Inventory overstocking can harm both financial stability and operational efficiency. When an organization is overstocked, it ties up capital in excess inventory that might not sell, increasing storage costs and the risk of inventory obsolescence. Additionally, the funds used to purchase the excess inventory could have been better invested in other areas of the business, such as marketing or research and development. Overstocking also hampers cash flow, as money is locked in stock rather than available for immediate operational needs. Managing inventory effectively is critical for maintaining a healthy balance sheet and ensuring that resources are optimally allocated. Here is an in-depth exploration of the main causes of overstocking, their implications, and possible solutions.

1 Inaccurate Demand Forecasting

One of the primary causes of overstocking is inaccurate demand forecasting. When businesses rely on outdated forecasting methods or insufficient data, they can easily overestimate demand, leading to overstocking. A prime example is the clothing industry, where fashion trends can change rapidly. A well-known fashion brand recently faced challenges after overestimating demand for a new clothing line based on flawed data analysis, leading to unsold inventory.

To address this issue, companies can implement new technologies that automatically select the best forecasting methods for the data, incorporating trends and seasonal patterns to ensure accuracy. By improving forecasting accuracy, businesses can better align their inventory with actual demand, leading to more precise inventory management and fewer overstock scenarios. For instance, a Hardware retailer using Smart Demand Planner reduced forecasting errors by 15%, demonstrating the potential for significant improvement in inventory management​​​​.

2 Improper Inventory Management

Effective inventory management is fundamental to prevent overstocking. Without accurate systems to track inventory levels, businesses might order excess stock and incur higher expenses. This issue often stems from reliance on spreadsheets or inefficient ERP systems that lack real-time data integration.

State-of-the-art technologies provide real-time visibility into inventory levels, allowing businesses to automate and optimize reordering processes.  A large electric utility company faced challenges in maintaining service parts availability without overstocking, managing over 250,000 part numbers across a diverse network of power generation and distribution facilities. The company replaced its outdated system with Smart IP&O and integrated it in real-time with their Enterprise Asset Management (EAM) system. Smart IP&O enabled the utility to use “what-if” scenarios, creating digital twins of alternate stocking policies and simulating performance across key performance indicators, such as inventory value, service levels, fill rates, and shortage costs. This allowed the utility to make targeted adjustments to their stocking parameters, which were then deployed to their EAM system, driving optimal replenishments of spare parts.

The outcome was significant: a $9 million reduction in inventory, freeing up cash and valuable warehouse space while sustaining target service levels of over 99%​

3 Overly Optimistic Sales Projections

Businesses, especially those in growth phases, may predict higher sales than they achieve, leading to excess inventory intended to meet anticipated demand that never materializes. An example of this is the recent case with an electric vehicle manufacturer that projected high sales for its truck but faced production delays and lower-than-expected demand, resulting in an overstock of components and parts. This miscalculation led to increased storage costs and strained financial resources.

Another automotive aftermarket company struggled to forecast intermittently demanded parts accurately, frequently resulting in overstocking and stockouts.  Using AI-driven technology enabled the company to significantly reduce backorders and lost sales, with fill rates improving from 93% to 96% within just three months. By leveraging Smart IP&O forecasting technologies, the company could generate accurate estimates of cumulative demand over lead times, providing better visibility of potential demand scenarios. This allowed for optimized inventory levels, reducing storage costs and improving financial efficiency by aligning inventory with actual demand​.

4 Bulk Purchasing Discounts

The appeal of cost savings from bulk purchases can prompt businesses to buy more than needed, tying up capital and storage space. This often leads to storage challenges when excess stock is ordered to secure a discount.

To address this challenge, businesses should weigh the benefits of bulk discounts against the costs of holding excess inventory. Next-generation technology can help identify the most cost-effective purchasing strategy by balancing immediate savings with long-term storage costs. By implementing Smart IP&O, MNR could accurately forecast inventory requirements and optimize its inventory management processes. This led to an 8% reduction in parts inventory, reaching a high customer service level of 98.7% and reducing inventory growth for new equipment from a projected 10% to only 6%.

