Demand Forecasting Solution Implementation Guide for Efficient S&OP Development
As global supply chain uncertainties increase, the importance of S&OP (Sales & Operations Planning) processes has become more critical than ever. An efficient S&OP process goes beyond simply coordinating sales and production to become a core management activity that simultaneously enhances corporate profitability and customer satisfaction. Particularly, accurate demand forecasting serves as the foundation of the entire S&OP process, playing a decisive role in maintaining appropriate inventory levels and establishing production plans.
Diagnosing Limitations of Current S&OP Processes
Inventory Management Failures Due to Inaccurate Demand Forecasting
Currently, most companies use traditional demand forecasting methods that rely on experience and intuition, and inventory management failures caused by this approach are resulting in serious cost losses.
Many companies rely solely on simple statistical approaches or past pattern analysis to forecast demand, which fails to properly reflect rapidly changing market environments or external factors, leading to decreased forecasting accuracy. Particularly in exceptional situations like pandemics or economic crises, past patterns become completely ineffective, sometimes paralyzing forecasting systems entirely.
Such inaccurate forecasting produces two fatal consequences. In cases of excess inventory, storage costs, disposal costs, margin losses from discount sales, and even opportunity costs occur. Particularly for fashion or electronics products that are sensitive to trends or experience rapid technological changes, value drops dramatically over time, causing enormous losses. Cases like a Vietnamese fashion company experiencing serious cash flow problems due to inaccurate demand forecasting are frequent.
Conversely, inventory shortages bring additional costs from emergency orders, direct sales losses, and most critically, declining customer trust. Considering that new customer acquisition costs are 5-25 times higher than existing customer retention costs, customer defection due to inventory shortages means greater long-term losses.
Limitations and Inefficiencies of Manual Data Analysis
The biggest bottleneck in traditional S&OP processes is the manual data processing approach. This method has three fundamental limitations.
First, there are limits to data processing speed. In typical companies, the time required for personnel to extract data from various systems, organize it in Excel, and analyze it to prepare for monthly S&OP meetings averages 40-60 hours. This is not simply a labor cost issue, but results in delayed decision-making until analysis results are available, causing companies to miss rapidly changing market opportunities.
Second, there is a lack of consistency in analysis. Since different personnel use different standards and methodologies for analysis, completely different results can emerge from the same data. If Personnel A applies seasonality adjustments while Personnel B uses simple averages, the reliability of forecasting results drops significantly. This causes decision-making confusion within organizations and undermines strategic consistency.
Third, there are limitations in analyzing complex variables. Factors that actually influence demand are very diverse and complex, including seasonality, trends, economic indicators, competitor dynamics, marketing activities, and weather changes. All these variables interact with each other to form demand patterns, and it is nearly impossible for human cognitive abilities to perform accurate analysis considering dozens of variables simultaneously.
Organizational Conflicts Due to Interdepartmental Forecasting Discrepancies
The most frequent problem occurring in S&OP meetings is that sales teams and inventory management teams present their respective plans based on different forecasting figures. Such forecasting discrepancies go beyond simple numerical differences to cause serious organizational conflicts and inefficiencies.
Sales teams, pressured to achieve sales targets and optimistic about market opportunities, usually present optimistic sales plans. Meanwhile, inventory management teams prefer conservative forecasting figures based on historical data and realistic constraints, due to the burden of direct losses from excess inventory.
These perspective differences turn S&OP meetings into venues for unproductive arguments. When inventory is secured according to sales team plans but actual sales are poor, inventory management teams must bear the burden of handling excess inventory. Conversely, when inventory is secured according to conservative inventory team forecasts but higher-than-expected demand occurs, sales teams receive criticism for missed opportunities.
A more serious problem is that repeated situations accumulate interdepartmental distrust. Inventory management personnel become reluctant to present forecasting figures hastily, while sales teams express dissatisfaction with the inventory team's "passive attitude." Eventually, political considerations and responsibility avoidance rather than objective data drive decision-making, significantly reducing overall organizational efficiency.
The root cause of such conflicts lies in each department performing individual forecasting based on different information and methodologies. Without unified forecasting standards and objective data analysis systems, such inefficiencies will continue to repeat.
