Inventory Forecasting Guide - Best Practices in 2023
2023 Comprehensive Guide to Inventory Forecasting in Third-Party Logistics
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Inventory forecasting is a critical aspect of third-party logistics (3PL) operations, as it directly impacts inventory management, supply chain efficiency, and customer satisfaction. A comprehensive guide to inventory forecasting in third-party logistics includes the following key steps and considerations:
Understanding trends begins with gathering historical data on inventory levels, sales, and demand patterns. This data should cover a sufficiently lengthy period to identify seasonal trends, promotions, and any other factors that may affect demand. Collect data on external factors that may influence demand, such as economic trends, market conditions, and competitor activities. Use advanced analytics tools and techniques to analyze the data and uncover insights to inform the forecasting process.
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There are several demand forecasting methods, including time-series forecasting (using historical data patterns), causal forecasting (relating demand to specific factors like promotions or marketing campaigns), and collaborative forecasting (involving input from stakeholders like customers and suppliers). Forecasting methods will vary based on the product category or industry. Implement a combination of forecasting methods to improve accuracy and account for numerous factors influencing demand.
When appropriate implement a safety stock as a buffer to mitigate uncertainties in demand and supply. It acts as a cushion against sudden spikes in demand or delays in supply. Calculate safety stock based on the desired service level, historical demand variability, lead times, and other relevant factors.
Establish open lines of communication with clients to gain insights into their sales forecasts, promotions, and upcoming events that may affect demand. Collaborate with suppliers to understand lead times, production schedules, and potential supply constraints.
Classify inventory items based on their demand patterns, value, and criticality. Segment products into categories (e.g., fast-moving, slow-moving, seasonal) to apply different forecasting and inventory management strategies to each group.
Implement inventory management systems and demand forecasting software that leverage data analytics, AI, and machine learning to improve accuracy and automate processes. Utilize real-time data to make dynamic adjustments to inventory levels and respond quickly to changes in demand.
Regularly monitor forecast accuracy and actual demand to identify any discrepancies and improve forecasting models. Incorporate feedback from stakeholders and adjust forecasting methods as needed.
Prepare for various scenarios, such as unexpected spikes in demand, supply chain disruptions, or changing market conditions. Develop contingency plans to address each scenario effectively.
Inventory forecasting is an ongoing process that requires continuous improvement. Learn from past experiences, adapt to changing market dynamics, and consistently refine forecasting techniques. Track key performance indicators (KPIs) related to inventory management, such as fill rate, stock-out rate, and inventory turnover, to assess the effectiveness of the forecasting process.
Remember that no forecasting method is entirely foolproof, as uncertainties are inherent in any supply chain. However, by adopting a data-driven and collaborative approach, leveraging technology, and continuously refining the process, third-party logistics providers can optimize inventory levels, reduce costs, and enhance customer satisfaction.
What Is Inventory Forecasting?
Inventory forecasting is a process used by businesses to predict future demand for their products or services and estimate the appropriate amount of inventory they should have on-hand to meet that demand.
The goal of inventory forecasting is to strike a balance between avoiding stockouts (running out of products) and minimizing excess inventory (holding more inventory than necessary), which can tie up capital and lead to increased costs.
Working with your vendors to better understand the trends they see across a broader marketplace is a useful exercise to see if your trends align, what differences exist and understand what lead times are required for raw materials and production.
Types of Inventory Forecasting
There are many methods that businesses can use to predict future demand and optimize their inventory levels. Below are some of the commonly used inventory forecasting techniques:
- Time series forecasting involves analyzing historical sales data to identify patterns and trends over time. This method uses mathematical models to predict future demand based on past sales data, considering factors like seasonality and trend.
- Moving averages calculate the average demand over a specific period, such as weeks or months, and can help smooth out fluctuations in sales data. Simple Moving Average (SMA) and Weighted Moving Average (WMA) are common ways to measure average demand.
