The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds.  Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs.

This blog explores how the latest AI-driven technologies can transform the OEM aftermarket by analyzing large datasets to predict future demand more accurately, optimize inventory levels, enhance forecasting accuracy, and improve customer satisfaction, ultimately leading to better service and lower costs.

 

Enhancing Forecast Accuracy with AI  

Using state-of-the-art technology, organizations can significantly enhance forecast accuracy by analyzing historical data, recognizing patterns, and predicting future demand. Our latest (IP&O) Inventory Planning &Optimization technology uses AI to provide real-time insights and automate decision-making processes. It employs adaptive forecasting techniques to ensure forecasts remain relevant as market conditions change. The system integrates advanced algorithms to manage intermittent data and make real-time modifications while handling complex calculations and considering factors like lead times, forecast errors, seasonality, and market trends. By leveraging better data inputs and advanced analytics, companies can significantly reduce forecast errors and minimize the costs associated with overstocking and stockouts.  Our IP&O platform is designed to handle the complexities and challenges unique to service parts management, such as intermittent demand and large assortments of parts.

Repair and Return Module: The platform accurately simulates the processes of part breakdown and repair. It predicts downtime, service levels, and inventory costs associated with the current rotating spare parts pool. Planners will know how many spares to stock to achieve short- and long-term service level requirements and, in operational settings, whether to wait for repairs to be completed and returned to service or to purchase additional service spares from suppliers, avoiding unnecessary buying and equipment downtime.

 Intermittent Demand Forecasting: IP&O’s patented intermittent demand forecasting technology provides highly accurate forecasts for items with sporadic demand patterns typical in the aftermarket. This capability is crucial for optimizing inventory levels and ensuring that critical parts are available when needed without overstocking.

Real-Time Inventory Optimization: Our technology dynamically adjusts inventory policies to align with changing demand patterns and market conditions. It calculates optimal reorder points and order quantities, balancing service levels with inventory costs. This ensures that OEMs can maintain high service levels while minimizing excess inventory and related carrying costs.

Scenario Planning and What-If Analysis: IP&O allows users to create multiple inventory scenarios to evaluate the impact of different inventory policies on service levels and costs. This capability helps OEMs make informed decisions about stocking strategies and respond proactively to market changes or supply chain disruptions.

Seamless ERP Integration: The platform offers seamless integration with leading ERP systems, such as Epicor and NetSuite, enabling automatic synchronization of forecasts and inventory data. This integration facilitates efficient execution of replenishment orders and ensures that inventory levels are continually aligned with the latest demand forecasts.

Forecast Accuracy and Reporting:  Our Advanced System provides detailed reporting and dashboards that track forecast accuracy, inventory performance, and supplier reliability. By analyzing these metrics, OEMs can continually refine their forecasting models and improve overall supply chain performance.

 

Real-world examples illustrate the substantial impact of AI-driven Forecasting and Inventory Optimization in the OEM aftermarket.  Prevost Parts, a division of a leading Canadian manufacturer of intercity buses and coach shells, used IP&O to address the intermittent demand of over 25,000 active parts. By integrating accurate sales forecasts and safety stock requirements into their ERP system, supported by AI and real-time machine learning adjustments, they reduced backorders by 65%, lost sales by 59%, and increased fill rates from 93% to 96% in just three months. This transformation significantly improved their inventory allocation, reducing transportation and inventory costs​​.

 

Incorporating AI and ML into IP&O processes is not just a technological upgrade but a strategic move that can transform the OEM aftermarket. IP&O  technology ensures better service quality and customer satisfaction by improving forecast accuracy, optimizing inventory levels, and reducing costs. As the aftermarket sector continues to grow and evolve, embracing AI will be key to staying competitive and meeting customer expectations efficiently.

 

 

In this blog, we will analyze the automatic forecasting for time series demand projections, focusing on key techniques, challenges, and best practices. There are multiple methods to predict future demand for an item, and this becomes complex when dealing with thousands of items, each requiring a different forecasting technique due to their unique demand patterns. Some items have stable demand, others trend upwards or downwards, and some exhibit seasonality. Selecting the right method for each item can be overwhelming. Here, we’ll explore how automatic forecasting simplifies this process.

