In the modern business environment, an accurate demand forecasting analytics process is a make-or-break factor,especially in the pharmaceutical, biotech, and consumer goods industries, where the nature of the product means that overstocking or out-of-stocks can lead to significant losses.
In 2018, global sports brand Nike faced a $1 billion inventory glut.The event, which was caused by a failure to forecast demand, led to a sharp drop in Nike's stock price and ultimately led to a massive clearance sale that severely impacted profitability.
This is known as an example of the limitations of traditional forecasting methods that simply rely on historical data.
In fact, according to a study by global consulting firm McKinsey,45% of companies have inventory management issues due to inaccurate demand forecasting, costing them $1 trillion annually worldwide.
Organizations are now looking to address these challenges by adopting advanced predictive analytics powered by AI. In this article, we'll discuss the technologies that the AI industry is using to improve predictive analytics accuracy, as well as Deepflow's own strategy.
Ensemble learning is a technique that combines multiple predictive models to produce more accurate and reliable predictions than a single model. It's the same principle as when you make an important decision, but instead of relying on the judgment of a single person, you synthesize the opinions of multiple experts.
Especially in manufacturing environments, the complex interplay of variables such as raw material supply and demand, market demand, and production capacity often makes it difficult for a single model to make accurate predictions. Ensemble learning combines the strengths of each model and compensates for their weaknesses to make more reliable predictions.
Bagging,short for Bootstrap Aggregating, is a method of taking multiple random samples from the original data, training individual predictive models, and then aggregating their results.
The main advantage of this technique is that it prevents over fitting of individual models and increases the reliability of predictions. In healthcare, this technique has been used to reduce patient readmission rates by 20%, as it allows for more accurate predictions by collectively analyzing a variety of medical data.
Boosting is a method of building a strong predictive model by sequentially training multiple weak predictive models. It works by weighting the instances where the previous model got it wrong so that the next model makes up for it.
In particular, advanced boosting algorithms such as XGBoost have gained attention for their high predictive performance. The financial industry has used this technique to significantly improve the accuracy of loan risk assessments because of boosting's ability to learn complex patterns in stages.
Random forests work by generating multiple decision trees and synthesizing their predictions. Each tree is trained on a subset of data with different characteristics, which allows for predictions from different perspectives.
Random forests are characterized not only by their high prediction accuracy, but also by their ability to identify the importance of each variable. In manufacturing,this technique can be applied to equipment maintenance to predict equipment failure, while also analyzing which of the various sensor data is most relevant to the failure.
Deep learning algorithms have become key tools that dramatically improve the accuracy of predictive analytics. Deep learning learns complex data patterns through deep neural networks that mimic the structure of the human brain, and enables high-accuracy predictions based on this.
Unlike traditional machine learning methods, which require people to extract and design features, deep learning has the great advantage of being able to automatically learn and extract important features from data.
The core of a deep learning algorithm is a multilayer neural network. Each layer receives the output of the previous layer as input and learns a higher level of abstracted features.
For example, in product image analysis, if the first layer detects simple lines and colors, the next layer combines them to recognize shapes and textures, and the deeper layers can grasp the overall characteristics of the product. This hierarchical learning structure effectively captures complex patterns and contributes to improving prediction accuracy.
Convolutional neural networks (CNNs) are deep learning algorithms that are specialized in image and pattern recognition. They effectively extract spatial features of images through convolution operations and compress and emphasize important features through pooling layers.
In the manufacturing industry, this technology can be used to automate product appearance inspection, defect detection, and quality control. In particular, it can identify even the finest patterns or defects that are difficult to detect with the human eye with high accuracy, greatly contributing to improving productivity and quality.
Recurrent neural networks (RNNs) are specialized for processing data that changes over time. They are very effective for analyzing time series data because they can reflect information from previous time points in current predictions.
For example, it can monitor the status of equipment by analyzing continuous sensor data generated during the manufacturing process, or it can make more accurate demand forecasts by learning the changing patterns of market demand. However, RNN has the limitation that it has difficulty in remembering long-term information.
Long short-term memory neural networks (LSTM) were developed to overcome the limitations of the RNN described above.
