Demand Forecasting Costs: Do Higher Costs Mean Better Results?

Efficient investment in demand forecasting costs is a core competitiveness that determines corporate survival and growth. Many companies are paying attention to this "management compass" that increases capital efficiency through appropriate inventory securing, doesn't miss sales opportunities, and prevents waste from overproduction.

However, one important question arises here: "Does demand forecasting become more accurate as more costs are invested?"

The answer is surprisingly 'no'. Between investment in demand forecasting systems and their effects, there exists not a simple proportional relationship, but a complex nonlinear relationship. This is because the 'law of diminishing returns' operates, where accuracy improvement becomes minimal even when input costs increase beyond a certain point.

Now is an era where 'how to invest wisely' rather than 'how much to invest' is the key. In the digital transformation era led by big data and AI, strategic approaches are needed to efficiently manage demand forecasting costs while achieving optimal effects.

This article will examine the pitfalls of demand forecasting costs, efficient investment strategies, and practical solutions that can achieve maximum effects with minimum costs. In particular, we will verify through actual cases how ImpactiveAI's 'Deepflow' solution, which can maximize cost-effectiveness, is solving corporate demand forecasting challenges.

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Is Investment in Demand Forecasting Costs Always the Right Answer?

Cost Factors to Consider When Introducing Demand Forecasting Systems

If companies skimp on demand forecasting investment, accuracy may decline, causing major disruptions to business management. Conversely, blindly increasing demand forecasting costs doesn't proportionally improve accuracy. Diminishing effects inevitably occur beyond a certain level between investment scale and prediction performance.

Let's examine the major cost factors companies face when introducing demand forecasting systems.

- Initial Implementation Costs: Software licenses, server/cloud infrastructure, data integration and migration

- Operation and Maintenance Costs: System updates, daily management, technical support, cloud usage fees

- Human Resource Costs: Hiring and training costs for data scientists, analysts, and IT personnel

- Data Acquisition and Quality Management Costs: External data purchase, data cleansing, and verification costs

- Opportunity Costs: Time required for in-house development, losses from prediction errors, decision delay costs

Ultimately, what's important is effectively allocating given demand forecasting costs to maximize investment efficiency. This requires a strategic approach that combines strengthening internal data analysis capabilities with appropriate utilization of external verified demand forecasting solutions.

Particularly when considering AI-based demand forecasting system adoption, costs alone cannot be the only consideration. The following factors become key determinants of long-term success and ROI:

- Business Process Integration Ease: No matter how excellent a prediction model is, it cannot demonstrate practical value if it doesn't integrate smoothly with existing business processes. The possibility of integration with existing systems such as corporate ERP and SCM must be thoroughly reviewed.

- Scalability and Flexibility: Business environments constantly change. If large additional investments are needed whenever new products, markets, or channels are added, cost burden increases in the long term. Solutions with scalable architecture should be selected.

- Interpretability of Prediction Results: Complex AI models like 'black boxes' may show high accuracy but are difficult to understand why such predictions were made. Interpretability must be ensured so decision-makers can trust and utilize model results.

- Change Response Capability: How quickly models adapt and recover accuracy when extreme events like the COVID-19 pandemic occur is important. Lack of such resilience can actually lead to wrong decisions in crisis situations.

- Contribution to Internal Capability Enhancement: External solutions may help short-term performance improvement, but in the long term, they should contribute to strengthening organizational data analysis capabilities. Whether knowledge transfer and internal expert development are possible should be reviewed.

Data Quality Management and Optimal Prediction Model Selection Are Key

Ultimately, what's important is effectively allocating given demand forecasting costs to maximize investment efficiency. This requires a strategic approach that combines strengthening internal organizational data analysis capabilities with appropriate utilization of external verified demand forecasting solutions.

For demand forecasting advancement, securing high-quality data must come first. No matter how high the cost investment, accurate prediction is difficult to expect if basic data is poor.

Additionally, selecting prediction models suitable for each business situation is very important. Simply introducing the latest algorithms doesn't guarantee prediction accuracy. A process of comparing and verifying various models along with deep understanding of industry domains is necessary.

Strategic Combination of Internal Capability Enhancement and External Solution Utilization

For demand forecasting advancement, strategies beyond simply increasing investment scale are needed. First, strengthening internal organizational data and analytics capabilities is important. Continuous prediction accuracy improvement can be achieved by nurturing professional personnel and systematizing analysis processes.

