Advanced Forecasting for Reducing Inventory Costs

In today's competitive business environment, efficient inventory management is a key strategic factor for reducing costs and increasing customer satisfaction. One of the most effective ways to achieve this is through advanced forecasting.

The Case for Advanced Forecasting

The case for Advanced Forecasting is compelling. It’s a strategic approach that leverages cutting-edge technologies and methodologies to predict future trends and behaviors, enabling businesses to make informed decisions and optimize their operations.

Statistics form the backbone of any forecasting model. Advanced forecasting, however, goes beyond traditional statistical methods. It employs complex statistical techniques like multivariate analysis, Bayesian inference, and Monte Carlo simulations. These techniques allow for a more nuanced understanding of data, accounting for multiple variables and their interdependencies.

Advanced analytics is another pillar of advanced forecasting. It involves the use of sophisticated algorithms that can learn from data, identify patterns, and make predictions. Machine learning, a subset of advanced analytics, enables models to improve their accuracy over time, learning from their errors and refining their predictions.

Big data models are integral to advanced forecasting. The advent of big data has provided businesses with a wealth of information. Advanced forecasting leverages this data, using big data models to process and analyze large and diverse datasets. These models can handle structured and unstructured data, providing a more holistic view of the factors influencing future trends.

Artificial Intelligence (AI) has revolutionized forecasting. AI algorithms, like neural networks and deep learning models, can model complex, non-linear relationships that traditional statistical methods cannot. They can adapt to new data and changing conditions, making them particularly useful for forecasting in dynamic and uncertain environments.

The benefits of advanced forecasting are evident. According to a McKinsey study enabling advanced forecasting based on underlying drivers of demand rather than past outcomes, improves forecast accuracy by 10 to 20 percent. This translates into a potential 5 percent reduction in inventory costs and revenue increases of 2 to 3 percent (https://www.mckinsey.com/capabilities/quantumblack/our-insights/most-of-ais-business-uses-will-be-in-two-areas ).

These benefits underscore the value of advanced forecasting in today’s data-driven business landscape. By leveraging statistics, advanced analytics, big data models, and AI, businesses can not only predict the future but also shape it.

Case Study: IKEA and AI-based predictive forecasting

https://www.ikea.com/global/en/stories/design/using-artificial-intelligence-for-smarter-demand-forecasting-210527/#:~:text=Smart%20forecasting%20%E2%80%93%20lower%20cost,its%20customers%20at%20all%20touchpoints.

IKEA, a global retail giant, has developed an advanced tool that significantly improves the accuracy of its demand forecasting. This tool, known as “Demand Sensing,” uses artificial intelligence and leverages both existing and new data to offer highly accurate forecast insights.

Before the implementation of Demand Sensing, IKEA faced challenges such as overstocking or understocking, missed revenue opportunities, and inefficient resource allocation. These issues were particularly significant given the scale of IKEA’s operations, which involve estimating the products needed by more than 450 IKEA stores and e-commerce platforms across 54 markets.

The implementation of the AI-based predictive analytics system led to a significant improvement in sales forecasting accuracy and a reduction in forecasting errors. The system can use up to 200 data sources for each product to calculate forecasts and predict future demand more smartly and effectively. Influencing factors considered by the system include shopping preferences during festivals, the influence of seasonal changes on purchase patterns, and weather forecasts.

The results have been impressive. With the Demand Sensing tool, IKEA has achieved a level of close to 98% accepted forecast, with only 2% corrected. This is a significant improvement from the previous 92% accepted forecast and 8% corrected.

The implementation of AI in IKEA’s supply chain through the Demand Sensing tool showcases the transformative power of advanced forecasting systems in improving operational efficiency and customer satisfaction.

Elements of Inventory Costs That Are Reduced Through Advanced Forecasting

Advanced forecasting primarily reduces two elements of inventory costs (figure 1):

Holding Costs and Stockout Costs

1. Holding Costs Holding costs, also known as carrying costs, are the costs associated with storing unsold inventory. These costs can be significant and include several components:

Storage Costs: This includes the cost of warehouse space, utilities, and security. The more inventory a business has, the more space it needs to store it.

Insurance and Taxes: Businesses must insure their inventory against loss or damage. They may also have to pay taxes on their inventory, depending on local regulations.

Depreciation: Over time, some types of inventories may lose value. This is particularly true for perishable goods, seasonal items, or products with a short life cycle.

Opportunity Cost: The money tied up in inventory could have been used for other purposes, such as investing in new business opportunities or paying down debt. This is known as the opportunity cost of holding inventory.