- A very popular method that has been increasingly used is exponential smoothing. Exponential smoothing is a time series forecasting method that assigns exponentially decreasing weights to past observations. This technique gives more importance to recent data points while forecasting.
- For merchants with high seasonality variants a common practice is seasonal decomposition of time series which is used to identify and separate the underlying trend, seasonal patterns, and remainder (random noise) in a time series. This approach allows for a more accurate understanding of seasonality and trend effects.
- Regression models use various independent variables, such as advertising expenditure, economic indicators, or promotional activities, to predict future demand. Multiple Linear Regression and Polynomial Regression are common techniques in this category.
- Advanced machine learning algorithms, such as Artificial Neural Networks, Random Forests, and Gradient Boosting, can be employed to build more accurate and sophisticated inventory forecasting models. These algorithms can handle complex relationships between various factors affecting demand.
- Many trade organizations use collaborative forecasting, where businesses work together with suppliers, retailers, and other partners in the supply chain to share demand forecasts. This approach facilitates better coordination and inventory management throughout the entire supply chain.
- Casual forecasting involves identifying cause-and-effect relationships between demand and various factors like promotions, pricing changes, or external events. These relationships are then used to forecast future demand.
- Judgmental forecasting relies on expert knowledge and judgment from individuals with experience in the industry. It can be useful when historical data is scarce or when sudden, unexpected events occur.
- Businesses often use a combination of these forecasting techniques to achieve more accurate and reliable inventory forecasts. The choice of method depends on the available data, the complexity of the demand patterns, and the industry-specific characteristics of the business.
Best Tools for Inventory Forecasting
There are many great forecasting tools available to manage inventory. Keep in mind that the technology landscape evolves rapidly, so new inventory tools come to market frequently. Determining which tool is appropriate for you is probably best looked at first within your own suite of software used to manage your business. Here are ten great solutions to consider:
- Demand Solutions is a comprehensive supply chain planning platform that includes inventory forecasting, demand planning, and inventory optimization capabilities. It provides a user-friendly interface and supports various forecasting methods.
- JDA Demand Planning, now part of Blue Yonder (formerly JDA Software), is a robust solution for demand forecasting and inventory optimization. It offers advanced analytics and machine learning capabilities.
- ToolsGroup SO99+ is a supply chain planning platform that combines demand forecasting, inventory optimization, and replenishment planning. It uses machine learning algorithms to improve forecast accuracy.
- Oracle’s NetSuite is an integrated cloud-based enterprise resource planning (ERP) system that includes inventory forecasting functionality. It is suitable for businesses looking for a comprehensive solution that covers multiple aspects of their operations.
- SAP Integrated Business Planning is part of SAP’s supply chain management suite, providing forecasting and demand planning capabilities along with other supply chain features.
- Anaplan is a flexible cloud-based planning platform that offers inventory forecasting and demand planning modules. It is known for its scalability and ability to handle complex planning scenarios.
- EazyStock is a user-friendly inventory optimization software that includes advanced forecasting capabilities. It is designed to help businesses reduce excess inventory while preventing stockouts.
- GMDH Streamline is a powerful standalone inventory forecasting tool that uses artificial intelligence and machine learning algorithms to forecast demand accurately.
- WiseSight is a demand forecasting and inventory optimization platform that uses machine learning to improve forecast accuracy and optimize inventory levels.
- Lokad is a cloud-based forecasting and inventory optimization software that caters to various industries and business sizes. It offers advanced analytics and predictive modeling.
Before choosing a tool, it’s essential to consider your business’s specific needs, the complexity of your supply chain, the level of integration required with other systems, and the scalability of the solution. Some tools offer free trials or demonstrations, allowing you to assess their suitability for your business before making a final decision. Always check for the latest reviews and customer feedback to ensure the tool aligns with your requirements.
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What Causes Demand Fluctuations?