Simultaneously, strategically utilizing external specialist solutions is also an effective method. Through collaboration with companies that have verified technological capabilities in the demand forecasting field, visible results can be achieved in a short time. The advantage of reducing risks and opportunity costs associated with in-house development is also significant.

Actively utilizing verified external demand forecasting solutions like ImpactiveAI's 'Deepflow' while strengthening internal data analysis capabilities can be a wise choice. This is because high-level prediction accuracy can be secured in a short time while minimizing risks and opportunity costs associated with in-house development.

From a TCO perspective, introducing demand forecasting solutions like Deepflow can be a sufficiently reasonable investment alternative. Thanks to its flexible structure, customization according to corporate individual needs is possible, and considerable cost reduction effects can be expected in the long term.

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Why You Should Choose Deepflow If Demand Forecasting Cost Efficiency Is a Concern

Application of High-Performance Prediction Models Based on Deep Learning

Deepflow possesses over 24 prediction models including various deep learning and advanced time series algorithms such as DilatedRNN, GRU, TCN, LSTM, BiTCN, and NBEATSx. Among these, models optimized for each SKU's characteristics are automatically selected.

General demand forecasting solutions often use only limited models or apply the same algorithm to all products.

In contrast, Deepflow maximizes prediction accuracy without additional costs by automatically selecting and applying optimal models suited to each product's characteristics and patterns. This reduces the need for companies to separately hire data scientists or invest in additional model development.

Extensive Data Learning Capabilities

Deepflow's differentiated strength lies in its data engineering capability to collect and learn over 6 million external environment data points. It integrates and analyzes over 1,700 variables including price economic indicators, consumer sentiment price indicators, and industry event variables, over 6 million trend data points, over 100 industry data points, and over 20 meteorological data points.

This extensive data learning enables companies to utilize vast external data that would be difficult to secure independently without additional costs. It minimizes prediction errors by accurately analyzing hidden patterns and external variable influences that are difficult to identify with existing ERP data alone.

Optimal Parameter Selection Based on Optimization Algorithms

Deepflow applies optimization technology utilizing genetic algorithms. This technology has the capability to automatically select optimal parameters from over 500 million parameter combinations.

It improves accuracy while reducing unnecessary data processing and computation by selecting and learning only core factors that substantially affect prediction from vast data.

Generally, machine learning model parameter tuning is done manually by data scientists, requiring considerable time and costs. Assuming it takes an average of 2-3 weeks for one professional data scientist to optimize one model, companies with thousands of SKUs could incur hundreds of millions of won in labor costs for this work alone.

Deepflow's automated parameter optimization eliminates the need for such manual tuning, significantly reducing labor costs and shortening model development and update cycles.

Efficiency Maximization Through Product-Specific Customized Model Application

Deepflow automatically selects and applies optimal prediction models for each SKU. Through systematic processes from data preprocessing to normalization, missing value handling, and model training, it provides customized prediction models suited to each product's characteristics.

This customized approach improves accuracy compared to general-purpose models, simultaneously reducing inventory costs and opportunity loss costs. It prevents duplicate system investments by eliminating the need to develop separate systems for each product group, and minimizes initial launch cost losses as prediction models quickly stabilize even after new product launches.

ROI-Centered Approach

In a reality where most AI projects don't lead to substantial business value creation, Deepflow was designed based on the philosophy of 'look at value (ROI) rather than technology'.

Deepflow focuses on business models and substantial profit generation beyond technical performance of algorithms and data. Substantial inventory cost reduction and opportunity loss prevention effects compared to system implementation costs are clearly measured, providing integrated value across business decision-making including safety stock setting and production plan optimization rather than simple prediction accuracy.

Strategic Measures for Demand Forecasting Cost Reduction

Expanding cost investment to improve demand forecasting accuracy is an inevitable choice. However, accuracy doesn't increase proportionally just because massive budgets are invested. Rather, strategic approaches that can maximize return on investment are required.

Introducing excellent external solutions like ImpactiveAI's Deepflow is a viable alternative, but strengthening internal organizational capabilities is also a core point that cannot be overlooked. From now on, let's examine practical measures that can increase prediction accuracy while reasonably managing demand forecasting costs.

Efficient Investment Through Establishing Data Governance Systems

The core of demand forecasting advancement is stably securing and managing high-quality data. No matter how excellent a prediction model is developed, accurate prediction is difficult to expect if basic data is poor.

Therefore, establishing enterprise-wide data governance systems must come first. This means establishing standardized policies and processes covering the entire process from data collection, storage, processing, to utilization. Through this, data quality can be improved and data silos between departments can be resolved.

