AI Demand Forecasting Implementation: Is It Possible for Our Company Too?

As artificial intelligence (AI) technology rapidly advances, companies' business methods are also changing significantly. Particularly in the demand forecasting field, AI's influence is prominently emerging. AI-based demand forecasting has now become an essential tool for companies to accurately identify future market demand, increase operational efficiency, and gain competitive advantages.

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AI-Based Demand Forecasting: New Opportunities for Small and Medium Enterprises

Why Is AI Demand Forecasting Important?

AI-based demand forecasting can significantly increase return on investment (ROI) through inventory management optimization, stockout prevention, and planning time reduction.

In rapidly changing markets, accurate prediction has become difficult with only past data or simple statistics. AI improves prediction accuracy by comprehensively analyzing complex factors like seasonal characteristics, price elasticity, competitor promotions, and market trends. It helps companies respond proactively by discovering subtle demand patterns that humans find difficult to identify.

Why Is AI Implementation Difficult for Small and Medium Enterprises?

While 78.4% of domestic companies agree on the necessity of AI implementation, only 30.6% actually utilize it. Small and medium enterprises' AI adoption rate is 5.3%, half the level of large companies (9.2%).

Major barriers include lack of AI understanding, infrastructure and budget shortages, and specialist shortage. Particularly, the lack of professional personnel who can properly utilize AI is considered the biggest obstacle. Many executives approach AI with vague expectations or unrealistic goals, often leading to failure.

How Does This Checklist Help?

This checklist provides practical guidance for small and medium enterprises to successfully implement AI-based demand forecasting systems. Beyond vague expectations or cost burdens, it focuses on creating real business value and minimizing risks by considering actual situations and available resources.

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Strategic Preparation for AI Demand Forecasting Implementation

Why Is Clear Goal Setting Important?

The first step in AI implementation is clearly defining the business problems you want to solve and setting specific goals. Approaching with vague expectations like "AI implementation will somehow improve things" increases the likelihood of project failure.

When selecting AI consulting partners, you should verify whether they deeply understand your company's business status and goals, analyze specific problems in current processes, and present measurable improvement effects achievable through AI implementation.

Companies must clarify whether they aim for cost reduction through AI (repetitive task automation, labor cost reduction) or revenue increase (customer experience innovation, personalized product recommendations).

ROI Analysis and Effect Measurement

AI implementation requires setting specific key performance indicators (KPIs) and pre-verifying effects through proof of concept (PoC). Actual cases show specific effects like 40% improvement in PR task productivity, 28% increase in campaign ROI, and 35% increase in lead generation through AI implementation.

Success cases show that 'pilot projects' are the most effective strategy for overcoming small and medium enterprises' core obstacles of 'cost burden' and 'uncertainty.'

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Data Infrastructure and Management System Construction for AI Demand Forecasting Implementation

How Do You Secure High-Quality Data?

AI model accuracy and performance are heavily dependent on the quality and quantity of training data. Data quality for demand forecasting must satisfy several key criteria.

Above all, data must accurately reflect reality and be complete. AI models trained on incomplete or distorted data can produce incorrect prediction results, significantly distorting business decision-making. Additionally, data must maintain consistency across various data sources, ensure timeliness, and be actually relevant in business contexts. Duplicate data must be removed as it distorts analysis.

For successful AI demand forecasting, not only sales records, market trends, inventory information, and promotion data but also external data like weather, economic indicators, and social media trends must be broadly utilized. Integrating these diverse data sources can dramatically improve prediction accuracy.

Cloud vs. On-Premises: How to Choose?

AI infrastructure selection should be determined based on company scale, budget, data sensitivity, and internal IT capabilities.

Choosing cloud methods can save enormous initial costs for physical server construction. The pay-as-you-use structure is particularly advantageous for budget-limited small and medium enterprises. Moreover, IT resources can be quickly expanded or reduced according to business growth, enabling agile responses to changing market situations. Internet-only access to systems worldwide is also a major advantage in modern business environments.

