Published on

November 24, 2023


Harnessing ML for Optimal Inventory Control

Discover the power of machine learning for inventory control. Automate management & solve problems for enhanced inventory management.
Julia Dunlea
VP of Marketing

Inventory control has long been a critical aspect of business operations, affecting profitability, customer satisfaction, and overall efficiency. With the ever-increasing complexity of global markets and unpredictable consumer demands, the traditional methods of inventory management no longer suffice.

In today's fast-paced and competitive marketplace, companies are turning to innovative technologies to gain a competitive edge. Among these technologies, machine learning (ML) stands out as a powerful tool for optimizing inventory management.

Gone are the days of relying solely on manual calculations and guesswork to determine inventory levels. ML, a branch in the artificial intelligence (AI) realm, empowers businesses to make data-driven decisions by leveraging advanced algorithms that learn from historical data and predict future demand patterns. 

This transformative technology can enable your organization to streamline its inventory control processes, minimize costs, and maximize customer satisfaction.

In this article, we delve into how machine learning can be incorporated within your business to revolutionize your inventory management practices and ultimately drive growth. 

What are the limitations of inventory management?

Inventory management can be integral to your business operations and revenue. However, conventional inventory control faces some limitations that can impact its effectiveness, including:

  • Forecasting demand: Accurately predicting customer demand can be difficult, especially in industries with rapidly changing trends or when introducing new products. The challenge with forecasting is not only predicting customer demand but establishing a meaningful confidence interval on the demand. For example, two predictions with the same average estimate of 3,000 units may represent vastly different scenarios for inventory optimization, either ranging from 2,900 to 3,100 units or from 2,000 to 4,000 units.
  • Managing inventory levels: Balancing inventory levels is a delicate task because carrying excess inventory ties up working capital and incurs holding costs, while insufficient inventory leads to stockouts and dissatisfied customers.
  • Inventory shrinkage: Inventory shrinkage refers to losses caused by theft, damage, or administrative errors. This can occur in retail stores where shoplifting or internal theft reduces inventory levels, resulting in inaccurate stock records and potential stockouts.
  • Seasonal demand: Businesses operating in industries with seasonal fluctuations face inventory management challenges. For instance, a toy manufacturer may need to manage inventory levels to meet increased demand during the holiday season while avoiding excessive inventory during the rest of the year. 
  • Supply chain disruptions: Disruptions in the supply chain, such as delays in raw material delivery, transport troubles, or unforeseen events like natural disasters, can impact inventory management. For example, a smartphone manufacturer experiencing delays in receiving crucial electronic components will not be able to meet customer demand or profit.
  • Cost management: Inventory management involves various costs, including holding costs, ordering costs, and obsolescence costs. For instance, a distributor of perishable goods must carefully manage expiry dates to minimize obsolescence costs.
  • High operational and logistics costs: Managing inventory involves operational and logistics expenses such as storage, handling, and transportation costs. This can be troublesome for businesses with extensive product lines or geographically dispersed operations, such as an eCommerce company with multiple fulfillment centers, requiring efficient inventory allocation and optimized shipping routes to control operational costs. To explore effective strategies for reducing these costs and enhancing your business's efficiency, you can use a route optimization software.

As you can see, the shortcomings of manual inventory administration make it a must to optimize inventory control. That’s where machine learning comes in!

You can leverage ML’s advanced algorithms and approaches to streamline your inventory management processes, reducing costs and improving overall efficiency.

Machine learning vs conventional inventory control software

Traditionally, businesses have relied on conventional software to handle their inventory operations. However, with the advent of machine learning, a new and advanced approach to inventory control has emerged.

But what are the differences between them?

Conventional inventory control software primarily relies on predefined rules and simple statistical time series models (like ARMA, ARIMA, etc.) to manage inventory and estimate future demand. 

This approach can be effective in certain scenarios with stable and predictable demand patterns to perform a variety of tasks, including:

  • Automating inventory tracking to reduce the risk of overstocking or understocking.
  • Providing real-time visibility to enable the making of informed decisions based on accurate data.
  • Streamlining processes by automating manual processes, improving efficiency, and reducing errors.
  • Reducing inventory shrinkage by providing better visibility into inventory movement, reducing the risk of theft, damage, and obsolescence.

However, the limitations of conventional inventory control software become apparent when faced with dynamic and volatile demand situations. 

The basic statistical methods it uses are far less powerful than ML models, which is why it may struggle to capture complex patterns or adapt quickly to changes in customer behavior or market trends. 

