.
Inventory is not limited to finished goods (available manufactured goods that are ready for purchase)—it also includes raw materials to be used in the production process and work-in-progress (WIP) goods that are in the process of being manufactured.
For this reason, inventory management is absolutely critical not just for maintaining adequate stock in stores, but also optimizing the production process.
Optimizing inventory management is critical to minimizing potential costs. The carrying cost of holding and storing inventory can be particularly damaging. Holding too much inventory can tie up important capital that other areas of the business might require for investment. In addition, the risk of price decline associated with a drop in the market price can be worrisome for many organizations.
In addition, the risk of inventory depreciation—or the risk that inventory will lose value over time—requires organizations, particularly many consumer goods organizations, to carefully optimize their inventory levels. In addition to carrying costs, ordering costs related to placing and receiving orders—as well as shortage costs from when there’s not enough inventory to meet demand—can add up to potential business losses.
Challenges with inventory management
Walking the fine line between having the right amount of inventory and incurring the costs associated with too much inventory can be extremely challenging. Accurately forecasting demand, inventory variability, poor data quality, lead time variability, and much more make the process of managing inventory complex.
Here are some of the top challenges:
Demand forecasting accuracy
Seasonality, market fluctuations, and sudden changes in consumer preferences can lead to inaccurate demand forecasts. Fluctuations in consumer behavior have been particularly top of mind recently, as the changing economic climate and other unexpected events (like the COVID-19 pandemic) have led to extremely dynamic conditions.
Unexpected events have also led to a serious upheaval of supply chains in recent years. This inventory variability can make it challenging to maintain consistent and optimal inventory levels. In addition, supplier reliability and variability in production may also be factors in inventory management.
Managing a variety of SKUs with various demand patterns, product lifecycles, and supply chain requirements can be extremely challenging. Managing the variety of suppliers for these different products introduces a whole host of problems related to lead times, reliability, and quality control.
For some industries—particularly life sciences and food & beverage—specific requirements and regulations govern how inventory should be handled, tracked, and reported. Compliance may involve specific labeling/tracking standards, stringent quality control and safety standards, inventory valuation standards, and more. Managing these complexities can be burdensome for many organizations.
Balancing inventory carrying costs with the risk of stockouts can be a delicate trade-off. Inventory management involves finding the right equilibrium between minimizing costs and ensuring the availability of products to meet customer demand. Things like managing the right level of safety stock while taking into account the costs of stockouts; maintaining the right level of customer service while incurring carry costs; and finding reductions in lead time variability to decrease the need for safety stock can make inventory management an exceptionally challenging business problem.
These are only some of the top concerns in inventory management. Addressing these challenges requires businesses to adopt efficient inventory management practices, implement analytical solutions, and regularly review and adjust inventory strategies to align with changing market conditions.
Problems with traditional analytical approaches
Traditional approaches to analytics for inventory management rely heavily on understanding past performance to inform future decision-making.
Organizations leverage metrics like the inventory turnover ratio, days sales of inventory (DSI), fill rate, stockout rate, and order accuracy to enable more effective inventory management. Historical analysis on sales, demand patterns, and inventory levels can provide insight into optimizing inventory levels. Order quantity models can help determine optimal orders to minimize the total cost of inventory. Leveraging safety stock based on historical data and service level targets can be used as a buffer against unexpected fluctuations in demand or supply chain disruptions.
While traditional analytical approaches that have been used for decades have offered some insight into inventory management best practices, they have limitations. Many traditional models assume predictable demand, and as we’ve seen in the past few years, demand can be anything but predictable. Real-world variability and seasonality must be taken into account in order to more accurately forecast future demand.
In addition, traditional approaches may lack adaptability to dynamic market conditions, unexpected demand spikes, or supply chain disruptions. These approaches may also only offer a limited consideration of the many variables associated with the supply chain, such as supplier performance, transportation costs, or production constraints.
Traditional analytical tools can have limited capabilities, imposing constraints on effectively optimizing inventory. Bringing together data from a variety of supply chain-related data sets may be difficult or impossible with many analytical tools. Integrating valuable third-party data from weather providers or market research firms may also be burdensome. These tools also typically do not have the ability to analyze data in real time, limiting the value of these data points.
In addition, many tools lack anomaly detection and alerting, delaying notification to key stakeholders when problems arise. Because of the technical complexity of using many traditional analytics tools, many stakeholders may not be able to draw insights from the data in order to more effectively manage inventory.
Traditional analytics tools also do not have the predictive analytical capabilities necessary to take inventory management optimization to the next level. The lack of ML-based algorithms in traditional analytical tools may only allow for shallow insights that do not augment decision-making.
AI analytics for optimizing inventory
AI analytics can help significantly improve inventory management analytics by introducing advanced capabilities to enhance accuracy, adaptability, and efficiency.
With AI analytics, businesses can become more agile and data-driven with their inventory management processes. AI analytics helps organizations achieve better outcomes through capabilities including machine learning (ML) models, advanced insight generation capabilities, real-time supply chain data, and predictive analytics.
The first step on a journey toward optimizing inventory is ensuring a full view into all available data.
Unlike traditional analytics, AI-enabled analytics provides a window into the full suite of available data resources, including internal and external data from suppliers, weather data, and market research firms.
With enhanced data processing capabilities and connectivity, AI analytics provides a real-time view into data from IoT devices and sensors, online applications, data warehouses, and data lakes. Enhanced data processing capabilities also allow for quick and easy insight generation from terabytes to petabytes of data.
AI-powered demand forecasting
With all of this data available at an organization’s fingertips, they can improve demand forecasting with AI-powered ML algorithms.
The ability to incorporate not only historical data but also market trends and external factors can help generate more accurate and adaptive demand forecasts. ML models in AI analytics can identify complex patterns and adjust forecasts in real time as conditions change.
In addition, this holistic view of data will dramatically improve analysis of demand variability, lead time variability, and other supply chain factors, leading to a more efficient calculation of safety stock.
Supplier performance and risk monitoring
AI-enabled predictive analytics unlocks more opportunities for inventory optimization. With the power of AI, predictive analytics can be used to monitor supplier performance and assess risk factors that may affect lead times or product quality. Supplier scorecards can be created to evaluate supplier performance based on various KPIs like on-time delivery-rates, quality control, lead times, and cost-effectiveness.
Furthermore, predictive models can be used to estimate the lead time needed for orders to be delivered, more effectively reducing the likelihood of stockouts from delivery delays. These models can also be used to recognize seasonal demand patterns, allowing for adjustments in inventory levels.
By incorporating demand elasticity and competitive pricing data, organizations can use predictive models to optimize pricing strategies to help manage inventory more effectively.
Tellius for inventory optimization
Tellius offers the ability to leverage AI analytics for inventory management across industries. The platform combines the best of traditional analytics platforms with the power of AI to unlock more advanced analytical capabilities.
Tellius empowers inventory managers, supply chain managers, purchasing and procurement teams, sales, marketing, and executives with the tools to unlock data insights for inventory optimization.
With a “Google-like” natural language search for ad hoc data exploration, inventory management stakeholders can simply ask questions of their data. Automated insights highlight key data points and allow for deep drill-downs into the data to understand why metrics are changing. Anomaly detection and alerting allows everyone to keep updated on the data as a situation changes. Meanwhile, AutoML capabilities extend advanced analytical analysis to more personnel across business units.
Go here to learn how Tellius helps organizations achieve more supply chain efficiency, or sign up here for a demo.