In the home appliance market, cost reduction and operational efficiency are paramount. In this context, analytical capability is essential for making quick, data-driven decisions that ensure competitiveness. During the S&OE process, we faced the challenge of extracting information about forecasted demand over the short and medium term using the company’s ERP (SAP).
This demand data was cross-referenced with crucial production planning information, such as shifts, bottlenecks, and stock by product family. Unfortunately, this task was manually performed using Excel, resulting in a fragmented view across multiple spreadsheets, causing performance issues due to the volume of data.
The main challenges identified were: the excessive manual labor, the lack of an integrated analytical view of the process, the analysis limited to product families rather than SKU level, and a poor understanding of key operational bottlenecks. Consequently, it was necessary to devise an analytical solution that reduced manual labor by automating the data collection process.
Moreover, it was crucial to create a more comprehensive analytical view that included not just product families but also individual SKUs and centralized the analysis of bottlenecks, stock, and demand, thus improving operational modulation.
Activities were conducted to map innovation processes and utilize appropriate methodologies and rituals. A key component was the use of a Data Lake House, already in place at Nidec-GA, which enabled faster and more integrated access to and analysis of information.
The CRISP-DM methodology guided the project development, establishing a structured and interactive process divided into six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Each phase involved specific activities such as identifying business needs, collecting and preparing data, creating and validating models, and implementing solutions.
Additionally, rituals for monitoring and reviewing the project were established, including regular evaluation and adjustment meetings. These rituals allowed for ongoing tracking of progress, identification of challenges, and strategic decision-making to ensure the success of the initiative.
In summary, the conception and execution of the idea involved mapping innovation processes, adopting the CRISP-DM methodology to guide development, and utilizing the Data Lake House to centralize and integrate data. The established rituals enabled continuous monitoring of the project, ensuring its effectiveness and maximizing the outcomes achieved.
One of the main challenges for this type of project is the lack of technical expertise and adequate resources to handle Data Lake structures and data analysis, as they require advanced knowledge in areas such as data science, engineering, and data storytelling. In this regard, having a strategic partner, in this case, Dojo Smart Ways, became crucial.
In addition to the inherent challenges of data projects, such as understanding the data, its quality, and integration, one of the obstacles encountered was the initial resistance to new approaches from business users. The lack of literacy, meaning the ability to understand and interpret data, can cause business users to resist or reject the implementation of data projects. To overcome this challenge, Dojo Smart Ways implemented a Data Literacy program, training business users so they could understand and use data efficiently. The goal was to provide them with the necessary skills and knowledge to interpret and make data-driven decisions, as well as demonstrate the added value these projects can bring to their areas.
Finally, this initiative was essential in creating an organizational culture that values and promotes the importance of data as a strategic asset. This involves sharing success stories, highlighting the benefits achieved through data analysis, and recognizing the contributions of business users in this process.
Initially, the project automated the entire data consolidation process across different areas of the S&OE cycle, focusing not just on product families but also on individual SKUs, centralizing the analysis of bottlenecks, inventory, and demand, and improving operational modulation. This automated process enhanced operational efficiency by 40%, with increased analytical capabilities at a granularity level that allows crossing of SKUs with bottlenecks and inventory using intelligent routines.
Moreover, the project enabled better communication between Planning and Production areas; greater overall visibility of compressor inventory information, crossing at the level of common components among different SKUs; balancing of the supply and demand Pareto; expansion of the planning horizon; and improved management of flow constraints. This new analytical approach provided a roadmap for other AI initiatives that could be implemented for strategic gains in efficiency and effectiveness.
Finally, in addition to achieving remarkable operational efficiency, this project also led to a significant reduction in inventory, thanks to our enhanced analysis of the S&OE process.
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