S&OP Process Innovation Through AI-Based Demand Forecasting
Accurate Demand Forecasting Provided by Machine Learning
AI-based demand forecasting systems differentiate themselves from traditional methods through multidimensional pattern recognition capabilities. While existing statistical models consider 2-3 variables, AI simultaneously processes dozens to hundreds of variables, and Impactive AI's Deepflow analyzes 500 million combinations using over 50,000 internal and external data points.
Particularly, AI enhances forecasting accuracy by recognizing non-linear relationships (such as S-curves between temperature and beverage demand). In time series data analysis, Transformer-based models learn long-term temporal dependencies and seasonality, providing much more sophisticated forecasting than traditional methods.
Real-time Market Change Response and Automatic Updates
The second advantage of AI-based demand forecasting systems is real-time learning and automatic update functionality. Traditional methods see decreased forecasting accuracy during market changes, require significant time for manual adjustments, and can become ineffective in situations like pandemics. Impactive AI's Deepflow operates over 200 models through ensemble learning, enhancing forecasting stability to help S&OP personnel respond swiftly to market changes.
Building Individualized Forecasting Models by SKU
AI-based demand forecasting enhances accuracy through customized models for each product. Traditional methods applied the same formula to all products, resulting in decreased accuracy. Deepflow analyzes each SKU's historical sales data to learn unique patterns considering product lifecycle, price elasticity, seasonality, and promotional effects.
Implementing Efficient S&OP Processes with Impactive AI Deepflow
Forecasting Accuracy of 224 High-Performance AI Models
The core competitive advantage of Impactive AI Deepflow lies in possessing 224 diverse AI models. This means more than simply having many models - it provides specialized algorithms optimized for different data characteristics and forecasting situations.
Particularly noteworthy among these are the latest Transformer-based models. I-transformer is a model that effectively captures long-term dependencies in time series data, capable of learning patterns over much longer time ranges than existing LSTM or GRU models. TFT (Temporal Fusion Transformers) provides functionality to automatically evaluate the importance of each variable while simultaneously considering temporal and static characteristics. This enhances interpretability of forecasting results, enabling business personnel to trust and utilize AI forecasting more effectively.
Looking at actual performance metrics makes their excellence even clearer. In raw material price forecasting, Deepflow achieved 99.30% accuracy for 1-week forecasts, 99.07% for 2-week forecasts, and 98.60% for 3-week forecasts. This represents revolutionary levels compared to traditional statistical models that typically show 70-80% accuracy.
More importantly, these high-performance models operate in a competition-based manner. Multiple models simultaneously perform forecasting on the same data, and the system automatically selects the best-performing model or derives final forecasts through weighted averaging of multiple model results. This significantly improves overall forecasting stability and accuracy through ensemble effects where other models compensate for specific model weaknesses.
Core Functions of Deepflow Forecast
Deepflow Forecast is designed as a comprehensive demand management platform beyond a simple forecasting tool. Its core functions are specifically as follows.
The first notable function is medium to long-term forecasting capability ranging from 6 to 12 months. While most existing systems focus on 1-3 month short-term forecasting, Deepflow Forecast provides long-term prospects necessary for annual business planning and strategic decision-making. This provides particularly important value for products with strong seasonality or new product launch planning.
The second core function is a real-time Business Intelligence dashboard. Through an intuitive web-based interface, it visually highlights products expected to have inventory shortages or excess. S&OP personnel can identify products requiring attention at a glance without complex data analysis, and can even check the severity level of inventory issues for each product.
Third, the system's automation level is very high. Data agents automatically integrate with ERP systems to collect sales data, inventory data, and production data in real-time, while model learning and forecasting processes are completely automated. Users can obtain accurate forecasting results without separate data processing or model tuning work.
Basic Inventory Integration and Inventory Depletion Days Management
One of Deepflow Forecast's differentiated functions is inventory depletion day forecasting through real-time integration with basic inventory data. This provides practical insights that can be directly utilized in actual inventory operations beyond simple demand forecasting.
The system accurately predicts when inventory for each SKU will be depleted by combining current inventory holdings with expected sales volumes. For example, if Product A's current inventory is 1,000 units and future weekly average sales are expected to be 150 units, it predicts inventory depletion in approximately 6.7 weeks. However, the important point is that this is sophisticated forecasting considering seasonality, promotional plans, market trend changes, rather than simple division.
