You know demand forecasting is important, but many people don't know where or how to start. It's a common struggle for countless companies. Market research reports and consulting materials all emphasize the importance of demand forecasting, but they often fail to provide concrete, actionable plans tailored to a company's specific situation. A company with annual revenue of 5 billion KRW can't approach demand forecasting the same way as one with 500 billion KRW.
This is a key reason why many companies fail in their attempts to implement demand forecasting. They either blindly try to adopt a highly advanced solution without considering their own capabilities and resources, leading to project abandonment, or they stick with overly simplistic methods that yield no real results.
The common thread among successful companies is that they accurately assessed their current situation and chose a realistic methodology to match. Instead of trying to build a perfect system all at once, they built on small successes and progressed incrementally.
This article provides a realistic roadmap. It outlines the most efficient demand forecasting implementation methods by company size, detailing step-by-step approaches, potential pitfalls, and success factors based on real-world experience.
Realistic Approach: A Simple but Effective StartFor startups and small businesses with annual revenue under 10 billion KRW, the most important goal isn't a perfect system, but building a fundamental data-driven decision-making framework. The methodology should be effective even with limited resources and personnel.
First, you need to organize your existing data. Check if basic data—like sales figures, product-specific sales volumes, customer information, and inventory status—is being systematically collected. Many small businesses use POS systems or simple ERPs, but the data is often fragmented or inconsistent.
Excel or Google Sheets are sufficient. The important thing is to establish a routine for data accumulation on a regular basis, whether daily or weekly. You need at least 6 months to a year of data to find meaningful patterns.
Once the data is accumulated, start with simple analysis. The goal is to understand basic demand patterns by visualizing seasonality, day-of-the-week patterns, and monthly trends. Tools like Power BI or Tableau are helpful, but Excel's chart functions can also get the job done.
In particular, look for correlations with external factors. By understanding how the weather, public holidays, and events affect sales, you can create a much more accurate demand plan than with simple forecasting.
Instead of complex machine learning models, use proven statistical techniques like moving averages, exponential smoothing, or seasonal adjustments. Using Python or R is better, but you can get meaningful results with Excel's forecasting functions.
More important than forecasting accuracy is creating a continuous improvement process. Compare monthly forecasts with actual results, and analyze items with large discrepancies to improve your model.
The key to success for small business demand forecasting is consistency, not perfection. The most important thing is to establish a routine of updating data weekly and reviewing forecast results monthly. The CEO or leader's direct involvement is also crucial for the entire organization to become accustomed to data-driven decision-making.
A word of caution: don't be too ambitious. Instead of trying to get precise forecasts for every product and channel from the start, begin with the 10-20 core products that account for 80% of your revenue.
Mid-sized businesses with annual revenue ranging from 10 to 100 billion KRW often have basic IT infrastructure in place. The key at this stage is to integrate fragmented systems and build an automated forecasting process.
The biggest challenge for mid-sized businesses is integrating data scattered across various systems like ERP, CRM, SCM, and POS. You need to create a system that can integrate data in real-time by building a data warehouse or a cloud-based data lake.
Data quality management is critical in this process. You must address issues like the same product being managed with different codes across systems or inconsistent date formats. Establish data governance rules and create a system to continuously monitor data quality.
With integrated data, you can apply advanced analytics techniques. Use time series analysis, regression analysis, and cluster analysis to deeply understand demand patterns by customer segment, region, and channel.
This is also the stage where you can fully leverage machine learning models. You can significantly improve forecasting accuracy by applying algorithms like Random Forest, XGBoost, and LSTM. However, as models become more complex, their interpretability decreases, so you need to adjust the complexity to a level that business managers can understand.
The goal is to automate the entire process, including model training, evaluation, deployment, and monitoring. Build a system that automatically retrains the model when new data comes in and sends alerts if its performance drops.
Implementing A/B testing to compare the performance of multiple forecasting models and automatically select the best one enables continuous performance improvement.
At the mid-sized business stage, securing specialized personnel like data scientists or data analysts is important. However, if hiring is difficult, you can also build capacity by enhancing the skills of existing employees. Systematically develop data analysis capabilities through online or in-house training.
It's also crucial to create a system where domain experts from each department can participate in model development. Insights from the sales team, campaign information from the marketing team, and operational expertise from the production team must be reflected in the model.