However, cloud has clear limitations. Storing sensitive corporate data on third-party servers can pose security and privacy risks. Additionally, business operations can be directly affected by cloud service provider stability.

Conversely, on-premises methods allow companies to completely control data, making them far safer security-wise. Particularly for companies handling highly sensitive data, on-premises may be more suitable. However, it has disadvantages of significant initial investment costs and limited scalability.

Realistically, hybrid approaches that store sensitive core data on-premises while utilizing cloud scalability for computing-intensive AI workloads may be the most reasonable choice for small and medium enterprises. This approach can achieve both security and cost efficiency.

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Personnel Capability Enhancement and Organizational Culture Change Management for AI Demand Forecasting Stabilization

How Do You Secure AI Specialists?

Major professional personnel required in AI demand forecasting fields are as follows:

Data scientists must have programming abilities in Python, SQL, etc., based on statistical and mathematical background knowledge. Technical capabilities alone are insufficient; communication abilities to convert complex data analysis results into business insights and clearly convey them to management and field personnel are essential.

AI engineers must be proficient in programming languages like Python and Java and have deep understanding of machine learning and deep learning technologies. Particularly, practical abilities to build systems that operate stably in actual business environments using latest AI frameworks are core.

AI business strategists are talents with both technical understanding and business insights. The ability to identify how AI technology can contribute to achieving company strategic goals and create specific implementation plans is important.

Systematic approaches are needed to solve such specialist shortage problems. The most effective method is for leaders to first learn AI utilization capabilities and conduct practical education throughout the organization based on this. If internal capabilities are insufficient, programs like LG Aimers should be actively utilized to enhance employees' AI literacy. Simultaneously, when recruiting external AI specialists, differentiated compensation systems and environments for researching latest technologies should be provided beyond simply offering high salaries to attract and retain excellent talent.

How Do You Create an AI-Friendly Organizational Culture?

AI implementation requires fundamental changes in organizational culture and work methods. Successful change management requires identifying and appropriately responding to risks that may arise during this process.

The biggest risk is increased employee anxiety. If fears that AI might threaten jobs spread throughout the organization, resistance prevails over innovation. Additionally, over-dependence tendencies on AI can actually deteriorate employees' creative thinking and innovation capabilities. Risks of biased data-trained AI producing unfair results should not be overlooked.

To overcome these risks, management must first present clear and consistent visions for AI implementation. Rather than simply "implementing AI," specific pictures of "how our organization will develop through AI" must be drawn. Transparent communication to employees that the real purpose of AI implementation is focusing on more meaningful work through efficiency improvement is necessary.

Emphasizing collaborative relationships between humans and AI is particularly important. AI is a tool that reduces employee workload by automating repetitive and time-consuming tasks. This enables humans to focus more on high-value-added tasks like creative strategy formulation, complex problem-solving, and emotional communication with customers. Additionally, strong cultures for continuous learning and skill improvement should be built to help employees accept change as growth opportunities rather than fear them.

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AI Demand Forecasting System Construction and Operation

How Do You Select AI Models and Solutions?

AI demand forecasting models embed expert systems that automatically analyze each item's level, seasonality, and trends, applying time series forecasting technology to provide accurate predictions.

For successful AI model development, excellent prediction capabilities are essential. Beyond simply analyzing past data, inference capabilities to accurately foresee future situations are core. Since prediction accuracy is critically dependent on training data quality, securing high-quality data is most important. Additionally, the most suitable algorithms among various options like linear regression, decision trees, and neural networks must be selected according to company data characteristics and prediction targets. Above all, models must have explainability so business decision-makers can clearly understand AI's prediction bases.

When selecting solution providers, only technical capabilities shouldn't be evaluated. Most important is the ability to accurately identify company business status and core problems and present measurable improvement effects achievable through AI implementation. Additionally, solutions that can flexibly respond to various business situations and provide intuitive interfaces easily usable by field personnel should be provided.

Most importantly, performance should be pre-verified through proof of concept (PoC) in actual environments. No matter how theoretically excellent a solution is, it's meaningless if it doesn't function properly in your company's actual data and work environment. Long-term solutions that can protect data and model ownership should be selected to avoid risks of dependence on specific suppliers.