LSTM has selective memory capability that allows it to remember important information for a long time and erase unnecessary information through a special gate structure. LSTM performs well in situations where both long-term patterns and short-term fluctuations must be considered, such as production planning and inventory management.
In today's industrial environment, the accuracy of predictions is a key factor in determining a company's competitiveness. In particular, the use of advanced predictive analytics techniques is essential in environments where complex variables interact, such as in the manufacturing, bio, and chemical industries.
These advanced analytics techniques go beyond simple pattern recognition to provide the ability to systematically manage uncertainty and respond immediately to changing environments in real time.
Bayesian inference is an advanced analytical technique that improves the accuracy of predictions through a probabilistic approach. This method systematically updates existing predictions as new data is collected, and quantitatively assesses the uncertainty of each prediction.
Applying Bayesian inference to the manufacturing process allows for optimization that takes into account the interaction of process variables and continuously improves the predictive accuracy of the quality control system. In particular, when launching new products or introducing new processes with high uncertainty, more reliable predictions can be made by comprehensively considering existing similar cases.
Stochastic modeling is an approach that explicitly considers the randomness and variability of a system. By creating and analyzing various scenarios through Monte Carlo simulations, it is possible to set confidence intervals for predictions and quantify risks.
Bayesian networks are effective in identifying the dynamic characteristics of a system by modeling the causal relationships between complex variables. For example, in the manufacturing process, the effects of various variables, such as raw material quality, process temperature, pressure, and production speed, on the quality of the final product can be expressed in a probabilistic graph structure. This allows for a systematic analysis of the effects of changes in each variable on other variables and the derivation of optimal process conditions.
Hidden Markov models probabilistically model changes in the internal state of a system that cannot be directly observed from time series data. For example, it is possible to estimate the wear or deterioration of internal parts that cannot be directly determined from sensor data of manufacturing equipment.
This analysis allows for the prediction of equipment failure or process abnormalities in advance and the implementation of preventive maintenance. In particular, by learning the probability of state transition over time, it is possible to predict when and what kind of problems are likely to occur.
Real-time data analysis is a key technology that enables immediate response in a rapidly changing production environment. Sensor data is analyzed in real time using streaming data processing technology, and the model is continuously updated using an online learning algorithm.
The key to these advanced analytics technologies is to build an efficient large-scale data processing environment through the introduction of distributed processing architecture and edge computing. Although there are difficulties in introducing these technologies in terms of time and cost, if they are actually implemented, it will be possible to make quick decisions on site.
These advanced predictive analytics techniques each have their own advantages, and they can be used in a combination that is appropriate for the situation.
Bayesian inference is strong in managing uncertainty and integrating knowledge, while probabilistic modeling is effective in risk assessment and scenario analysis. Real-time data analysis plays a key role in situations that require immediate response.
However, the successful implementation of these advanced techniques requires high-quality data collection and management, and close cooperation between domain experts and data scientists. It also requires the establishment of an appropriate computing infrastructure and analysis platform.
When these elements are balanced, advanced predictive analytics techniques can make a real contribution to enhancing a company's competitiveness.
If you are curious about the application cases of deep learning prediction models by industry, please refer to “Application Cases of Deep Learning Prediction Models by Industry and Points to Consider When Introducing Them.” You can also find an analysis of the actual causes of failure of demand forecasting AI in “Why Demand Forecasting AI Fails.”
Open-source-based predictive analytics models are gaining attention for their high accessibility and rich community support. Each tool has its own unique characteristics and advantages and disadvantages, so it is important to make the right choice for your company's situation and purpose.
RapidMiner provides a suite of products for data analysts to build new data mining processes, set up predictive analytics, and more.
RapidMiner is an intuitive platform that enables complex analytics without coding. With a drag-and-drop interface and 500+ built-in algorithms, data analysts can quickly develop predictive models.
This makes it easy to use, especially for business analysts and domain experts.However, performance limitations can occur when dealing with large datasets,and the additional cost of advanced features can be prohibitive for small businesses and startups.
