Predictive Analytics: Securing Your Inventory with Smart Forecasting

Predictive analytics plays a pivotal role in inventory security, helping minimise supply chain disruptions and mitigate associated risks. But how can it be effectively implemented at the logistics warehouse level? Assâad Moumen, Supply Chain Manager at Wavestone, sheds light on these challenges.

Is predictive analytics simply about forecasting the future by analysing past data? Not quite. ‘ A forecast is, by its very nature, wrong. Nobody can predict the future ,” clarifies Assâad Moumen.

Definition of Predictive Analysis

In logistics, predictive analysis is used to anticipate demand based on historical data, such as sales or production figures, combined with external variables like inflation and weather trends. “It’s all about comprehending and accurately modelling these factors,” Assâad Moumen explains.

From Naive Forecasting to Advanced AI: The Evolution of Predictive Analysis Methods

One of the traditional methods of predictive analysis, known as “naive forecasting,” involves predicting trends by projecting an average based on past data. “ Historically, this was done in Excel. It can be done without sophisticated software, but it’s limited in terms of the volume of data it can handle. ”. According to Assâad Moumen, naive forecasting “isn’t accurate enough,” much like other standard methods, such as exponential smoothing.

So, how can we improve predictive analysis to make it more effective? “There’s a technique that involves integrating not just sales data but also correlations with external factors.” For example, understanding the relationship between sales and weather patterns. “At this point, we can apply more advanced algorithms, typically AI-driven, to better understand behaviours, seasonality, and trends.”

The arrival of AI has taken the field forward: it enables the creation of more sophisticated models and projections. Gone are the days of relying on Excel; now, we use libraries and logistics prediction algorithms that can be customised. But can we go even further? Assâad Moumen’s answer: “We can also set up forecasts using bespoke tools, drawing on a range of data science techniques.”

Does every supply chain today employ predictive analytics? “This is not necessarily the case,” explains Assâad Moumen. “ It’s not because they’re lagging behind, but rather because the business of some companies does not depend on predictive analysis. The aeronautics industry, for example, works on a firm order book that can last up to 10 years: in this case, there’s no need for statistical analysis to anticipate demand. ’

However, for most industries, predictive analytics has become essential to addressing typical logistical challenges. It helps to “avoid issues that are almost always present in the logistics chain.” Key advantages include:

• Preventing overstocking or stockouts, as well as supply issues.
• Anticipating resource needs to meet demand, such as factories and transport capacity.
• Enabling better raw material contract negotiations.
• Allocating budgets more accurately for new product launches.

Predictive analytics impacts inventory management based on stock categories:

• Safety Stock: Maintained to buffer against unpredictable demand shifts, where predictive analysis helps secure optimal stock levels. In this case, predictive analysis is used to “secure a level of variation,” says Assâad Moumen.
• Operational Stock: Driven by shorter-term forecasting, this inventory is critical for daily warehouse operations.

Determining Optimal Stock Levels

For logistics optimisation, predictive analysis is invaluable in setting stock levels that align with forecasted demand. Applied effectively, it minimises replenishment lead times, ensuring rapid response to demand fluctuations.

Strategic Warehouse Planning with Predictive Analysis

With increasing pressures on logistics real estate, predictive analysis is now essential for managing space effectively. “It’s extremely useful in setting out the logistics master plan,” a 3- to 5-year action strategy covering all supply chain flows. How does it help? “It allows us to foresee warehousing needs and tackle the real-estate challenges we face today. By projecting the storage requirements for the medium term, it helps us shape a solid real estate strategy.”

Enhancing Warehouse Ergonomics and Efficiency

How does predictive analysis contribute to logistics optimisation? “We are able to make stock more accessible, allowing operators to work ergonomically and respond faster.” Product placement is designed to suit the workflow, reducing strain on staff and improving overall performance.

To multiply efficiency, two guiding principles stand out: automation and modularity. ‘ How can we better utilise space within a warehouse using new technologies? We can implement new types of shelving that take up less space, or even fully automate or mechanise the warehouse with robots or shelves that can shift based on order requirements. ”
Thus, a complete logistics optimisation solution combines logistics predictive algorithms with automated warehouse management software. Together, these technologies coordinate the intricate movement of robots within the warehouse!

To learn more, discover how our Skypod automated warehouse system optimises warehouse performance.