Enterprise-wide Integration and Advanced Forecasting EcosystemLarge corporations with annual revenue over 100 billion KRW have complex organizational structures and diverse business areas. At this stage, you need to build a unified, enterprise-wide demand forecasting system and provide customized solutions that reflect the specific characteristics of each business unit.
Demand forecasting for a large corporation should be an integrated platform that supports strategic decision-making, not just simple sales forecasting. Design a scalable, cloud-based data architecture and build an infrastructure capable of real-time streaming data processing.
For global corporations, the data architecture must be able to conduct unified analysis while adhering to regional data sovereignty regulations. By adopting a microservices architecture, you can design a system that allows each business unit to develop models independently while creating enterprise-wide synergy.
At the large corporation level, it's possible to automate forecasting models using the latest deep learning technology and AutoML. Actively use cutting-edge technologies like Transformer-based time series prediction models, Graph Neural Networks for supply chain forecasting, and reinforcement learning for dynamic price optimization.
Adopting a specialized solution like ImpactiveAI's Deepflow allows you to automatically compare and evaluate 224 advanced machine learning models to select the best one. This enables customized forecasting tailored to the specific characteristics of each product line.
Beyond simply reporting forecast results, you need to build a system that supports business decisions in real time. The goal is a system that automatically suggests optimized decisions by linking demand forecasting with supply planning, pricing strategies, and marketing campaigns in real-time.
You can use Digital Twin technology to simulate various scenarios and perform "What-if" analysis to support strategic decisions. For example, you can preemptively predict the impact of new product launch timings, price policy changes, or marketing budget allocations.
For large corporations, establishing a COE (Center of Excellence) dedicated to demand forecasting is effective. Create a cross-functional team of data scientists, business analysts, and domain experts to systematically manage enterprise-wide forecasting capabilities.
You also need an AI governance framework that ensures the fairness, transparency, and explainability of the model. Especially in regulated industries, you must build a system that can track and explain the decision-making process of forecasting models.
Different approaches are necessary for each company size, but with the help of a professional demand forecasting solution, you can achieve your goals much more efficiently. ImpactiveAI's Deepflow is a flexible solution that can be applied regardless of a company's size or maturity.
Deepflow's core strength is its automated modeling process. You simply connect your data, and it automatically builds the optimal forecasting model through a feature selection process with 500 million possibilities. While it has 224 advanced models to meet the complex needs of mid-sized and large corporations, it also provides an intuitive interface that is easy for small businesses to use.
The AutoML feature, in particular, enables automatic data linking, periodic model updates, and automated process handling without any user intervention. This is a key feature that makes it usable for companies of all sizes, from small and medium-sized businesses with limited expertise to large corporations with complex requirements.
Looking at the actual performance, it shows consistent results regardless of company size. In a semiconductor company, it reduced inventory overstock by 80.68% and understock by 99.9%. A pharmaceutical company achieved 90.1% forecast accuracy.
The first principle for all companies, regardless of size, is a phased approach. It's important not to try to build a perfect system from the start but to accumulate small successes and expand gradually.
Starting with a pilot project to confirm clear results before expanding it company-wide is the safest and most effective method. Begin with your core product lines or key customers and expand the scope step-by-step.
No matter how advanced the algorithm, you won't get meaningful results if the data quality is low. However, it's also not ideal to delay a project by striving for data perfection. Starting with 80% data quality and continuously improving it is a more realistic approach. At the same time, it's crucial to incorporate business context that can't be learned from data alone into the model.
Implementing demand forecasting is not just a technical issue; it's an organizational culture change. Shifting from decision-making based on experience and intuition to one based on data is not easy. You need to approach this systematically from a change management perspective. Increase employees' data literacy through education and training, and share success stories to motivate them to embrace the change.
Demand forecasting is no longer an option—it is a necessity. Only companies with data-driven decision-making capabilities can survive in a rapidly changing market, regardless of their size.
The important thing is not to try to build a perfect system but to take the first step at a level you can start with right now. If you are a small business, start with Excel analysis. If you are a mid-sized company, start with integrating existing systems. If you are a large corporation, start with an enterprise-wide data strategy.
Demand forecasting capability isn't built overnight. You can only gain a competitive advantage through continuous investment and ongoing improvement. Starting now will determine your company's competitiveness in one, three, and even five years.
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