The importance of data-driven decision making in manufacturing and production is increasing day by day. However, many practitioners in the field still face difficulties in data analysis and utilisation.
Today, we will look at the problems that are often encountered in the field and the innovative solutions provided by Deepflow.
Data analysts at manufacturing sites face difficulties in the complex data processing process every day. The process of integrating and analysing production data scattered across dozens of Excel sheets requires a lot of time and effort.
In particular, the complexity of analysis increases exponentially when real-time data from multiple production lines must be monitored simultaneously while taking into account past production history and market seasonality.
This difficulty in data analysis leads to delays in decision-making. There is always a risk of missing important patterns or drawing incorrect conclusions in the process of comparing and analysing changes in production volume from the same period last year with production trends this year, and predicting future demand by reflecting market volatility.
Moreover, the weekly production plan report submitted to management requires high accuracy and clear evidence, but it is not easy to meet these requirements in the complex data analysis process.
This goes beyond simple inefficiency in work and affects the company's core management indicators. It becomes difficult to maintain an appropriate inventory level, which causes problems in optimising the production line utilisation rate and sometimes even prevents the company from responding quickly to urgent orders from customers.
This is a serious problem that can lead to increased operating costs and a decline in customer satisfaction.
Decision-makers at manufacturing sites are having a hard time verifying the reliability of the results presented by AI-based predictive systems.
Not only is it almost impossible to clearly understand and explain the process and rationale behind the results, but also the accuracy of the figures presented by the predictive model.
This is due to the fundamental limitation of AI systems, which operate like a black box and make it difficult to understand their internal logic.
This problem is a serious obstacle to the major decision-making process of a company. It is difficult to provide clear evidence for the forecast results in the management reporting process, and rational discussions based on AI forecast values are limited when coordinating opinions among different departments.
In particular, when the forecast values presented by AI are significantly different from the actual situation in a rapidly changing market environment, the analysis of the cause and identification of the responsible party become ambiguous.
This is a critical issue in terms of risk management for companies. It is difficult to judge the reliability of AI predictive models in strategic decision-making situations such as new product launches or entering new markets, and if a prediction error occurs, a quick response may be delayed, causing significant losses to the company.
As a result, many companies have paradoxically come to prefer decision-making based on intuition and experience, even though they have introduced advanced AI systems.
On-site managers at manufacturing companies experience excessive time and manpower consumption in their daily data analysis tasks.
Significant inefficiencies occur in the process of integrating data distributed across various systems such as ERP, MES, and QMS, standardising data in different formats, and converting them into meaningful insights.
Moreover, since this data processing process is done manually, there is a risk of human error.
This inefficient data analysis process is a major factor that hinders companies' agile decision-making. It takes hours just to prepare the daily report before the morning production meeting, and it is difficult to quickly analyse the cause of quality issues, which delays timely response.
In particular, there are frequent situations where the company is unable to respond in real time to the multifaceted data analysis requests made by management on the spot.
This can lead to a weakening of the company's competitiveness, which is more than just a matter of work efficiency. It is difficult to analyse the real-time linkage between inventory levels and production plans, which hinders proper inventory management, and there are also cases where business opportunities are missed due to delays in responding immediately to market changes.
In the current situation of increasing uncertainty in the global supply chain, the inefficiency of such data analysis is recognised as a serious problem that is directly linked to the survival of companies.
Deepflow has developed an innovative integrated graph system to resolve the complexity and inefficiency of such data analysis.
Key production indicators such as product shipment volume, raw material requirements, and future inventory levels can be analysed in an integrated manner on a single dashboard, and historical data from the past 24 months and forecast data from the next 12 months are naturally visualised in a single continuous flow.
Deepflow provides visualisation technology that focuses on increasing the transparency and reliability of information needed for decision-making, rather than simply showing data.
The prediction confidence interval is displayed in the form of a widening band, making it easy to understand that uncertainty increases over time. In addition, the uncertainty is further visualised as a heat map, allowing you to see at a glance when there are large fluctuations within the prediction range.
It also automatically detects data such as seasonality, trends, and outliers, and displays each pattern in a different colour and with different graph elements, making it easy to understand even complex data.
The multi-layer graph allows you to analyse data from various angles, and if there is high volatility in a specific time period or condition, it automatically highlights this and warns you of high-risk areas in real time.
Deepflow's visualization technology helps you easily understand and utilize complex data, revolutionising the decision-making process.
Deepflow has introduced innovative explainable AI technology to solve the problem of the reliability of AI prediction results.
For each prediction value derived by the system, the core variables that influenced it are automatically analysed and presented, and the contribution of each variable is quantitatively evaluated to clearly explain the basis of the prediction.
For example, if the demand for a particular product is expected to increase, the impact of each factor, such as changes in market trends, seasonal factors, and consumer sentiment, is quantified and displayed.
These forecast results are explained in a way that is immediately understandable to field practitioners. Instead of complex statistical terms or technical explanations, key insights are delivered in the language used in the field.
In addition, if the forecast value deviates significantly from the existing pattern, the cause of the anomaly or market change is automatically detected and notified, allowing for proactive response.
Unlike existing solutions that only show trends based on past data, Deepflow provides an advanced insight-providing system that focuses on future predictions.
It goes beyond simply showing current and past data, visualising future changes and the impact of key variables to intuitively understand future possibilities.
It helps decision makers make more strategic plans by specifically analysing expected profitability, cost-saving potential, and risk factors. Deepflow provides insights into future changes and is a key tool for helping companies grow strategically.
Of particular note is Deepflow's advanced productivity effect visualization feature. It intuitively visualizes the stock shortage and stock excess ratio saved by introducing AI, and the inventory cost saved by doing so.
Users can start with the entire operational data and explore in detail down to the inventory change trends of individual items or specific periods, and visually see the percentage that each element has contributed to reducing inventory costs.
It also allows you to compare the reduction rate of inventory shortages and excesses with other points in time or simulate the expected cost reduction effect for future decision-making. This visualization environment shortens decision-making time and clearly communicates the specific cost reduction effect to both practitioners and management.
Deepflow transforms data into insights linked to productivity, not complex numbers, helping companies make data-driven, optimized decisions.
In the face of the uncertainties and rapidly changing market environment facing the manufacturing industry, the strategic use of data is no longer an option but a necessity.
However, true data-based decision-making cannot be achieved simply by introducing advanced AI systems. The key is to create an environment where practitioners can naturally understand and utilise data.
The future competitiveness of the manufacturing industry depends on how effectively it utilises data. This will go beyond simple cost reduction and productivity improvement to become the core foundation for the ability to proactively respond to market changes and sustainable innovation.
In particular, in the current business environment where uncertainties in the global supply chain are increasing and customer demands are diversifying, accurate and rapid data-based decision-making is directly linked to the survival of companies.
IMPACTIVE AI is responding to these demands of the times and aims to become a partner that deeply understands the concerns of the field and provides practical solutions.
We will go beyond being a simple solution provider and become a partner that collaborates with the manufacturing industry to lead the digital transformation. By listening to the voices of the field and solving the pain points of practitioners, we will play a leading role in opening up a new future for the Korean manufacturing industry.
Data is no longer just a collection of information, but a strategic asset that determines the future of a company. IMPACTIVE AI will help all manufacturing companies turn uncertainty into opportunity by maximising the value of this data.
This is the future of the Korean manufacturing industry that we envision, and the direction of innovation that IMPACTIVE AI is aiming for.
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