Data Preparation Checklist for Demand Forecasting Implementation

The first challenge companies face when considering demand forecasting system implementation is data preparation. Most companies have fundamental questions about whether their data is suitable for demand forecasting, and we frequently witness many AI projects failing during the data preparation phase.

Companies that have relied on traditional statistical methods still remain stuck with simple forecasting using past indicators, and the reality is that many companies still perform demand forecasting through manual Excel work. If you need detailed analysis of specific limitations of Excel-based inventory management, please refer to [Limitations of Excel Inventory Management and Changes Achieved Through AI Implementation].

In this situation, implementing AI-based demand forecasting systems becomes an innovative solution that can simultaneously realize two core values: improved forecasting accuracy and reduced inventory costs. For checkpoints that must be verified before implementing inventory management systems, please refer to [Five Essential Points to Check Before AI Inventory Management Implementation].

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Essential Data Types to Know Before Demand Forecasting Implementation

Essential Components of Internal ERP Data

The fundamental data for demand forecasting is sales and shipment history generated from internal ERP systems. Core data elements include date, product ID, category, price, and sales volume, with date-specific sales volume data serving as the starting point for time series pattern analysis.

Many companies worry about the quality or completeness of their data, but in reality, even incomplete data can be sufficiently utilized for AI model training. What matters more than data perfection is consistency and continuity. With over 24 months of historical data, meaningful predictive models can be built using only sales volume information consistently recorded on monthly or weekly basis. Particularly in the retail industry, which is significantly affected by promotions, richer data enables more accurate predictions.

Strategic Utilization of External Environmental Data

Internal data alone has limited responsiveness to market changes or external shocks. ImpactivAI's Deepflow maximizes predictive performance by utilizing over 50,000 internal and external data points, including environmental data, economic indicators, and market trends.

Environmental data analyzes the impact of seasonality and climate change on demand. For products with strong seasonal characteristics like agricultural products or clothing, weather data has decisive influence on forecasting accuracy. Macroeconomic indicators such as GDP growth rate, inflation rate, and consumer confidence index reflect overall market sentiment and purchasing power changes, enabling more accurate understanding of mid to long-term demand trends.

Solutions for Data Shortage Situations

For new products or seasonal products that lack sufficient historical data, augmented data and synthetic data technologies provide solutions. For methods to successfully build AI prediction models even in data shortage situations, please refer to [SME-Tailored Demand Forecasting That Can Start Even with Insufficient Data]. Deepflow analyzes patterns of similar products and reinforces training data by simulating various scenarios.

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Realistic Challenges in Data Processing During Demand Forecasting Implementation

Complexity of Different Data Structures Across Clients

Even after solving data shortage problems, another obstacle awaits during actual implementation. Each client has completely different data structures. In the case of clothing companies, even identical designs generate separate SKUs for different sizes and colors, resulting in very limited sales data per individual item. Conversely, chemical or steel companies have relatively simple data patterns with large-volume transactions concentrated on a few main products.

Traditional AI solutions have used methods that convert customer data to internal standard structures, but this process requires clients to significantly modify their existing data systems, and when new customers are added, existing customers' data must also be reorganized. Deepflow systematically supports converting customer-owned data to the most suitable form for predictive models to solve this complexity. This problem significantly increases implementation costs and complexity, causing many projects to be discontinued. For specific reasons why demand forecasting AI implementation fails and solutions, please refer to [Reasons Why Demand Forecasting AI Implementation Fails] and [Innovation Points for the Future Through Demand Forecasting Failure Cases].

Data Quality Management and Consistency Assurance

Data quality issues are decisive factors that determine demand forecasting system performance. The same product is frequently registered with different codes across different periods, or classification criteria change due to personnel changes. Particularly in systems dependent on manual input, typos and omissions inevitably occur.

Realistically, it is often difficult to significantly modify existing systems, so the ability to flexibly handle such problems at the AI solution level is important.

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Deepflow's Innovative Data Processing Approach

Data Architecture That Chooses Flexibility Over Standardization

ImpactivAI's Deepflow has taken a fundamentally different approach to overcome the limitations of existing solutions.

Instead of forcibly standardizing customer data, we provide standardized data formats and offer systematic support to convert customers' time series sales data to the most suitable form for predictive models. Dedicated data agents collaborate with client companies to analyze time series data structures, identify core elements such as dates, product IDs, and sales volumes, and support the process of organizing them in optimized forms for predictive models.

In other words, even if column names or date formats differ, we accurately identify time series patterns and suggest appropriate conversion methods, allowing client companies to build effective demand forecasting systems without significantly changing their existing ERP systems.

AutoML-Based Automated Model Optimization

Deepflow's differentiation lies in its AutoML system that automatically tests over 200 different prediction models and finds optimal combinations. During this process, feature selection of up to 500 million cases is automatically performed. Among hundreds of variables, only factors that have meaningful impact on predictions are selected to maximize model performance. For in-depth content about the principles behind Deepflow's high forecasting accuracy achievement, please refer to [Reasons Why Deepflow Could Achieve High Demand Forecasting Accuracy].