Traditional inventory control software also has the following shortcomings compared to AI approaches:

  • Limited predictive analytics: It can provide demand forecasting, but ML platforms can use more advanced algorithms to predict demand patterns and identify hidden correlations like understanding what is driving customer demand. This may help identify quiet periods when the business should offer discounts to drive demand.
  • Inability to learn from data: Inventory management software can improve over time based on user feedback, but it cannot learn from data in the same way that ML can. ML algorithms can learn from their mistakes and iteratively improve predictive power.
  • Limited scalability: It can handle a certain amount of data and complexity, but it may struggle with large volumes or highly complex inventory systems.
  • Inability to handle unstructured data: It relies on structured data, while ML can handle unstructured data, such as social media posts or customer reviews, to provide deeper insights.

Machine learning leverages advanced algorithms and techniques to analyze large volumes of data, identify patterns, and make accurate predictions. 

Its algorithms can detect nonlinear relationships, incorporate external factors (such as weather patterns or social media trends), and adjust forecasts in real time based on changing market conditions. This will enable businesses to improve demand forecasting accuracy and optimize inventory levels accordingly. 

The benefits of ML for inventory control

  • ML is capable of learning and adapting from data inputs, which makes them more dynamic and flexible compared to conventional inventory control software, especially in demand forecasting and optimization. 
  • It can handle complex demand patterns, identify trends, and adjust forecasts based on multiple variables and constraints. 
  • These AI algorithms enable businesses to determine optimal reorder points, safety stock levels, and inventory allocation strategies – resulting in more precise inventory management decisions, enhanced supply chain management, optimization of costs, and a reduction in the risk of stockouts or excess inventory.

That being said, it is important to note that implementing ML to manage your inventory requires robust data collection and integration processes. 

ML algorithms rely on high-quality and diverse datasets for accurate predictions. You’ll need to ensure that you have access to relevant and comprehensive data sources to train your ML models effectively so that you can get accurate results. 

Incorporating ML may also require skilled data scientists or analysts to develop and maintain the models, which will come at additional costs and resources.

Here is a comparative table of the two approaches for a quick overview

Features Machine learning Conventional software
Demand forecasting Analyzes diverse data points for accurate forecasts. Relies on historical data and basic statistical methods.
Real-time insight Detects patterns and anomalies for proactive actions. Limited ability to identify supply chain disruptions.
Dynamic adaptability Adjusts reorder points and quantities based on data. Relies on static reorder points and predetermined quantities.
Data requirements Requires robust data infrastructure and quality inputs. Relies on structured data inputs.
Optimization capabilities Optimizes inventory levels and reduces carrying costs. Provides basic functionalities for stock tracking and purchase orders.
Complexity handling Handles complex and rapidly changing business environments. Suitable for simpler inventory management needs.
Customer satisfaction Minimizes stockouts and overstocks for improved customer satisfaction. May result in stockouts or excess inventory, leading to customer dissatisfaction.
Scalability Can handle large volumes of data and adapt to growing business needs. May struggle to scale effectively with increasing data and business complexity.
Advancements and future scope Continually evolving and integrating new technologies and methodologies. May have limited scope for incorporating advanced techniques and emerging trends.

While conventional inventory control software remains valuable in certain contexts, organizations seeking to optimize their inventory management should explore the possibilities presented by machine learning-driven solutions.

Ultimately, the choice between the two approaches depends on your specific business needs and the complexities of your supply chain.

Applications of machine learning in inventory control

Machine learning offers a wide range of applications in inventory control, revolutionizing how businesses manage their inventory and optimize operational efficiency. Let's explore some specific examples of how machine learning can be applied in inventory control:

1. Demand forecasting

ML can analyze historical sales data, market trends, and external factors to accurately forecast customer demand. For instance, an eCommerce retailer can use ML to analyze past sales patterns, customer behavior, website traffic, and even external data like social media trends or weather conditions. 

By considering these variables, ML algorithms can generate more accurate demand forecasts, enabling businesses to optimize inventory levels, reduce stockouts, and avoid excess inventory.

2. Inventory optimization 

AI algorithms can help businesses determine the optimal inventory levels for different products. By considering factors like lead time, seasonality, and cost constraints, ML algorithms can identify the right balance between carrying costs and stockouts. 

For example, a consumer electronics company can use ML to analyze historical data, production cycles, and sales forecasts to optimize inventory levels, ensuring that they have enough stock to meet customer demand while minimizing excess inventory and associated costs.

3. Minimizing downtime by predicting maintenance with analytics

By using sensor data and historical maintenance records, ML can predict equipment failure or maintenance needs. For instance, a manufacturer can use ML to monitor sensor data from production machinery, analyzing patterns and detecting anomalies that may indicate potential breakdowns. This will encourage the manufacturer to schedule preventive maintenance to minimize unplanned downtime and reduce the risk of stockouts or production disruptions.