5 Seasonal Demand Fluctuations

Difficulty in aligning inventory with seasonal demand can result in surplus stock once the peak sales period ends. Toy manufacturers, for example, might produce too many holiday-themed toys only to face low demand after the holidays. The fashion industry frequently experiences similar challenges, with certain styles becoming obsolete as seasons change. The latest technologies can help businesses anticipate seasonal demand shifts and adjust inventory levels accordingly. By analyzing past sales data and predicting future trends, businesses can better prepare for seasonal fluctuations, minimize overstocking risk, and improve inventory turnover.

6 Supplier Lead Time Variability

Unreliable supplier lead times can lead to overstocking as a buffer against delays. If lead times improve or demand decreases unexpectedly, businesses may have excess inventory. For example, an auto parts distributor might stockpile components to mitigate supplier delays, only to find lead times improving suddenly.

Advanced technology can help by providing real-time data and predictive analytics to manage lead time variability better. These tools allow companies to dynamically adjust their orders, reducing the need for excessive safety stock.

7 Inadequate Inventory Policies

Outdated or incorrect inventory policies, such as faulty Min/Max settings, can lead to over-ordering.  However, using Modern technology to regularly review and update inventory policies ensures they align with current business needs and market conditions. By keeping policies up-to-date, businesses can reduce the risk of overstocking due to procedural errors. A recent case study demonstrated how a major retailer used Smart IP&O to revise inventory policies, resulting in a 15% reduction in overstock​​.

8 Promotions and Marketing Campaigns

Misalignment between marketing efforts and actual customer demand can cause businesses to overestimate the impact of promotions, resulting in unsold inventory. For example, a cosmetics company might overproduce a limited edition product, expecting high demand that doesn’t materialize. Leveraging Smart IP&O can help align marketing initiatives with realistic demand expectations, avoiding excess stock. By integrating marketing plans with demand forecasts, businesses can optimize their promotional strategies to better match actual customer interest.

9 Fear of Stockouts

Companies often maintain higher inventory levels to avoid stockouts, which can lead to lost sales and unhappy customers. This fear can drive businesses to overstock as a safety net, especially in industries where customer satisfaction and retention are crucial. A notable example comes from a large retail chain that significantly increased its inventory of household goods to avoid stockouts. While this strategy initially helped meet customer demand, it later resulted in excess inventory as consumer purchasing patterns stabilized. This overstocking contributed to a profit drop of nearly 90% in the second quarter, largely due to markdowns and the clearing of excess stock.

To mitigate such situations, businesses can utilize advanced inventory planning and optimization tools to provide accurate demand forecasts. For instance, a leading electronics manufacturer used Smart IP&O solution to reduce inventory levels by 20% without impacting service levels, effectively reducing costs while maintaining customer satisfaction by ensuring they had the right amount of stock on hand​​​​.

10 Overcompensation for Supply Chain Issues

Businesses may overstock to safeguard against ongoing supply chain disruptions, but this can lead to storage issues. For instance, a tech company might stockpile components to avoid potential supply chain hiccups, resulting in surplus inventory and increased costs. Advanced systems can help businesses better anticipate and respond to supply chain challenges, balancing the need for safety stock with the risk of overstocking. A technology firm used Smart IP&O to streamline its inventory strategy, reducing excess stock by 20% while maintaining supply chain resilience​​.

11 Long Lead Times and Unreliable Suppliers

Prolonged lead times and unreliable suppliers can lead businesses to order more stock than needed to cover potential supply gaps. However, less critical Items that are forecasted to achieve very high service levels represent opportunities to reduce inventory.  By targeting lower service levels on less critical items, inventory will be “right size” over time to the new equilibrium, decreasing holding costs and the value of inventory on hand. A major public transit system reduced inventory by more than $4,000,000 while improving service levels using our cutting-edge technology.

12 Lack of Real-Time Inventory Visibility

Without real-time insights into inventory, businesses often order more stock than necessary, leading to inefficiencies and increased costs. Smart IP&O enabled Seneca companies 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.  By running and comparing different scenarios, they can easily define and update optimal stocking policies for each tech support rep and stockrooms.