Furthermore, the system simultaneously considers safety stock levels. It automatically calculates appropriate safety stock levels by analyzing different demand volatility and supply lead times for each product, and suggests actual ordering points based on this. This enables S&OP personnel to prevent inventory shortage risks in advance while minimizing unnecessary inventory holding costs.
Particularly noteworthy is the multi-stage supply chain inventory cascade analysis function. It considers BOM (Bill of Materials) relationships among raw materials, semi-finished products, and finished products to reverse-calculate and show the impact of finished product demand changes on upper-stage inventory. This provides very useful information for integrated inventory planning.
Verified Performance Metrics
Actual performance data from companies that implemented Deepflow Forecast clearly demonstrates this solution's value. The most notable achievement is inventory shortage and excess reduction effects, achieving an average improvement of 33.4%.
Looking at specific cases, a Vietnamese fashion apparel company had 70% of all products in excess inventory status before Deepflow implementation. Due to the fast-changing nature of trends in the fashion industry, accurate demand forecasting was difficult, causing enormous inventory losses. After Deepflow implementation, excess inventory decreased by 10-20% monthly, and normal inventory turnover was recovered within 6 months.
Another notable achievement is improved new product planning success rates. The company plans to add a new product forecasting module in the middle of this year to increase new product planning success rates from 35% to over 70%. This is possible because of functionality to evaluate new product success possibilities in advance through analysis of past performance patterns of similar products and market trends.
In terms of cost reduction, one client achieved remarkable results of saving 24.8 billion won monthly in inventory costs. This resulted from a combination of excess inventory reduction, emergency order cost savings, and disposal loss reduction. In terms of work productivity, improvements of up to 99% were shown, because data analysis work previously handled manually became completely automated.
Resolving Organizational Conflicts Through Unified Forecasting Standards Across Departments
One of Deepflow's most important values is enabling sales teams and inventory management teams to share the same objective forecasting figures. AI-based neutral forecasting results block opportunities for departmental interests or subjective judgments to intervene, providing a foundation for rational data-based decision-making.
Specifically, in S&OP meetings, sales teams and inventory management teams no longer argue with different forecasting figures. Instead, they can focus on "how should we respond if this forecast is correct" based on unified demand forecasts provided by Deepflow. Sales teams plan marketing strategies and sales activities to achieve forecasted demand, while inventory management teams establish optimal inventory levels and ordering plans based on the same forecasting figures.
Particularly, the explainability function for forecasting results plays an important role in building organizational trust. Deepflow not only provides forecasting figures but also clearly shows the basis for those forecasts and major influencing factors. For example, it provides specific analysis results like "March Product A demand is expected to increase 15% compared to the previous month due to seasonal factors (40%), competitor price increase effects (30%), marketing campaign effects (20%), and others (10%)."
This enables each department to understand forecasting results more deeply, and even when forecast adjustments are needed, constructive discussions can proceed based on objective evidence. Since responsibility for forecasting accuracy lies with the system and data rather than specific individuals or departments, opportunities for interdepartmental conflicts are significantly reduced.
S&OP Process Construction Strategy for Deepflow-Based Practical Implementation
Phased Implementation Approach
Building Deepflow-based S&OP processes requires systematic phased approaches. For successful implementation, customized roadmaps must be established after understanding current situations.
Phase 1 is data infrastructure construction and quality improvement. Since AI system performance is directly linked to data quality, securing accuracy and completeness of sales/inventory/production data from existing systems (ERP, POS, WMS, etc.) is important. Particularly, consistency in product codes, client information, and date formats is required.
Phase 2 is pilot project operation. Starting small with some product lines that are easy to forecast and have good data quality instead of entire product lines, focusing on securing system adaptation time and discovering/solving problems in advance.
Phase 3 is gradual expansion. Based on verified pilot models/processes, gradually expanding target product lines and functions while pursuing product-line-specific model tuning and organizational capability growth.
Phase 4 is utilizing advanced functions. After basic demand forecasting stabilization, sequentially introducing advanced functions like inventory optimization, new product planning support, and price forecasting, while building AI forecast-based automated decision-making rules to maximize system effectiveness.