Automatic forecasting becomes fundamental in managing large-scale demand projections. With thousands of items, manually selecting a forecasting method for each is impractical. Automatic forecasting uses software to make these decisions, ensuring accuracy and efficiency in the forecasting process. It’s importance lies in its ability to handle complex, large-scale forecasting needs efficiently. It eliminates the need for manual selection, saving time and reducing errors. This approach is particularly beneficial in environments with diverse demand patterns, where each item may require a different forecasting method.

 

Key Considerations for Effective Forecasting

1. Challenges of Manual Forecasting:
   • Infeasibility: Manually choosing forecasting methods for thousands of items is unmanageable.
   • Inconsistency: Human error can lead to inconsistent and inaccurate forecasts.
2. Criteria for Method Selection:
   • Error Measurement: The primary criterion for selecting a forecasting method is the typical forecast error, defined as the difference between predicted and actual values. This error is averaged over the forecast horizon (e.g., monthly forecasts over a year).
   • Holdout Analysis: This technique simulates the process of waiting for a year to elapse by hiding some historical data, making forecasts, and then revealing the hidden data to compute errors. This helps in choosing the best method in real-time.
3. Forecasting Tournament:
   • Method Comparison: Different methods compete to forecast each item, with the method producing the lowest average error winning.
   • Parameter Tuning: Each method is tested with various parameters to find the optimal settings. For example, simple exponential smoothing may be tried with different weighting factors.

 

The Algorithms Behind Effective Automatic Forecasting

Automatic forecasting is highly computational but feasible with modern technology. The process involves:

• Data Segmentation: Dividing historical data into segments helps manage and leverage different aspects of historical data for more accurate forecasting. For instance, for a product with seasonal demand, data might be segmented by seasons to capture season-specific trends and patterns. This segmentation allows forecasters to make and test forecasts more effectively.
• Repeated Simulations: Using sliding simulations involves repeatedly testing and refining forecasts over different periods. This method validates the accuracy of forecasting methods by applying them to different segments of data. An example is the sliding window method, where a fixed-size window moves across the time series data, generating forecasts for each position to evaluate performance.
• Parameter Optimization: Parameter optimization involves trying multiple variants of each forecasting method to find the best-performing one. By adjusting parameters, such as the smoothing factor in exponential smoothing methods or the number of past observations in ARIMA models, forecasters can fine-tune models to improve performance.

For instance, in our software, we allow various forecasting methods to compete for the best performance on a given item.  Knowledge of Automatic forecasting immediately carries over to Simple Moving Average, linear moving average, Single Exponential Smoothing, Double Exponential Smoothing, Winters’ Exponential Smoothing, and Promo forecasting. This competition ensures that the most suitable method is selected based on empirical evidence, not subjective judgment. The tournament winner is the closest method to predicting new data values from old. Accuracy is measured by average absolute error (that is, the average error, ignoring any minus signs). The average is computed over a set of forecasts, each using a portion of the data, in a process known as sliding simulation, which we have explained previously in a previous blog.

 

Methods used in Automatic forecasting

Normally, there are six extrapolative forecasting methods competing in the Automatic forecasting tournament:

• Simple moving average
• Linear moving average
• Single exponential smoothing
• Double exponential smoothing
• Additive version of Winters’ exponential smoothing
• Multiplicative version of Winters’ exponential smoothing

The latter two methods are appropriate for seasonal series; however, they are automatically excluded from the tournament if there are fewer than two full seasonal cycles of data (for example, fewer than 24 periods of monthly data or eight periods of quarterly data). These six classical, smoothing-based methods have proven themselves to be easy to understand, easy to compute and accurate. You can exclude any of these methods from the tournament if you have a preference for some of the competitors and not others.

Automatic forecasting for time series data is essential for managing large-scale demand projections efficiently and accurately. Businesses can achieve better forecast accuracy and streamline their planning processes by automating the selection of forecasting methods and utilizing techniques like holdout analysis and forecasting tournaments. Embracing these advanced forecasting techniques ensures that businesses stay ahead in dynamic market environments, making informed decisions based on reliable data projections.

 

 

 

Forecast-based inventory management, or MRP (Material Requirements Planning) logic, is a forward-planning methodology for managing inventory. This method ensures that businesses can meet demand without overstocking, which ties up capital, or understocking, which can lead to lost sales and dissatisfied customers.

By anticipating demand and adjusting inventory levels accordingly, this approach helps maintain the right balance between having enough stock to meet customer needs and minimizing excess inventory costs. Businesses can optimize operations, reduce waste, and improve customer satisfaction by predicting future needs. Let’s break down how this works.