The software has provided field technicians with evidence they did not have before, showing them their actual consumption, frequency of part use, and rationale for stocking policies, using 90% as the targeted service level norm.  Field technicians have embraced its use, with significant results:  “Zero Turns” inventory has dropped from $400K to under $100K, “First Fix Rate” exceeds 90%, and total inventory investment has decreased by more than 25%, from $11 million to $ 8 million.

In conclusion, overstocking seriously threatens business profitability and efficiency, leading to increased storage costs, tied-up capital, and potential obsolescence of goods. These issues can strain resources and limit a company’s ability to respond to market changes. However, overstocking can be effectively managed by understanding its causes, such as inaccurate demand forecasting, prolonged lead times, and unreliable suppliers. Implementing robust AI-driven solutions like Smart IP&O can help businesses optimize inventory levels, reduce excess stock, and enhance operational efficiency. By leveraging advanced forecasting and inventory optimization tools, companies can find the right balance in meeting customer demand and minimizing inventory-related costs.

Effective supply chain and inventory management are essential for achieving operational efficiency and customer satisfaction. This blog provides clear and concise answers to some basic and other common questions from our Smart IP&O customers, offering practical insights to overcome typical challenges and enhance your inventory management practices. Focusing on these key areas, we help you transform complex inventory issues into strategic, manageable actions that reduce costs and improve overall performance with Smart IP&O.

1. What is lead time demand?
The demand is expected to occur over the replenishment lead time. Lead time demand is determined by Smart’s forecasting methods. 

2. What is the Min, and how is it computed?
The Min is displayed in the drivers section of SIO is the reorder point and is the sum of the lead time demand and the safety stock. When on-hand inventory falls below the minimum due to demand, you will need to order more.  Smart also has a “min” in the “ordering rules” field of SIO, which is the minimum order quantity you can place with a supplier. 

3. What is the Max, and how is it computed?
The max is the largest quantity of inventory that will be on the shelf if you adhere to the ordering policy. The Max is the sum of the Min (reorder point) plus the defined OQ. 

4. How do you determine the order quantity (OQ)?
The order quantity is initially imported from your ERP system. It can be changed based on a number of user defined choices, including:

Multiple lead time demand
Multiple monthly or weekly demand
Smart’s recommended OQ

5. What is the Economic Order Quantity?
It is the order quantity that will minimize the total costs, considering the cost of holding and costs of ordering inventory. 

6. What is the “recommended OQ” that Smart computes?
It is the economic order quantity plus an adjustment if necessary to ensure that the size of the order is greater than or equal to the demand over lead time.

7. Why is the system predicting that we’ll have a low service level?
Smart predicts the service level that will result from the specified inventory policy (Min/Max or Reorder Point/Order Quantity), assuming adherence to that policy.  When the predicted service level is low, it can mean that the expected demand over the lead time is greater than the reorder point (Min).  When demand over the replenishment lead time is greater than the reorder point your probability of stocking out is higher resulting in a low service level. It may also be that your lead time for replenishment isn’t entered accurately.  If the lead time entered is longer than reality, the reorder point may not cover the demand over the lead time.  Please check your lead time inputs.

8. Why is the Service level showing as zero when the reorder point (or min) is not zero?
Smart predicts the service level that will result from the specified inventory policy (Min/Max or Reorder Point/Order Quantity), assuming adherence to that policy. When the predicted service level is low, it can mean that the expected demand over the lead time is greater than the reorder point (Min), sometimes many times greater, which would all but guarantee a stock-out.  When demand over the replenishment lead time is greater than the reorder point your probability of stocking out is higher resulting in a low service level. It may also be that your lead time for replenishment isn’t entered accurately.  If the lead time entered is longer than reality, the reorder point may not cover the demand over the lead time.  Please check your lead time inputs.