Inventory forecasting is affected by multiple internal and external factors. These fluctuations in demand can be short-term or long-term and can significantly impact a company’s operations, production, and inventory levels. We have seen extreme cases of inventory fluctuations in recent years (2020-2022) that has brought a significant focus into the challenges of maintaining just the right inventory level. Some common causes of demand fluctuations include:
Many products experience seasonal demand patterns, where demand surges or declines during specific times of the year. For example, winter clothing tends to have higher demand during colder months, while swimwear sees increased demand in the summer. Demand often spikes during holidays and special events, such as Christmas, Black Friday, Valentine’s Day, or local festivals. Retailers and businesses may experience a significant increase in sales during these periods.
Economic factors, such as changes in GDP, unemployment rates, interest rates, and consumer confidence, can influence demand. In a strong economy, consumers tend to spend more, leading to increased demand for goods and services. Changes in consumer preferences, emerging trends, or technological advancements can lead to shifts in demand for certain products or services. New products or innovations may see a sudden surge in demand, while outdated products may experience declining demand.
Competitors’ marketing strategies, pricing changes, product launches, or promotions can impact demand for a business’s products. Aggressive marketing campaigns or discounts from competitors may divert customers and affect demand levels.
Natural disasters, extreme weather conditions, or other unforeseen events can disrupt supply chains and cause demand fluctuations. For example, a hurricane might disrupt transportation and supply of goods in a region.
Changes in government regulations, import/export restrictions, or tax policies can influence demand for certain products or industries.
Different stages of a product’s lifecycle (introduction, growth, maturity, and decline) can lead to fluctuations in demand. For example, a new product may experience high initial demand but may taper off as it reaches maturity.
Issues in the supply chain, such as delays in production, transportation problems, or supplier disruptions, can lead to variations in product availability and demand.
Changes in consumer behavior, buying patterns, or lifestyle choices can cause shifts in demand for specific products or industries. Understanding the causes of demand fluctuations is essential for businesses to adapt their strategies, manage inventory effectively, and optimize supply chain operations to meet changing customer demands and market conditions.
Challenges With Inventory Forecasting
The challenges that face those that forecast inventory demands can sometimes be unrelenting. Just when you believe you have it right, another variable enters the equation and changes the outcome. Here are some of the key challenges with inventory forecasters face:
Demand for products can be highly unpredictable, especially for items with erratic or intermittent demand patterns. Sudden changes in customer behavior, market trends, or external factors can lead to significant fluctuations in demand. Incorporating seasonality and long-term trends accurately into forecasts can be complex, particularly when these patterns are subject to change over time.
Forecasting demand for new products can be challenging, as historical data may not be available or dependable. It is difficult to predict how customers will respond to a novel offering. Products go through different lifecycle stages, and demand can change accordingly. Managing inventory during product introductions, growth, maturity, and decline phases requires careful forecasting.
Forecast accuracy heavily relies on the quality and quantity of historical sales data. Inaccurate or inaccurate data can lead to unreliable forecasts.
External factors such as economic fluctuations, natural disasters, regulatory changes, and other external events can disrupt demand patterns and render traditional forecasting models less effective. Forecasting becomes more challenging when there are multiple stakeholders involved in the supply chain, each with their own forecasts and plans. Misalignments between forecasts can lead to excess inventory or stockouts.
Accurately predicting lead times for inventory replenishment is essential for maintaining optimal stock levels. Variability in lead times can impact forecast accuracy. Sales promotions, marketing campaigns, or external advertising efforts can significantly influence short-term demand, making it difficult to gauge the underlying baseline demand accurately. Companies with extensive product portfolios and multiple sales locations face the challenge of forecasting accurately for each product and location, considering their individual characteristics and demand patterns.
Unexpected events, such as sudden shifts in the competitive landscape, disruptive technologies, or geopolitical changes, can disrupt demand patterns and render forecasts less dependable.
Lastly, human biases and subjectivity in forecasting can lead to overestimating or underestimating demand, resulting in suboptimal inventory decisions.