On a balance sheet, holding costs typically show up under Inventory in the Current Assets section. However, some components of holding costs (like depreciation, insurance, and taxes) may appear under different headings, such as Expenses.

2. Stockout Costs: Stockout costs occur when a business runs out of a particular item and cannot meet customer demand. These costs can be direct or indirect:

Lost Sales: If customers cannot find the products they want, they may choose to shop elsewhere, leading to lost sales.

Customer Dissatisfaction: Stockouts can lead to customer dissatisfaction and harm a company’s reputation. Over time, this can result in a loss of customer loyalty and decreased sales.

Expedited Shipping Costs: In some cases, a business may choose to expedite shipping at their own expense to fulfill a customer order and avoid a stockout.

Stockout costs do not typically appear directly on a balance sheet. However, they can indirectly affect several areas of the financial statements. For example, frequent stockouts may lead to decreased Sales Revenue. Over time, this could impact Net Income and the overall equity of the business.

Advanced forecasting systems can help businesses optimize their inventory levels, reducing both holding and stockout costs. By accurately predicting demand, businesses can ensure they have just the right amount of inventory - not too much, and not too little. This can lead to cost savings, increased sales, and improved customer satisfaction.

Implementing Advanced Forecasting

Implementing advanced forecasting involves 5 steps (figure 2):

1. Data Collection: The first step in implementing advanced forecasting is to collect historical sales data, along with any other relevant data such as promotional activities, economic indicators, and market trends. With the advent of big data, businesses can now collect and process vast amounts of data in real-time. This data can come from various sources such as transaction records, customer interactions, social media, and external databases. The collected data is then cleaned and transformed into a format suitable for analysis.

2. Model Selection: The next step is to choose a forecasting model that suits your business needs. This could be a time series model, a machine learning model, or a combination of both. Time series models are statistical techniques that analyze data collected over time to identify trends and patterns. On the other hand, machine learning models use algorithms to learn from data and make predictions or decisions. Artificial intelligence (AI) can be used to automate the model selection process, choosing the most appropriate model based on the characteristics of the data.

3. Model Training: Once a model has been selected, it needs to be trained using the historical data. This involves adjusting the model’s parameters to minimize the difference between the model’s predictions and the actual observed values. Advanced analytics techniques can be used to optimize this process, ensuring that the model accurately captures the underlying patterns in the data.

4. Forecasting: After the model has been trained, it can be used to generate forecasts for future periods. This is where demand sensing comes into play. Demand sensing is a method that uses machine learning and AI to analyze current data and predict future demand. It goes beyond traditional forecasting methods by incorporating real-time data and short-term predictions to create more accurate and timely forecasts.

5. Review and Adjust: The final step is to regularly review the accuracy of your forecasts and adjust your model as necessary. This is an ongoing process, as the accuracy of forecasts can change over time due to changes in market conditions, customer behavior, and other factors. Advanced analytics can be used to monitor the performance of the forecasting model and adjust as needed.

By following these steps and leveraging big data models, artificial intelligence, advanced analytics, and demand sensing, businesses can implement advanced forecasting systems that improve operational efficiency, reduce costs, and enhance customer satisfaction. These systems provide businesses with the insights they need to make informed decisions and stay ahead of the competition.

Conclusion

Advanced forecasting, leveraging cutting-edge technologies and methodologies, has emerged as a potent tool for businesses to optimize inventory management, thereby reducing costs and enhancing customer satisfaction. By employing complex statistical techniques, advanced analytics, big data models, and artificial intelligence, advanced forecasting transcends traditional methods, enabling businesses to predict future trends and behaviors with increased accuracy.

The case of IKEA’s implementation of the AI-based “Demand Sensing” tool exemplifies the transformative power of advanced forecasting. It led to significant improvements in sales forecasting accuracy, reduced forecasting errors, and optimized inventory levels, demonstrating the potential of such systems in improving operational efficiency and customer satisfaction.

Advanced forecasting primarily reduces two elements of inventory costs: holding costs and stockout costs. By accurately predicting demand, businesses can ensure optimal inventory levels, leading to cost savings, increased sales, and improved customer satisfaction.

The implementation of advanced forecasting involves steps like data collection, model selection, model training, forecasting, and regular review, and adjustment of the model. By following these steps and leveraging big data models, artificial intelligence, advanced analytics, and demand sensing, businesses can implement advanced forecasting systems that provide them with the insights they need to make informed decisions and stay ahead of the competition.

Advanced forecasting is a strategic approach that not only allows businesses to predict the future but also shape it, leading to increased profitability and competitiveness in today’s data-driven business landscape.