How Do You Integrate and Deploy Systems?

How new demand forecasting models smoothly connect with existing systems (CRM, ERP, SCM) determines operational efficiency. Real-time data processing and API integration functions are essential conditions for modern planning systems.

Phased approaches starting with small pilot projects to reduce risks and gradually expanding through success cases are recommended.

Continuous Monitoring and Advancement

Since AI model performance varies according to environmental changes, regular retraining, monitoring, and performance tuning are essential. Functions and accuracy must be continuously improved by reflecting employee and customer feedback.

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Risk Management and Legal Considerations for AI Demand Forecasting Application

Major AI Risks and Response Measures

The most serious risk when implementing AI-based demand forecasting systems is data-related. When tampered, compromised, or biased datasets are input into AI systems, inaccurate prediction results are inevitable. Making important business decisions based on such incorrect information can cause enormous losses to companies.

Ethical issues are also risks that cannot be overlooked. AI trained on data biased toward specific genders, races, or ages can make biased judgments. This can cause not only corporate reputation damage but also legal problems.

Additionally, many companies having excessive expectations for AI is also a risk factor. Unrealistic expectations that AI will solve all problems are likely to lead to project failures and in severe cases, development may be discontinued.

To effectively manage these risks, systematic data governance frameworks must first be established. Comprehensive systems including data quality management, systematic data management processes, and thorough data protection and regulatory compliance are needed. Ethical AI practices should be introduced to continuously monitor and mitigate prediction model bias, and preventive mechanisms should be prepared to prevent unfair or discriminatory results.

The most realistic approach is starting with low-risk pilot projects. This allows efficient management of budget and human resources while pre-verifying whether AI can actually provide value to your company.

Legal Review of Contracts and Intellectual Property Rights

AI solution implementation contracts include more complex legal considerations than general software development contracts. Clearly organizing 'copyright' and 'patent rights' ownership targets and setting AI output delivery methods are important.

Due to AI system development uncertainty, 'quasi-mandate contract' types not accompanied by 'work completion obligations' or 'contract breach responsibilities' may be recommended. Development output ownership and newly generated intellectual property rights ownership must be clearly specified in contracts.

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AI-Based Demand Forecasting: Essential Strategy for Sustainable Growth

AI-based demand forecasting system implementation is an essential strategy for survival and growth for small and medium enterprises. Successful AI implementation requires comprehensive change management for overall business processes and culture beyond simply purchasing technology.

Core Checklist Summary

Strategic Preparation

• Clear business goal setting and specific problem definition for AI resolution
• ROI analysis and key performance indicator setting

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Data Infrastructure and Management

• High-quality data securing and systematic refinement
• Optimal AI infrastructure selection suited to company characteristics

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Personnel and Organizational Culture

• AI specialist securing or systematic education program operation
• AI-friendly organizational culture creation and change management investment

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System Construction and Operation

• AI model and solution selection suitable for business problem solving
• Smooth integration with existing systems and continuous monitoring

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Risk Management and Legal Considerations

• Major risk identification and mitigation strategy establishment
• Legal matter review including intellectual property rights and responsibility attribution

Recommendations for Successful AI Implementation

- Phased Approach: Choose realistic and effective methods of creating small success cases through pilot projects and gradually expanding.

- Data-Centric Thinking: The core of AI competition is 'data.' Platform construction that can secure high-quality data and continuously manage it is the foundation of AI success.

- Human-AI Synergy: AI is a tool that automates repetitive tasks to help humans focus on creative strategy formulation and complex problem-solving.

- Strategic Investment in Change Management: Clear vision presentation by management, transparent communication, and AI acceptance enhancement through employee education are keys to successful change management.

- Professional Partnership Utilization: Expert help with deep knowledge of AI business is essential for reducing trial and error and increasing success possibilities.

Through such multidimensional preparation and strategic approaches, small and medium enterprises can successfully enter the 'AI transformation' era where they create new business models through AI, respond agilely to market changes, and ultimately secure sustainable competitive advantages.

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