Factors to Consider When Creating Inventory Forecasts
There are many factors to consider when creating accurate inventory forecasts. It is crucial for businesses to optimize their inventory levels, reduce carrying costs, and ensure sufficient stock availability. To do so, one must understand what factors should be considered and which ones should be left out. Here are several factors that should be considered when creating inventory forecasts:
Analyze past sales data to identify patterns, trends, and seasonality. Historical data provides insights into demand fluctuations and helps establish a baseline for forecasting. Recognize and account for seasonal fluctuations in demand. Various products may experience varying levels of demand during certain times of the year (e.g., holidays, seasons, or special events).
Stay informed about market trends, economic conditions, industry developments, and competitor activities. External factors, such as changes in consumer preferences or the introduction of new products, can impact demand. Consider marketing campaigns, promotions, and discounts that can influence consumer buying behavior. These activities may lead to short-term spikes in demand that need to be factored into the forecast.
Consider lead times for sourcing and manufacturing products. Longer lead times may necessitate higher inventory levels to avoid stockouts. Assess the reliability of suppliers in delivering goods on time and in the expected quantities. Supplier performance issues can disrupt the supply chain and affect inventory levels.
Calculate the costs associated with holding inventory, including storage, insurance, and depreciation. Balancing holding costs with the risk of stockouts is essential for effective forecasting. Review inventory turnover ratios to determine how quickly products are selling relative to the amount of inventory on hand. High turnover indicates efficient inventory management, while low turnover may signal excess stock.
Collaborate with sales teams to gather their insights and forecasts. Sales representatives often have valuable knowledge about customer preferences and upcoming demand. Account for new product launches and their potential impact on overall inventory needs. Accurate forecasting is critical during product launch phases to avoid stockouts or overstocking. Listen to customer feedback and reviews to understand their preferences and anticipate potential changes in demand.
Consider economic indicators, such as GDP growth, consumer spending, and inflation rates, to assess overall economic conditions that may impact consumer behavior and demand. Utilize appropriate forecasting methods, such as time-series analysis, moving averages, exponential smoothing, or machine learning algorithms, depending on the data and complexity of the forecasting task. Involve key stakeholders, including sales, marketing, finance, and operations, in the forecasting process to ensure a comprehensive understanding of demand drivers.
Regularly review and update inventory forecasts based on actual sales performance. Continuously improve forecasting accuracy by learning from past forecasting errors.
By considering these factors and using a combination of data-driven analysis, businesses can enhance their inventory forecasting processes and optimize their inventory management strategies.
What’s the Difference Between Inventory Forecasting and Inventory Replenishment?
Inventory forecasting and inventory replenishment are two distinct concepts related to managing inventory in a business. While they are closely interconnected, they are distinct aspects of inventory management. Here’s an explanation of each term:
Inventory forecasting refers to the process of predicting the future demand for products or materials. It involves analyzing historical sales data, market trends, customer behavior, and other relevant factors to estimate the quantity of inventory that will be needed over a specific period. The goal of inventory forecasting is to make accurate predictions to avoid stockouts (insufficient inventory) or overstocking (excessive inventory). By understanding future demand patterns, businesses can optimize their inventory levels, plan production or procurement, and enhance customer satisfaction.
Inventory replenishment, also known as inventory replenishment planning, involves managing the inventory levels to ensure that they are maintained at appropriate levels. It is the operational aspect of inventory management that focuses on executing the inventory replenishment process. Inventory replenishment entails monitoring the inventory levels, tracking sales, and triggering actions to restock or reorder items when they fall below a certain threshold. The aim is to maintain sufficient stock levels to meet customer demand while minimizing carrying costs associated with excess inventory.
In essence, inventory forecasting provides insights into future demand patterns, while inventory replenishment puts those insights into action by executing the process of restocking or reordering inventory. Effective inventory management requires a combination of accurate forecasting and efficient replenishment strategies to strike a balance between inventory availability and cost optimization.
How Can Inventory Forecasting Benefit Your eCommerce Business?