 

Core Concepts of Forecast-Based Inventory Management

Inventory Dynamics Models: Inventory dynamics models are fundamental to understanding and managing inventory levels. The simplest model, known as the “sawtooth” model, demonstrates inventory levels decreasing with demand and replenishing just in time. However, real-world scenarios often require more sophisticated models. By incorporating stochastic elements and variability, such as Monte Carlo simulations, businesses can account for random fluctuations in demand and lead time, providing a more realistic forecast of inventory levels.

IP&O platform enhances inventory dynamics modeling through advanced data analytics and simulation capabilities. By leveraging AI and machine learning algorithms, our IP&O platform can predict demand patterns more accurately, adjusting models in real time based on the latest data. This leads to more precise inventory levels, reducing the risk of stockouts and overstocking.

Determining Order Quantity and Timing: Effective inventory management requires knowing when and how much to order. This involves forecasting future demand and calculating the lead time for replenishing stock. By predicting when inventory will hit safety stock levels, businesses can plan their orders to ensure continuous supply.

Our latest tools excel at optimizing order quantities and timing by utilizing predictive analytics and AI. These systems can analyze vast amounts of data, including historical sales and market trends. By doing so, they provide more accurate demand forecasts and optimize reorder points, ensuring inventory is replenished just in time without excess.

Calculating Lead Time: Lead time is the period from placing an order to receiving the stock. It varies based on the availability of components. For example, if a product is assembled from multiple components, the lead time will be determined by the component with the longest lead time.

Smart AI-driven solutions enhance lead time calculation by integrating with supply chain management systems. These systems track supplier performance, and historical lead times, to provide more accurate lead time estimates. Additionally, smart technologies can alert businesses to potential delays, allowing for proactive adjustments to inventory plans.

Safety Stock Calculation: Safety stock acts as a buffer to protect against variability in demand and supply. Calculating safety stock involves analyzing demand variability and setting a stock level that covers most potential scenarios, thus minimizing the risk of stockouts.

IP&O technology significantly improves safety stock calculation through advanced analytics. By continuously monitoring demand patterns and supply chain variables, smart systems can dynamically adjust safety stock levels. Machine learning algorithms can predict demand spikes or drops and adjust safety stock accordingly, ensuring optimal inventory levels while minimizing holding costs.

The Importance of Accurate Forecasting in Inventory Management

Accurate forecasting is key for minimizing forecast errors, which can lead to excess inventory or stockouts. Techniques such as utilizing historical data, enhancing data inputs, and applying advanced forecasting methods help achieve better accuracy. Forecast errors can have significant financial implications: over-forecasting results in excess inventory while under-forecasting leads to missed sales opportunities. Managing these errors through systematic tracking and adjusting forecasting methods is crucial for maintaining optimal inventory levels.

Safety stock ensures that businesses meet customer needs even if actual demand deviates from the forecast. This cushion protects against unforeseen demand spikes or delays in replenishment. Accurate forecasting, effective error management, and strategic use of safety stock enhance forecast-based inventory management. Companies can understand inventory dynamics, determine the right order quantities and timing, calculate accurate lead times, and set appropriate safety stock levels.

Using state-of-the-art technology like IP&O provides significant advantages by offering real-time data insights, predictive analytics, and adaptive models. This leads to more efficient inventory management, reduced costs, and improved customer satisfaction. Overall, IP&O empowers businesses to plan better and respond swiftly to market changes, ensuring they maintain the right inventory balance to meet customer needs without incurring unnecessary costs.

 

 

In this blog, we will explore how organizations can achieve exceptional efficiency and accuracy with AI-driven inventory optimization. Traditional inventory management methods often fall short due to their reactive nature and reliance on manual processes. Maintaining optimal inventory levels is fundamental for meeting customer demand while minimizing costs. The introduction of AI-driven inventory optimization can significantly reduce the burden of manual processes, providing relief to supply chain managers from tedious tasks. With AI, we can predict demand more accurately, reduce excess stock, avoid stockouts, and ultimately improve our organization’s bottom line. Let’s explore how this approach not only boosts sales and operational efficiency but also elevates customer satisfaction by ensuring products are always available when needed.