9. But my actual service levels aren’t as low as Smart is predicting, why?
That may be true because Smart predicts your service level if you adhere to the policy.  It is possible you aren’t adhering to the policy that the service level prediction is based on.  If your on-hand inventory is higher than your Max quantity, you aren’t adhering to the policy.  Check your input assumptions for lead time.   Your actual lead times might be much shorter than entered resulting in a predicted service level that is lower than you expect.

10. Smart seems to be recommending too much inventory, or at least more than I’d expect it would; why?
You should consider evaluating the inputs, such as service level and lead times.  Perhaps your actual lead times aren’t as long as the lead time Smart is using.  We’ve seen situations where suppliers artificially inflate their quoted lead times to ensure they are always on time.  If you use that lead time when computing your safety stocks, you’ll inevitably over-stock.  So, review your actual lead time history (Smart provides the supplier performance report for this) to get a sense of the actual lead times and adjust accordingly.  Or it is possible you are asking for a very high service level that may be further compounded with a very volatile item that has several significant spikes in demand.  When demand significantly fluctuates from the mean, using a high service level target (98%+) will result in stocking policies that are designed to cover even very large spikes.  Try a lower service level target or reducing the lead time (assuming the specified lead time is no longer realistic) and your inventory will decrease, sometimes very substantially.

11. Smart is using spikes in demand I don’t want it to consider, and it is inflating inventory, how can I correct this?
If you are sure that the spike won’t happen again, then you can remove it from the historical data via an override using Smart Demand Planner. You’ll need to open the forecast project containing that item, adjust the history, and save the adjusted history.  You can contact tech support to help you set this up. If the spikes are part of the normal randomness that can sometimes occur, it’s best to leave it alone. Instead, consider a lower service level target.  The lower target means the reorder points don’t need to cover the extreme values as often resulting in a lower inventory.

12. When I change the Order Quantity or Max, my cycle service levels don’t change, why?
Smart reports on “cycle service level” and “service level.”  When you change your order quantities and Max quantities this will not impact the “cycle service level” because cycle service levels report on performance during the replenishment period only.  This is because all that protects you from stockout after the order is placed (and you must wait until the order arrives for the replenishment) is the reorder point or Min. Changing the size of the order quantity or Max on hand (up to levels) won’t impact your cycle service levels.  Cycle service level is influenced only by the size of the reorder points and the amount of safety stock being added whereas Smart’s “service level” will change when you modify both reorder points and order quantities.

13. My forecast looks inaccurate.  It’s not showing any of the ups and downs observed in history, why?
A good forecast is the one number that is closest to the actual compared to other numbers that could have been predicted.  When the historical ups and downs aren’t happening in predictable intervals then often, the best forecast is one that averages or smooths through those historical ups and downs.  A forecast predicting future ups and downs that aren’t happening in obvious patterns historically is more likely to be less accurate than one that is forecast a straight or trend line only.

14. What is optimization? How does it work?
Optimization is an option for setting stocking policies where the software picks the stocking policy that yields the total lowest operating cost.  For example, if an item is very expensive to hold, a policy that has more stockouts, but less inventory would yield total lower costs than a policy that had fewer stockouts and more inventory.   On the other hand, if the item has a high stock out cost then a policy that yields fewer stockouts but requires more inventory would yield more financial benefit than a policy that had less inventory but more stockouts.  When using the optimization feature, the user must specify the service level floor (the minimum service level).  The software will then decide whether a higher service level will yield a better return.  If it does, the reordering policies will target the higher service level.  If it doesn’t the reordering policies will default to the user defined service level floor.        This webinar provides details and explanations on the math behind optimization.  https://www.screencast.com/t/3CfKJoMe2Uj

15. What is a what-if scenario?
What-if scenarios enable you to try out different user-defined choices of inventory policy and test the predicted impact on metrics such as service levels, fill rates, and inventory value. To explore these scenarios, click on the Drivers tab, either at the summary level or the “Items” level, and enter the desired adjustments. You can then recalculate to see how these changes would affect your overall inventory performance. This allows you to compare various strategies and select the most cost-effective and efficient approach for your supply chain.