Additionally, models are automatically retrained whenever data is updated, continuously improving forecasting accuracy. When market condition changes or new patterns are detected, they are immediately reflected in the model, ensuring always up-to-date prediction results without separate maintenance.

Industry-Specific Data Preparation Strategies and Implementation Considerations Before Demand Forecasting Implementation

Manufacturing Industry's Production Planning Integration Data

When implementing demand forecasting in manufacturing, it's necessary to consider not only sales data but also production plans and raw material procurement schedules. For industries with high raw material price volatility, it's effective to utilize Deepflow's price prediction module together. In steel or chemical industries where raw material costs directly impact product prices, integrated analysis of raw material price outlook and demand forecasting enables more accurate business planning.

Retail Industry's Channel-Specific Demand Pattern Analysis

In the retail industry, demand patterns differ across various sales channels such as online and offline, directly-operated and franchise stores. Segmented forecasting reflecting each channel's characteristics is needed, and channel-to-channel inventory movement or mutual complementary effects must also be considered. For the necessity of implementing AI demand forecasting systems specialized for retail, please refer to [Why Retail Industry Should Implement AI Demand Forecasting Systems].

Particularly for trend-sensitive products like clothing or cosmetics, utilizing social media data or search trend information can significantly improve forecasting accuracy. Deepflow automatically collects and analyzes such external signals to detect market trend changes early.

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Data Strategy for Successful Demand Forecasting Implementation

Practical Strategies for Successful Demand Forecasting Implementation

Key Confirmation Items During Pre-Implementation Preparation

To successfully implement demand forecasting systems, clear goal setting and realistic expectation adjustment are more important than technical aspects. Many companies vaguely aim for "improved forecasting accuracy," but specific KPIs such as inventory cost reduction, stockout rate decrease, and production planning optimization must be clearly defined.

Data verification is also important. Based on items confirmed in the previous checklist, it's advisable to carefully examine with experts whether currently held data is sufficient for demand forecasting and how insufficient areas can be supplemented.

💡 Need systematic readiness assessment?
→ Use the Data Preparation Checklist for Demand Forecasting Implementation (Free Download) to directly check 43 items across 6 core areas. You can objectively assess your company's current preparation status.

Particularly for products with strong seasonality or items heavily influenced by promotions, simple sales volume data alone has limitations, making external environmental data integration essential.

Actual Prediction Performance Verification Through PoC

After completing theoretical review, a small-scale PoC (Proof of Concept) project is needed to verify actual prediction performance. Select 5-10 core products rather than the entire product range, train prediction models with historical data, and conduct comparative verification with actual sales results.

During the PoC phase, it's important to test not only forecasting accuracy but also how prediction results will be utilized in practice. It's advisable to check how field personnel react when prediction values differ significantly from existing experience or intuition, and whether decision-making based on prediction results is actually effective.

Practical Application and Continuous Improvement System Construction

After passing PoC verification, the key is creating an environment where field personnel can easily understand and utilize prediction results. No matter how accurate predictions are, if personnel cannot understand "why such results occurred," they are difficult to use in decision-making.

Deepflow's latest LLM-based insight report feature is an innovative function that solves this problem. Rather than simply providing "next month's sales forecast," it comprehensively analyzes seasonal pattern detection through 3-year demand trend analysis, recent 3-month surge/decline trend analysis, and trends of external environmental factors that currently have the greatest impact on products.

Particularly, it presents customized risks and opportunity factors for Sales, Marketing, and SCM teams, and even suggests specific Action Plans for achieving each department's KPIs, helping field personnel focus only on strategic decision-making without spending time on complex data interpretation.

Continuous Improvement of Forecasting Accuracy Through Data Feedback

The real value of demand forecasting systems comes from continuous learning and improvement. Beyond improving models by analyzing differences between predictions and actual results, prediction models must be updated to reflect market condition changes and new business factors.

Even when predictions miss the mark, it's important to clearly distinguish whether the cause is data shortage, model limitations, or unpredictable external shocks (pandemic, supply chain issues, etc.) and prepare appropriate countermeasures. Only when such feedback systems are properly established can demand forecasting systems become core competitive advantages rather than simple tools.

🎯 Check Your Company's Demand Forecasting Readiness Right Now

Data preparation for demand forecasting implementation is both a technical and organizational challenge. Rather than waiting for perfect data, starting with currently available data while improving prediction quality through clear goals and systematic verification processes is a realistic approach.

▶️ Download Demand Forecasting Implementation Checklist (Free)

• Check 43 detailed items across 6 core areas
• Includes industry-specific preparation requirements
• Immediately usable Excel format

After completing the checklist, if you want expert assessment of whether actual demand forecasting is possible with your company's data, establish customized implementation strategies through Deepflow free consulting. Utilizing flexible AI solutions like Deepflow enables building effective demand forecasting systems without complex data standardization processes, which will make decisive contributions to corporate digital innovation and competitiveness enhancement.