Inventory forecasting is critical in the success of your eCommerce business, contributing to its overall success and profitability. Here are the top five ways inventory forecasting can be advantageous:
Accurate inventory forecasting helps you predict the demand for products more precisely. By having a clear understanding of when and how much inventory you will need, you can avoid stockouts (running out of stock) and overstocking (excess inventory). This ensures that you always have the right amount of stock to fulfill customer orders promptly without tying up unnecessary capital in surplus inventory.
With inventory forecasting, you can optimize your inventory levels to match expected demand. This reduces the cost associated with holding excess inventory for prolonged periods. Moreover, it helps you avoid rush shipping costs or emergency restocking, which can be expensive.
By maintaining appropriate inventory levels, you can consistently fulfill customer orders without delay. This leads to improved customer satisfaction, as customers receive their products on time and as expected. Satisfied customers are more likely to become repeat buyers and recommend your eCommerce business to others.
Inventory forecasting can also help you plan and execute marketing campaigns and promotions more effectively. Knowing the expected demand allows you to align your promotions with the inventory levels, ensuring that you can meet the increased demand generated by the marketing efforts.
Carrying excess inventory incurs costs such as storage, insurance, and potential obsolescence. By accurately forecasting demand, you can minimize the amount of inventory held in stock, reducing these holding costs, and improving overall profitability.
Maximizing profits requires an inventory “balance” that optimally has the right amount of product at the right location/promotion at the time of customer demand. Mitigating the risks that comes with having insufficient inventory (loss sales) and too much inventory (tying up cash). Inventory forecasting empowers eCommerce businesses to operate more efficiently, meet customer demands reliably, and make informed decisions that positively impact their bottom line. It is an essential tool for optimizing inventory management and achieving sustainable growth in the competitive eCommerce landscape.
How Can a 3PL Provider Help With Inventory Forecasting?
A 3PL (Third-Party Logistics) provider can play a crucial role in helping businesses with inventory forecasting by leveraging their expertise, resources, and specialized software. Here is how a 3PL provider can assist with inventory forecasting:
3PL providers often have access to a vast amount of historical sales data and inventory records from multiple clients. They can analyze this data to identify trends, seasonal patterns, and demand fluctuations. Their experience in handling various industries and products allows them to offer valuable insights and expertise in predicting future demand accurately.
Many 3PL providers utilize sophisticated inventory management and forecasting software. These tools use algorithms and statistical models to process historical data and generate forecasts. By partnering with a 3PL, businesses can benefit from these advanced tools without having to invest in expensive software themselves.
3PL providers often collaborate closely with their clients to understand their business goals, marketing plans, and product lifecycles. This collaborative approach enables them to align their forecasting with the client’s sales and marketing strategies, ensuring that inventory levels are well-adjusted to meet future demand.
Integration with the client’s systems (e.g., eCommerce platforms, order management systems) allows the 3PL provider to access real-time data on sales and inventory levels. This up-to-date information enhances the accuracy of their forecasts, enabling timely adjustments to inventory levels.
Using accurate forecasts, the 3PL provider can optimize inventory levels by recommending ideal safety stock levels, reorder points, and lead times. This helps prevent stockouts and overstocking, leading to improved inventory turnover and cost savings.
3PL providers can offer better visibility into the entire supply chain, from the procurement of raw materials to the delivery of finished products. This visibility allows businesses to identify potential bottlenecks, streamline processes, and optimize inventory movements to match demand fluctuations.
Overall, partnering with a 3PL provider can provide businesses with valuable support in inventory forecasting, leading to more efficient inventory management, reduced costs, and improved customer satisfaction. The 3PL’s expertise and resources complement the business’s capabilities, allowing them to focus on their core competencies while optimizing their supply chain and inventory strategies.
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Tips for Better Inventory Forecasting
Improving inventory forecasting is crucial for efficient inventory management and can help businesses reduce costs, minimize stockouts, and optimize cash flow. Here are ten tips for better inventory forecasting:
- Analyze past sales and demand patterns to identify trends, seasonality, and any unusual events that might have affected demand. This data serves as the foundation for your forecasting models.