Utilization in Monthly S&OP Meetings
The web-based dashboard provided by Deepflow dramatically improves monthly S&OP meeting efficiency. Reports previously prepared individually by each department can now be checked in real-time on one integrated platform.
In the meeting preparation stage, exception reports automatically generated by the system are utilized. Products expected to have inventory shortages or excess, products with large differences between forecasts and actual results, and products where new trends are detected are automatically highlighted and reported. This enables meeting participants to focus only on truly important issues.
During meeting proceedings, interdepartmental collaboration is greatly improved. In the past, sales team plan presentations and inventory team plan presentations proceeded separately, often leading to arguments due to conflicting content. However, after Deepflow implementation, constructive discussions about how each department will contribute become possible based on common forecasting figures.
New Product and Seasonal Product Management
Deepflow revolutionarily solves new product demand forecasting where no historical data exists. It applies existing product patterns to new products through transfer learning and forecasts by reflecting unique characteristics like price, target, marketing, and distribution channels. For seasonal products, it considers long-term trends, economic cycles, weather, and even climate change through multi-cyclical analysis. Products with short lifecycles operate separate lifecycle prediction models, automatically applying optimal algorithms for each product stage.
Considerations for Successful Implementation
Securing Data Quality and Completeness
Success in AI-based demand forecasting depends on data quality.
Data quality assurance consists of three stages. First, data completeness must be checked to secure at least 2-3 years of complete daily sales data without gaps. Next, to secure data accuracy, product code consistency, sales volume and amount consistency, and return/exchange processing accuracy must be checked, and outliers must be verified using statistical detection techniques. Finally, external data such as economic indicators, weather, competitor trends, and social media trends must be systematically integrated along with internal data.
Deepflow utilizes over 50,000 internal and external data sources to achieve high forecasting accuracy. Even when current client companies have limited data or inadequate classification systems, it enables sophisticated demand forecasting by utilizing various data sources.
Organizational Culture and Process Integration
The biggest obstacle to AI implementation is often organizational resistance rather than technical problems. Particularly, veteran employees who have long relied on experience and intuition frequently distrust AI forecasting results.
For successful organizational integration, hybrid approaches are effective. Rather than accepting AI forecasting results as absolute truth, they should be used as tools to complement domain experts' experience and knowledge. For example, override functions are provided that allow field personnel to adjust AI-suggested forecasts considering market conditions.
In terms of change management, phased implementation and sufficient education are important. Initially, AI forecasting results are used only as reference information, then gradually as major bases for decision-making as trust levels increase. Additionally, explainable AI functionality should be actively utilized to make AI operational principles and forecasting bases easily understandable.
If you're curious about strategies to reduce field personnel resistance and successfully implement demand forecasting solutions smoothly, please refer to "Practical Strategies for Overcoming Field Resistance in AI Prediction Solution Implementation" and "Methods to Reduce Field Personnel Resistance When Implementing AI."
Return on Investment Analysis
AI demand forecasting system implementation requires considerable initial investment, but its effects are also very large and diverse. ROI analysis must consider both quantitative and qualitative effects. Quantitative effects include inventory cost and labor cost reduction, and market opportunity expansion through improved decision-making speed. Qualitative effects include improved objectivity and consistency in decision-making, establishment of data-driven culture, and enhanced customer service levels, which contribute to long-term corporate competitiveness strengthening.
Looking at cases from actual implementing companies, most recover investment costs within 12-18 months. Particularly, companies with low inventory turnover rates or many product types tend to show greater effects.
Taking the First Step to Prepare for the Future of Data-Driven S&OP
Efficient S&OP processes are no longer optional but essential. In today's business environment where global supply chain uncertainties are increasing and customer demands are diversifying, accurate demand forecasting based on data and rapid decision-making capabilities have become core elements of corporate survival.
AI-based demand forecasting solutions provide powerful answers to these challenges. Efficient S&OP processes built through specialized solutions like Impactive AI Deepflow enable simultaneous achievement of inventory optimization and cost reduction, as well as customer satisfaction improvement and market responsiveness enhancement.
Most importantly, starting right now is crucial. Considering the pace of AI technology development, gaps between early-adopting companies and late-adopting companies will widen over time. The journey toward successful S&OP process innovation begins with accurate demand forecasting, and now is the optimal time to leverage AI power to build more advanced S&OP systems.
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