Retailers recognize the importance of good inventory management. Put simply, if products aren’t on the shelf, they can’t be sold. At the other extreme, excessive stockholding represents a serious financial drag. In short, getting inventory management right is never easy. Above all else, it relies on accurate forecasting of future demand and an agile supply chain that ensures the right stock is always available. 

To meet this challenge, retailers now have unprecedented opportunities to harness the power of data science. Low- and no-code platforms are helping retailers glean greater insight from their data and deploy technologies that include machine learning and artificial intelligence (AI) for key tasks like demand forecasting and stock replenishment. But while this new generation of tools is ideally suited to the demands of inventory management, they aren’t a silver bullet on their own. In this article, we outline how retailers can get the best from data science and build more intelligent supply chains that deliver real competitive advantage.  

What is Inventory Turnover?

Inventory turnover is a key metric that all retailers are judged by. Turnover measures the number of times inventory is sold and replaced in a given period (usually a year). More precisely, the inventory turnover ratio is determined by dividing the cost of goods sold (COGS) by the inventory’s average value over the specified timespan. 

• Strong sales and relatively low inventory costs.
• Sufficient inventory to meet demand, without holding stock in excessive volumes and/or for an undue length of time. 
• Short lead times, and therefore high levels of overall customer satisfaction. 

Low inventory turnover ratios suggest the opposite. Sales are slow compared to inventory levels: stock is sitting on the shelf for too long. Retailers with a low inventory turnover ratio are therefore likely to suffer in several areas: 

• Excess stock has a direct impact on the company’s financial results.
• Supply chain bottlenecks may prevent a retailer from acquiring new stock and restrict its ability to respond quickly to changes in consumer behavior.
• Poor inventory management will also undermine support for the business in wider financial markets since investors and industry analysts pay close attention to inventory turnover ratios. 

However, generalizations can be misleading. For example, a high inventory turnover ratio might not tell the whole story. The retailer in question could still be underperforming if a high ratio is masking the fact that there isn’t always enough stock available to fulfill sales requests. In addition to the financial cost of lost sales, the retailer is likely to suffer the reputational damage that comes with disappointed customers. 

On the other hand, a relatively high level of stock may represent a powerful business asset, even if it has a negative impact on a retailer’s inventory turnover ratio. Over the past few years, things like the COVID-19 pandemic, regional conflict, extreme weather, volcanic eruptions, and the infamous blockage of the Suez Canal have all highlighted the fragility of modern supply chains. In many cases, enterprises with higher levels of inventory have been better positioned to maintain sales volumes or fulfill consumer demand when trading conditions return to normal.

The Future of Demand Forecasting

Inventory management is a highly nuanced process. But the fact remains that while no two retailers are the same, accurate demand forecasting is central to any effective inventory management strategy. 

The challenge here should not be underestimated. Demand for products is always likely to be cyclical and influenced by a wide range of factors. Some of the more obvious examples include holidays and shopping events such as Thanksgiving, Christmas, and Black Friday. The weather and wider economic conditions may also play an important role, along with retailers’ internal decisions, including promotional campaigns and pricing. Furthermore, retailers seeking to optimize inventory turnover need to accurately forecast demand for each individual product line or stock keeping unit (SKU) in their range. In some cases, that could run to tens of thousands of SKUs.  

Squeezing More Value from Historic Sales Data

Fortunately, many retailers have a wealth of historical sales data to help guide their demand forecasting. And this is precisely where cutting edge data science tools come into play, making it far easier for retailers to find deep insight within their data and use it as a springboard for better, faster decision-making. 

Altair® RapidMiner®, for example, encompasses a comprehensive range of tools and functionalities that enable significant improvement in inventory turnover ratio. What’s more, the platform’s low- and no-code approach democratizes data science. Creating powerful models and other analytical tools has never been easier. A rich suite of visualization capabilities also allows a diverse array of users to identify the trends and patterns that are crucial for accurate demand forecasting and supply chain optimization.  

Key Features and Benefits for Inventory Management

This new generation of data science platform offers numerous opportunities for retailers to optimize their inventory turnover. These include:

Analyzing Historical Sales Data for Deeper Insight into Inventory Turnover Performance

Retailers can quickly identify factors that impact demand and seasonal and cyclical patterns. Insight is delivered right down to the level of individual SKUs, enabling slow- and fast-moving items to be pinpointed, along with their specific demand patterns.  As a result, retailers develop a far better understanding of past inventory performance and can use it to inform future decisions.