- Employ a combination of forecasting techniques, such as time series analysis, moving averages, exponential smoothing, and machine learning algorithms. Different methods can capture various patterns and provide more accurate predictions.
- A common mistake is to treat all products equally. Segment your inventory based on factors like product category, customer segment, or geographical location. Forecast each segment separately, as demand patterns can vary significantly.
- Keep communication lines open with sales and marketing teams to get insights into potential promotions, product launches, or changes in customer preferences that might impact demand.
- Take into account external factors like economic conditions, competitor activities, and industry trends, as they can influence demand.
- Keep track of inventory turnover ratios for different products to understand which items are moving quickly and which are slow-moving. This information can guide inventory forecasts.
- Incorporate safety stock levels to account for demand variability and supply chain uncertainties. Also, consider lead times when estimating how long it takes to replenish inventory.
- Work closely with your suppliers to share demand forecasts, lead times, and any changes in ordering patterns. This collaboration can lead to more accurate forecasts and better inventory management.
- Invest in reliable inventory management software that can automate the forecasting process, analyze data, and provide actionable insights.
- Use metrics like Mean Absolute Percentage Error (MAPE) or Root Mean Square Error (RMSE) to measure the accuracy of your forecasts. Regularly assess your forecasting performance and aim to improve it over time.
Remember that inventory forecasting is an ongoing process that requires constant improvement and refinement. Regularly reevaluate your methods and adjust them as needed to adapt to changing market conditions and business requirements.
Inventory forecasting plays a critical role in most businesses, but especially in eCommerce. As your business grows, so does the need to improve the amount of data collected to make informed decisions. Inventory forecasting can be extremely complex, so the sooner you start, the better.
There are many great resources and tools available to use and the benefits are clear from resource management, cash flow management to customer satisfaction.
FAQs About Inventory Forecasting
Inventory forecasting is the process of predicting future demand for products or materials to ensure that adequate inventory levels are available. It involves analyzing historical sales data, market trends, customer behavior, and other relevant factors to estimate the quantity of inventory that will be needed over a specific period.
Inventory forecasting is essential for effective inventory management. It helps businesses avoid stockouts and overstocking, optimize inventory levels, reduce holding costs, improve customer satisfaction, and make informed decisions related to procurement and production.
Data used for inventory forecasting includes historical sales data, customer orders, inventory levels, market trends, seasonality, and any relevant external factors that may influence demand.
Various methods and techniques are used for inventory forecasting, including time series analysis, moving averages, exponential smoothing, trend analysis, seasonal decomposition, and machine learning algorithms.
The accuracy of inventory forecasts depends on the quality of data, the forecasting method used, and the complexity of the demand patterns. Accurate forecasts typically require regular updates and adjustments based on real-time data and changing market conditions.
Yes, inventory forecasting can handle seasonal demand patterns by using seasonal decomposition methods and historical data analysis to predict demand fluctuations during specific periods.
The frequency of updating inventory forecasts depends on the volatility of the demand and the lead times for ordering or manufacturing inventory. In dynamic markets, forecasts may need to be updated weekly or even daily, while in more stable environments, monthly updates may be sufficient.
Safety stock is an additional quantity of inventory maintained as a buffer to protect against unexpected variations in demand or supply chain disruptions. It is calculated based on factors such as lead time, demand variability, and desired service levels.
Yes, inventory forecasting can assist with new product launches by using historical data from similar products or conducting market research to estimate demand patterns and determine initial inventory levels.
Yes, businesses can use inventory forecasting for multiple sales channels, such as brick-and-mortar stores, eCommerce platforms, and wholesale distribution. Integrated inventory management systems can facilitate cross-channel forecasting and inventory allocation.
Yes, inventory forecasting can be automated using advanced inventory management software that employs forecasting algorithms and integrates with other business systems to provide real-time data for analysis.
Accurate inventory forecasting improves supply chain management by enabling better coordination between suppliers, manufacturers, and retailers, reducing lead times, and optimizing inventory levels throughout the supply chain.