 

Insights for Improved Decision-Making in Inventory Management

1. Enhanced Forecast Accuracy Advanced Machine Learning algorithms analyze historical data to identify patterns that humans might miss. Techniques like clustering, regime change detection, anomaly detection, and regression analysis provide deep insights into data. Measuring forecast error is essential for refining forecast models; for example, techniques like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) help quantify the accuracy of forecasts. Businesses can improve accuracy by continuously monitoring and adjusting forecasts based on these error metrics. As the Demand Planner at a Hardware Retailer stated, “With the improvements to our forecasts and inventory planning that Smart Software enabled, we have been able to reduce safety stock by 20% while also reducing stock-outs by 35%.”

2. Real-Time Data Analysis State-of-the-art systems can process vast amounts of data in real time, allowing businesses to adjust their inventory levels dynamically based on current demand trends and market conditions. Anomaly detection algorithms can automatically identify and correct sudden spikes or drops in demand, ensuring that the forecasts remain accurate. A notable success story comes from Smart IP&O, which enabled one company to reduce inventory by 20% while maintaining service levels by continuously analyzing real-time data and adjusting forecasts accordingly. FedEx Tech’s Manager of Materials highlighted, “Whatever the request, we need to meet our next-day service commitment – Smart enables us to risk adjust our inventory to be sure we have the products and parts on hand to achieve the service levels our customers require.”

3. Improved Supply Chain Efficiency Intelligent technology platforms can optimize the entire supply chain, from procurement to distribution, by predicting lead times and optimizing order quantities. This reduces the risk of overstocking and understocking. For instance, using forecast-based inventory management, Smart Software helped a manufacturer streamline its supply chain, reducing lead times by 15% and enhancing overall efficiency. The VP of Operations at Procon Pump stated, “One of the things I like about this new tool… is that I can evaluate the consequences of inventory stocking decisions before I implement them.”

4. Enhanced Decision-Making AI provides actionable insights and recommendations, enabling managers to make informed decisions. This includes identifying slow-moving items, forecasting future demand, and optimizing stock levels. Regression analysis, for example, can relate sales to external variables like seasonality or economic indicators, providing a deeper understanding of demand drivers. One of Smart Software’s clients reported a significant improvement in decision-making processes, resulting in a 30% increase in service levels while reducing excess inventory by 15%. “Smart IP&O enabled us to model demand at each stocking location and, using service level-driven planning, determine how much to stock to achieve the service level we require,” noted the Purchasing Manager at Seneca Companies.

5. Cost Reduction By optimizing inventory levels, businesses can reduce holding costs and minimize losses from obsolete or expired products. AI-driven systems also reduce the need for manual inventory checks, saving time and labor costs. A recent case study shows how implementing Inventory Planning & Optimization (IP&O) was accomplished within 90 days of project start. Over the ensuing six months, IP&O enabled the adjustment of stocking parameters for several thousand items, resulting in inventory reductions of $9.0 million while sustaining target service levels.

 

By leveraging advanced algorithms and real-time data analysis, businesses can maintain optimal inventory levels and enhance their overall supply chain performance. Inventory Planning & Optimization (IP&O) is a powerful tool that can help your organization achieve these goals. Incorporating state-of-the-art inventory optimization into your organization can lead to significant improvements in efficiency, cost reduction, and customer satisfaction.

 

 

 

Inventory optimization software that supports what-if analysis will expose the tradeoff of stockouts vs. excess costs of varying service level targets. But first it is important to identify how “service levels” is interpreted, measured, and reported. This will avoid miscommunication and the false sense of security that can develop when less stringent definitions are used.  Clearly defining how service level is calculated puts all stakeholders on the same page. This facilitates better decision-making.

There are many differences in what companies mean when they cite their “service levels.”  This can vary from company to company and even from department to department within a company.  Here are two examples:

 

1. Service level measured “from the shelf” vs. a customer-quoted lead time.
   Service level measured “from the shelf” means the percentage of units ordered that are immediately available from stock. However, when a customer places an order, it is often not shipped immediately. Customer service or sales will quote when the order will be shipped. If the customer is OK with the promised ship date and the order is shipped by that date, then service level is considered to have been met.  Service levels will clearly be higher when calculated over a customer quoted lead time vs. “from the shelf.”

2. Service level measured over fixed vs. variable customer quoted lead time.
   High service levels are often skewed because customer-quoted lead times are later adjusted to allow nearly every order to be filled “on time and in full.” This happens when the initial lead time can’t be met, but the customer agrees to take the order later, and the customer quoted lead time field that is used to track service level is adjusted by sales or customer service.

Clarifying how “service levels” are defined, measured, and reported is essential for aligning organizations and enhancing decision-making, resulting in more effective inventory management practices.