Furthermore, systematic investment in infrastructure and personnel for data management should also be made. Rather than simply investing large-scale budgets, wisdom is needed to allocate resources in directions that can maximize data asset value from a long-term perspective. This can become the foundation for achieving high investment efficiency while reducing demand forecasting costs.

Securing Self-Reliance Through Internalizing AI-Based Demand Forecasting Platforms

Building AI-based demand forecasting platforms internally within companies is also an important task. Rather than relying entirely on external solutions, long-term competitiveness can be enhanced by securing independent capabilities.

For this, first securing and nurturing data science and AI professional personnel within the organization is necessary. Beyond simply recruiting a few experts, the overall capability level of the organization should be improved through systematic education and training.

Additionally, efforts to independently develop and advance prediction models suitable for business domains should be pursued in parallel. It's important to accurately reflect workplace needs and internalize optimal algorithms through continuous experimentation and verification.

Of course, considerable demand forecasting costs may be involved in the short term. However, internal capabilities secured through such investment will become a solid foundation for continuous prediction accuracy improvement in the future. This establishes a foundation for making optimal decisions while reducing dependence on external solutions.

Setting Investment Priorities Centered on Business Sites

Strategically determining where and how to invest limited demand forecasting budgets is also important. Simply increasing budget scale is not the answer; areas that can maximize investment effects must be selected.

For this, it's necessary to listen to voices from business sites. Opinions of personnel actually performing demand forecasting work should be actively collected, and characteristics and needs of each business unit should be thoroughly analyzed. Through this, factors that can have the greatest impact on prediction accuracy improvement can be identified, and investment priorities can be set accordingly.

For example, if raw material price volatility has a large impact on demand forecasting for Business Unit A, concentrated investment in related data acquisition and analysis would be appropriate. If Business Unit B is sensitive to external factors like competitor promotions, it could focus on developing specialized models for that.

Such strategic focus and concentration in cost input for wise demand forecasting budget operation is needed. A balanced approach that considers individual characteristics of each business unit while improving enterprise-wide investment efficiency is required.

When Reviewing Deepflow Introduction from a TCO Perspective

If introducing AI solutions for demand forecasting feels burdensome, approaching from a long-term perspective is advisable. While initial implementation costs may seem significant, considering several factors can lead to the conclusion that it's a sufficiently reasonable investment.

First, costs invested in personnel and infrastructure for in-house development must be considered. To build solutions at the level of Deepflow independently, not only professional developers but also high-performance hardware and cloud environments must be prepared. When considerable development periods are also considered, total cost of ownership (TCO) is substantial.

Additionally, the business impact that 1% improvement in demand forecasting accuracy can bring must be calculated. Quantitative effects such as storage cost reduction through inventory optimization, raw material supply risk mitigation, and opportunity loss minimization should be thoroughly analyzed to confirm the validity of solution introduction.

Risk Management Through Phased Approach Considering ROI

If large-scale investment still feels burdensome, considering a phased approach is also a method. Deepflow has a flexible structure that can be customized according to customer business characteristics and data environments.

First, you can start small through pilot projects. This means applying Deepflow limited to specific product groups or business units and verifying effects. Through this, return on investment can be thoroughly analyzed before deciding on enterprise-wide expansion.

The core of risk management is closely tracking and managing ROI at each stage. Optimal decisions can be made by quantitatively analyzing investment scale and business impact from prediction accuracy improvement.

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Integrated Perspective on Demand Forecasting Cost Optimization Is Key

Demand forecasting systems are no longer simple cost centers but strategic assets that determine corporate profitability and competitiveness. As seen in ImpactiveAI's Deepflow case, what's important is not how much cost is invested, but how to invest efficiently to create substantial business value.

Deepflow provides high prediction accuracy through various deep learning models, extensive external data, genetic algorithm-based optimization technology, and product-specific customized approaches. This technical excellence leads to substantial business results such as safety stock optimization, waste reduction, and stockout prevention, realizing high return on investment.

Additionally, through cloud-based services, phased approaches via pilot projects, and automated model updates, total cost of ownership (TCO) is minimized while visible effects can be seen in a short time.

Ultimately, demand forecasting cost efficiency lies not in simply lowering implementation costs, but in maximizing business value created relative to investment. Deepflow provides optimal solutions from this perspective and will be the most efficient choice for companies seeking to elevate their competitiveness through demand forecasting.

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