Companies considering in-house prediction model development projects are rapidly increasing. This is because AI utilization in demand forecasting has become essential rather than optional. As research results continue to show that AI-based demand forecasting accuracy is over 40% higher on average than traditional methods, many companies are joining this trend.
In this situation, companies face important decisions: Should they develop Python-based prediction models directly through their own IT teams, or should they adopt proven AI solutions like Deepflow?
Python has established itself as the most popular programming language for data analysis and machine learning model development. With its rich libraries and developer ecosystem, Python provides various tools necessary for implementing demand forecasting models and is already being utilized by many corporate IT teams. The in-house development approach has the advantage of perfectly reflecting a company's unique business logic and data characteristics.
However, the process of IT teams directly developing prediction models using Python can be more complex and lengthy than expected. Numerous challenges exist, including securing specialized personnel such as data scientists and ML engineers, extensive data preprocessing and feature engineering, model training and optimization, and continuous performance improvement and maintenance. This requires fundamental discussions about a company's core competencies and resource allocation.
On the other hand, specialized AI demand forecasting solutions like Deepflow have high-performance models already optimized by industry that can be immediately utilized. These solutions have the advantage of saving time and resources needed for in-house development while providing high-level prediction accuracy. Particularly, Deepflow's approach of providing industry-specialized AI models presents immediate solutions to specific forecasting problems companies face, unlike general prediction models built from scratch with Python.
This article will comprehensively compare two approaches - IT teams' direct Python-based prediction model development and Deepflow solution implementation - across multiple dimensions including cost, time, accuracy, scalability, and maintenance. Through this analysis, we aim to help companies make optimal choices suited to their situations, resources, and business goals, and provide insights for building more efficient and accurate demand forecasting systems.
The talent pool of data scientists, machine learning engineers, and MLOps specialists required for AI development is extremely limited in the market. For a domestic mid-sized company to recruit one skilled data scientist requires costs exceeding 100 million won annually, and retaining such talent long-term is an even more challenging task.
In startup stages, limited resources should be focused on building core business capabilities, but recruiting AI specialists creates significant financial burden. To solve these problems, some companies employ strategies of collaborating with external consultants or utilizing talent on short-term project bases, but these also show limitations in terms of knowledge transfer and project continuity.
According to research by global consulting firm McKinsey, 67% of domestic mid-sized companies that started AI-related projects failed to meet planned schedules due to talent acquisition problems, and 42% experienced project suspensions due to key personnel turnover.
The process of developing demand forecasting models with Python typically requires at least 6 months to over a year. This is because complex stages must be completed including data collection and cleansing, model development and training, testing and optimization, and system integration.
The problem is that development periods are frequently extended due to unexpected data quality issues or iterative work for model performance improvement during this process. This uncertainty acts as a significant risk factor for companies' business plans and investment recovery timing.
AI experts point out that such delays stem more from business process understanding and data quality issues rather than technical challenges. Particularly in mid-sized companies and above, unstructured data from legacy systems or data silo problems are identified as major causes of project delays.
Decision-makers must establish project management systems that separate development processes into clear milestones and strengthen verification processes at each stage to minimize these risks. Additionally, it's essential to organize data governance systems from enterprise-wide perspectives and pre-build data pipelines suitable for AI projects.
Specialized solutions like Deepflow can significantly shorten implementation periods through already optimized methodologies and proven frameworks, providing strategic advantages for companies that need to respond agilely to business environment changes.
AI model development is not a one-time project but a continuous journey. Even after development completion, continuous management including model performance monitoring, periodic retraining, and hyperparameter tuning is necessary. Particularly, model performance can deteriorate over time due to data drift phenomena, making regular updates essential.
In reality, approximately 40% of annual development costs in maintenance expenses occur continuously beyond initial AI model development costs. These maintenance costs are often underestimated during initial investment review stages and can act as serious budget pressure if not reflected in long-term TCO (Total Cost of Ownership) calculations.
AI experts point out that model performance degradation in operational environments is one of the biggest risk factors for corporate AI projects. Particularly in mid-sized companies and above, continuous model management can become more difficult due to imbalances between organizational data science capabilities and IT operational capabilities.
Decision-makers must establish operational systems and budget plans for continuous value creation rather than simple development completion for AI project success.
Specific measures include building MLOps systems, introducing automated monitoring systems, and organizing dedicated teams for model performance evaluation and retraining. Specialized solutions like Deepflow have the advantage that service providers handle most of these operational burdens, allowing companies to concentrate resources on core business activities. This can be an important consideration especially for organizations lacking AI operational experience.