Employing Predictive Analytics for More Accurate Demand Forecasting

Retailers can apply the power of predictive analytics to forecast future demand quickly and accurately. Typically, a retailer’s historic data contains a wealth of information on the trends, patterns and variables that impact sales. Predictive analytics identifies and uses the patterns within, and relationships between, these hidden variables to create data-driven forecasts that support better decision-making. Retailers can plan their inventory levels with far greater confidence and minimize the risk of both stockouts and overstocking. 

Analyzing Supply Chain Performance to Cut Lead Times and Streamline Inventories 

Supply chain performance can be monitored in real time and analyzed to provide deeper insight into lead time performance. Platforms can also optimize supplier relationships and reduce the time needed to replenish stock. 

Utilizing Machine Learning Capabilities to Automate Inventory Replenishment

Sophisticated machine learning models can be built quickly, opening the door to fully automated stock replenishment processes. 

Employing Visualization Tools to Identify and Share Trends and Insights

A wide range of visualization tools within a platform enable everyone within the business to understand and act upon the insights it generates.   

The Best Approach to Better Inventory Management

Significantly, Altair RapidMiner puts this sophisticated functionality within reach of non-specialist users. However, to make the most of a new data science platform for inventory management operations, retailers still need to follow several best practices. These include:

Ensuring Data Quality and Proper Pre-Processing

Accurate inventory turnover analysis demands clean, reliable data. However, raw data rarely meets these requirements. To maximize the value of the insights and guidance generated by their data science platform, retailers need to invest in effective data cleansing and preparation. Altair offers solutions that automate and accelerate these processes. With them, data is transformed in seconds – regardless of whether it’s structured or unstructured, on premises or in the cloud.  

Adopting a Collaborative Approach

Inventory management is a multidisciplinary challenge. Any new data science tool therefore needs to be understood and embraced by all relevant stakeholders within the business.  Reflecting this, Altair’s groundbreaking Center of Excellence (CoE) provides customers with a clear roadmap for internal collaboration. In a nutshell, the Altair CoE approach recognizes that people, not software, drive transformation. Utilizing established and repeatable processes, the CoE maximizes value by continually empowering, upskilling, and inspiring everyone involved in the data science journey – regardless of their existing skills or experience. 

Taking One Step at a Time

When introducing data science capabilities, the best approach is usually to scale up steadily. By starting with a simple analysis and evaluating the results carefully, retailers give themselves the space necessary to fine-tune models and generate more accurate insights and recommendations over time. This approach also helps to secure buy-in from internal stakeholders by minimizing disruption and demonstrating the ability of data science to deliver business value more quickly than an overambitious “big bang” strategy.   

Committing to Regular Monitoring, Ongoing Improvement, and Measuring KPIs/ROI

The well-worn cliché that “retail is detail” is just as relevant here. Inventory turnover is an endless, ongoing process. Retailers need to monitor inventory management performance continually, including overall return on investment (ROI) and key performance indicators (KPIs).

ROI is essential to evaluate inventory turnover optimization. Businesses can compare cost savings, efficiency improvements, and revenue growth within Altair RapidMiner. KPIs, such as lead times, stockouts and excess inventory, provide insights on the effectiveness of inventory turner initiatives. 

Altair RapidMiner incorporates analytical capabilities to monitor and evaluate KPIs and ROI, providing necessary real-time insights. Businesses use these analytical capabilities to make fully informed decisions and justify further investments in data-driven inventory management. 

Right Time, Right Place 

Predicting the future is never easy. But in terms of optimizing inventory turnover ratios, it’s the challenge that all retailers must address. What’s more, they must do so in an increasingly complex environment. Not so long ago, retail competition often stretched no further than local businesses. Today it extends globally, across multiple channels and operates 24/7. Retail supply chains are equally complicated. All of which means that optimizing inventory turnover has never been more important, or more difficult. In the past, some retailers have tended to treat the process as more art than science. But times are changing, and traditional methods are falling short. In response, an ever-increasing number of retailers are turning to data science. By taking full advantage of the new wave of accessible data science platforms, these enterprises can reimagine their approach to inventory management and consistently meet the age-old retail objective of having the right products in the right place at the right time.   

To learn more about how Altair RapidMiner can enhance retail operations, visit https://altair.com/retail or contact us.