KNIME is a platform that allows you to design data analysis processes through visual workflows, offering more than 1,500 different analysis modules. It can be seamlessly integrated with programming languages such as R and Python, making it highly compatible with existing development environments.
However, due to its vast features and complex user interface, the initial learning curve is steep. This can be a major barrier to entry, especially for beginners to data analysis.
H2O.ai is an open source platform that provides automated machine learning capabilities. It automates the complex modeling process to reduce development time and has the scalability to be applied to various industries.
However, to effectively use the platform, a deep understanding of machine learning is required, and the initial setup process is complex, requiring professional technical knowledge.
Scikit-learn is a representative machine learning library in the Python ecosystem. It provides a variety of prediction algorithms and is characterized by high scalability, which allows it to be easily integrated with other data analysis tools in Python. To use this tool, Python programming skills are essential, and it has limitations in implementing the latest deep learning models.
TensorFlow is a powerful deep learning framework developed by Google, optimized for developing complex neural network models. It allows for flexible model building and has a large global developer community, providing a wealth of resources and support.
However, developing an effective model requires a deep understanding of deep learning and advanced programming knowledge, and the implementation process is relatively more complex than other tools.
Deepflow has implemented a multi-layered strategy to overcome the limitations of existing predictive analytics and dramatically improve accuracy. In particular, it is achieving differentiated predictive performance through advanced AI models, comprehensive data utilization, and a customized approach.
Deepflow is developing and using 224 different models to overcome the limitations of existing open source models, which is significantly higher than the 10-20 models used by typical enterprise AI teams.
In particular, we have overcome the limitations of a single model through the AI Stacking Ensemble Predictive Model. This approach combines multiple models hierarchically to maximize the strengths of each model and complement their weaknesses, enabling more reliable and accurate predictions.
We have also developed hybrid models optimized for specific industries or data environments to further improve prediction performance.
Deepflow has increased the accuracy of its predictions through comprehensive data collection and learning strategies. It has moved away from the previous method of relying solely on existing ERP data and is now using a wide range of external environmental data, including 1,700 macroeconomic data sets, 6 million trend data sets, 100 industrial data sets, weather data, and industrial special event data.
What is particularly noteworthy is that future environmental data is generated up to six months in advance and used for forecasting. This overcomes the limitations of the existing method of simply extrapolating past patterns and enables proactive reflection of future changes.
If there is a lack of data, the time series augmentation technique is used to supplement the training data to maintain the forecasting performance.
Each product or SKU has its own unique demand patterns, seasonality, price elasticity, etc. Deepflow creates an optimized model for each SKU to accurately capture these individual characteristics.
This minimizes the forecasting errors that can occur when using a general-purpose model and enables more sophisticated forecasting tailored to the characteristics of each item.
Deepflow effectively delivers these advanced technical capabilities through a user-friendly UI.
It provides visualizations of past and future demand trends, analysis of the causes of forecast increases and decreases, forecasts of inventory changes, and future-oriented insights so that users can intuitively understand and utilize the results of complex predictive models. This enables users to make quick and accurate decisions based on the results of the forecast.
The development of predictive analytics AI is opening up new horizons for business decision-making. The predictive accuracy of AI models is expected to improve by 20-50% over the next five years, which is expected to bring about a dramatic change in the operational efficiency and competitiveness of companies.
A particularly notable development is the improvement in predictive accuracy by industry. In the healthcare sector, disease prediction accuracy is expected to reach 90%, and investment risk assessment in the financial sector is expected to achieve an accuracy of 85%.
In the distribution sector, demand prediction accuracy is expected to improve to 75-80%. These developments are due to improvements in real-time data processing capabilities, the introduction of quantum computing, and the advancement of deep learning technology.
Deepflow has achieved a level of predictive performance that far exceeds that of existing predictive methods and general-purpose machine learning services. Its value is proven by the fact that it is leading to tangible business results, such as inventory management optimization, operational efficiency improvement, and cost reduction.
Deepflow's approach is setting a new standard for AI-based predictive analytics, which is gaining attention in the modern corporate environment where data-driven decision-making is becoming increasingly important.
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