Similarly, an airline company can use ML to monitor aircraft engine data, identifying patterns that may indicate potential failures or performance degradation. By predicting maintenance needs in advance, the airline can proactively schedule maintenance activities, minimize downtime, and reduce the risk of unexpected equipment failures.

4. Predictive technologies for reduced waste

This branch of AI can optimize inventory levels and minimize waste, especially in industries with perishable or short shelf-life products. 

For example, a grocery store can use ML algorithms to analyze product expiration dates, customer demand patterns, and historical sales data. By considering these factors, the store can optimize stock levels, ensuring that they have enough fresh products on the shelves while minimizing waste due to product expiration.

Alongside predicting a range of customer demands, ML algorithms can output a probability that’s associated with different levels of demand. To clarify this, let's consider an example:

Imagine you want to forecast the number of purchases you might receive for a particular product. Instead of providing a single estimate, the ML algorithm can output a range of probabilities to indicate the likelihood of observing a certain number of purchases or fewer, such as:

  • A 20% chance of experiencing 2,000 purchases or fewer.
  • A 60% chance of observing 3,000 purchases or fewer.

Probabilities, like the examples above, provide valuable insights into the potential outcomes of customer demand.

5. Supplier management

Machine learning can enhance supplier selection, performance monitoring, and risk assessment. ML algorithms can analyze supplier data, including delivery times, product quality, and pricing, to identify the most reliable and cost-effective suppliers. 

For example, an automotive manufacturer can use ML to analyze supplier performance metrics, such as on-time delivery rates and defect rates, to optimize supplier selection and ensure a smooth supply chain flow.

Inventory control with Akkio: A leading ML platform

Akkio is a powerful predictive AI platform designed for analysts, which offers all the tools and capabilities to harness the power of machine learning in inventory control. It’s a no-code AI solution that’s bound to bolster your business operations.

Your business can quickly gain insights from consumer behavior data and effortlessly create accurate forecasts by using Akkio. With its user-friendly interface, which makes it particularly attractive for businesses new to machine learning, Akkio enables you to create custom ML models trained on your own datasets, integrate them with existing data sources, and deliver live reports wherever needed.

Akkio boasts two extra helpful features: the Chat Data Prep feature and the Chat Explore feature.

The Chat Data Prep feature will enable you to easily transform your data.

The Chat Explore feature will provide quick insights about your data, making the analysis process seamless and efficient.

Akkio's ML models can be applied to various aspects of inventory management, focusing on three primary functions:

1. Prediction

Prediction will help your business utilize historical data to predict future events to make informed decisions about production schedules, enhance supply chain management, and create ideal pricing strategies. 

Akkio’s prediction models can forecast the demand for specific products or raw materials so you can optimize your inventory levels and avoid the pitfalls of overstocking or understocking.

An example of Akkio’s prediction models.
An example of Akkio’s prediction models.

2. Forecasting

Akkio goes beyond simple prediction by employing sophisticated algorithms to analyze data and generate highly accurate forecasts. These forecasting models will help your organization predict trends and changes in demand, as well as identify potential disruptions in the supply chain. 

This is particularly beneficial if you’re in an industry with highly variable demand, such as retail or consumer goods, where accurate forecasting is crucial for inventory optimization and resource allocation.

3. Anomaly detection

Akkio's ML algorithms can detect anomalies by identifying patterns in data that deviate from expected norms., which is vital for recognizing unusual demand patterns or supply chain irregularities. 

It will help you identify unexpected surges in demand or delays in delivery, enabling timely corrective actions to minimize disruptions and optimize inventory levels.

An example of Akkio’s anomaly detection models.

Even better, using Akkio is a straightforward process. Once you select the desired function (prediction, forecasting, or anomaly detection) and specify the target fields, Akkio will automatically select the optimal ML model and handle the rest of the process for you.

Try a demand forecasting model in real time

See how you can elevate your business with ML using Akkio! Simply upload your datasets in this real-time demand forecasting model below, and try Akkio's powerful forecasting AI platform for yourself.

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Optimize your inventory control with Akkio

Inventory control is a complex process with challenges such as demand forecasting, inventory accuracy, cost management, and supply chain disruptions. 

While conventional inventory control software offers benefits like automation, real-time visibility, and streamlined processes, machine learning platforms like Akkio provide additional advantages that will significantly boost your inventory operations.

Akkio enables your business to create powerful ML models that address various aspects of inventory management. It offers more accurate demand forecasting, inventory optimization, and the ability to adapt to changing market conditions. It can also handle unstructured data, which will provide deeper insights for informed decision-making.

Overcome the obstacles of inventory management by harnessing Akkio's machine learning models to unlock the power of machine learning for enhanced inventory operations, today!

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