By addressing common questions and challenges, we’ve provided actionable insights to help you improve your inventory management practices. With Smart IP&O, you have the tools you need to make informed inventory decisions, reduce costs, and enhance overall performance.

Demand planning goes beyond simply forecasting product needs; it’s about ensuring your business meets customer demands with precision, efficiency, and cost-effectiveness. Latest demand planning technology addresses key challenges like forecast accuracy, inventory management, and market responsiveness. In this blog, we will introduce critical demand planning trends, including data-driven insights, probabilistic forecasting, consensus planning, predictive analytics, scenario modeling, real-time visibility, and multilevel forecasting. These trends will help you stay ahead of the curve, optimize your supply chain, reduce costs, and enhance customer satisfaction, positioning your business for long-term success.

Data-Driven Insights

Advanced analytics, machine learning, and artificial intelligence (AI) are becoming integral to demand planning. Technologies like Smart UP&O allow businesses to analyze complex data sets, identify patterns, and make more accurate predictions. This shift towards data-driven insights helps businesses respond quickly to market changes, minimizing stockouts and reducing excess inventory.

Probabilistic Forecasting

Probabilistic forecasting focuses on predicting a range of possible outcomes rather than a single figure. This trend is particularly important for managing uncertainty and risk in demand planning. It helps businesses prepare for various demand scenarios, enhancing inventory management and reducing the likelihood of stockouts or overstocking​.

Consensus Forecasting

Modern manufacturing is moving towards an integrated approach where departments and stakeholders collaborate more closely. Collaborative forecasting involves sharing insights across the supply chain, from suppliers to distributors and internal teams. This approach breaks down silos and ensures that everyone is working towards a common goal, leading to a more synchronized and efficient supply chain​.

Predictive and Prescriptive Analytics

Predictive analytics forecasts future outcomes based on historical data and trends, helping businesses anticipate demand fluctuations. For example, Smart Demand Planner (SDP) automates forecasting to adjust inventory and production levels accordingly​.

Prescriptive analytics goes further by offering actionable recommendations. Smart Inventory Planning and Optimization (IP&O), for instance, prescribes optimal inventory policies based on service levels, costs, and risks​. ogether, these tools enable proactive decision-making, allowing companies to both predict and optimize their responses to future challenges.

Scenario Modeling

Scenario modeling is becoming a key part of demand planning, enabling businesses to simulate different scenarios and assess their impact on operations. This method helps companies create adaptable strategies to effectively handle uncertainties. Smart IP&O enhances this capability by offering What If Scenarios that allow users to test different inventory policies before implementation. By adjusting variables like service levels or order quantities, businesses can visualize the effects on costs and service levels, empowering them to select the optimal strategy for minimizing risks and controlling costs​​.

Real-Time Visibility

As supply chains become more global and interconnected, real-time visibility into inventory and supply chain activities is crucial. Enhanced collaboration with suppliers and distributors, combined with real-time data, enables businesses to make quicker, more informed decisions. This helps optimize inventory levels, reduce lead times, and improve overall supply chain resilience​.

Multilevel Forecasting

This involves forecasting at different levels of the product hierarchy, such as individual items, product families, or even entire product lines. Multilevel forecasting is vital for businesses with complex product portfolios, as it ensures that forecasts are accurate at both the micro and macro levels​.

Demand planning is a decisive aspect of modern supply chain management, offering businesses the ability to enhance operational efficiency, reduce costs, and better meet customer demands. Leveraging advanced platforms like Smart IP&O significantly improves forecasting accuracy and inventory management, enabling swift responses to market fluctuations. Automated statistical forecasting, combined with capabilities like hierarchy forecasting and forecast overrides, ensures that forecasts are accurate and adaptable, leading to more realistic planning decisions. Additionally, with tools like scenario modeling, businesses can explore various demand scenarios across their product hierarchy, facilitating informed decision-making by providing insights into potential outcomes and risks. This approach allows businesses to anticipate the impact of policy changes, make better decisions, and ultimately optimize their inventory and overall supply chain management, staying ahead of key trends in the process.

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.