Many companies face unexpected technical difficulties when expanding prediction models initially started in single departments or limited scopes to entire enterprises. Numerous challenges need resolution during expansion including smooth integration with existing IT infrastructure, connecting various data sources, securing real-time processing capabilities, and maintaining performance as users increase.
AI experts analyze that these scalability problems are more significantly affected by organizational data governance and IT architecture maturity rather than technical constraints. Particularly in mid-sized companies and above, major challenges include integration with legacy systems, minimizing latency for real-time decision support, and ensuring compatibility with enterprise-wide IT security policies.
Decision-makers need architecture design considering enterprise-wide applicability from initial design stages to solve these scalability problems. Effective strategies can include adopting microservice architecture, building API-based interfaces, and utilizing cloud-native technologies.
Specialized solutions like Deepflow already possess enterprise-grade scalability and integration capabilities, facilitating linkage with various internal systems and minimizing technical risks that may arise during expansion processes.
Even with technical capabilities to develop models with Python, deep domain knowledge in relevant fields is essential for building sophisticated prediction models reflecting industry-specific characteristics. Each industry including logistics, retail, manufacturing, and energy has unique variables, patterns, and constraints, and effectively reflecting these in models is a challenge beyond simple technical issues.
For example, in fashion retail, complex factors like trend changes, seasonal effects, and discount policies affect demand, requiring deep understanding of the industry to accurately model these factors. AI experts point out that lack of domain knowledge is a major reason why even technically excellent models fail to lead to actual business value creation.
Particularly in mid-sized companies and above, the limitations of standardized AI models become more clearly evident in complex environments combining industry-specific characteristics with unique business processes. Decision-makers must build close collaboration systems between business experts and data scientists and establish knowledge management systems that systematically document industry-specific specialized knowledge for reflection in model development to overcome these domain expertise gaps.
Specialized solutions like Deepflow are developed based on implementation experience and domain expertise across various industries, providing optimized models that already reflect unique characteristics of each industry. This can be an important competitive advantage especially in areas with limited industry-specific AI application cases.
For large corporations or major manufacturing companies with revenues over 100 billion won, in-house Python-based development is a sufficiently viable option as they possess their own IT infrastructure and specialized personnel. However, in rapidly changing market environments, the over one-year development time can represent significant opportunity costs.
Particularly in industries with high daily sales data volatility like food and retail, rapid system construction and immediate performance creation are important.
For such companies, Deepflow proposes enterprise-grade solutions for company-wide implementation. It supports perfect integration with existing ERP and SCM systems and provides infrastructure capable of large-scale data processing and real-time prediction. Particularly, the 24-hour support system from specialized technical support teams ensures stable system operation.
For mid-sized companies with annual revenues between 30-100 billion won, in-house development may not be an efficient choice in terms of cost and risk. For such companies, we propose Deepflow's phased implementation strategy. Particularly effective implementation is possible for companies conducting daily sales in food and retail sectors.
First, solution utility can be verified through a 3-month pilot program. With a 20 million won pilot program cost, the system can be experienced at over 80% reduced cost compared to annual contracts, and the entire process from data integration to model training and performance optimization can be experienced during this period. Based on pilot period performance, company-wide implementation decisions can be made, minimizing implementation risks.
For growth-stage companies with revenues below 30 billion won, in-house development for prediction system construction may be realistically difficult. However, accurate demand forecasting is essential for corporate growth and profitability improvement even at this stage. For such companies, Deepflow provides optimized packages focused on core functions.
Particularly for growth companies in food and retail sectors, we recommend starting with basic packages centered on sales volume forecasting and inventory management functions, then adding advanced features like raw material price prediction as growth occurs. Through pilot programs to verify system effectiveness followed by conversion to long-term contracts, companies can fully utilize AI-based demand forecasting benefits while reducing initial burden.
In the data-driven decision-making era, AI demand forecasting system implementation has become core to corporate competitiveness. Accurate demand forecasting has become core competency directly connected to corporate survival in rapidly changing market environments. Particularly with increasing global supply chain uncertainty and rapidly changing consumer behavior, AI-based demand forecasting system implementation is becoming essential rather than optional.
Python-based in-house development and specialized AI solution implementation each have advantages and disadvantages, requiring careful selection according to company scale and characteristics. Particularly in industries where daily-level prediction is important like food and retail, system accuracy and stability are most important.
Deepflow responds to these market demands by providing solutions that can effectively solve demand forecasting challenges companies face. Particularly, system utility can be verified through 3-month pilot programs, allowing companies to experience AI demand forecasting systems with minimal risk.
Future corporate competitiveness depends on how accurately demand can be predicted and how efficiently resources can be managed. AI demand forecasting is now becoming core driving force leading corporate digital transformation beyond simple work efficiency tools.
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