Lean PCP Project: Analytical Intelligence for PCPLean System

• Grant number: 24/07326-3
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: February 01, 2025
• End date: January 31, 2027
• Field of knowledge: Engineering - Production Engineering - Production Management
• Principal Investigator: Ricardo Renovato Nazareno
• Grantee: Ricardo Renovato Nazareno
• Company: Sistemas e-Lean Desenvolvimento de Sistemas Ltda
• CNAE: Atividades profissionais, científicas e técnicas não especificadas anteriormente
• City: São Carlos
• Associated researchers: Antonio Freitas Rentes
• Associated research grant: 22/12323-8 - Lean PCP Project: analytical intelligence for Lean PCP system, AP.PIPE
• Associated scholarship(s): 24/20347-0 - Study and Application of Advanced Data Analysis Techniques for Inventory Optimization in Various Sectors, BP.TT; 24/20346-3 - PCP Lean Platform - User Experience and front-end development, BP.TT

Abstract

The Industry 4.0 is profoundly transforming how companies operate and produce goods and services. One of the key elements of this revolution is the increasing integration of information technology with manufacturing processes, and in this scenario, data analysis plays a crucial role in production planning and control systems (PCP).The PCP process has faced considerable challenges in recent times across all supply chains. Consequently, inventory management has also been a focus in this scenario. Despite significant progress achieved with management systems aimed at improving this process, the existence of inefficiencies in companies becomes evident. Such inefficiencies often result in stockouts or excess inventory, caused by both internal factors and external elements impacting the organization.Evidence of this was the collapse in 2021 in the microprocessor supply chain for various industry segments, compromising the economic recovery of companies across the chain after a year of the COVID-19 pandemic.In the current market, there are highly effective technologies that successfully automate inventory control, encompassing functions such as receiving, movement, storage, checking, dispatch, and document issuance.These systems play an exceptional role in inventory control. However, they have significant limitations when it comes to production planning and inventory maintenance. In other words, these systems lack agile and automated processes and routines focused on planning and resizing inventory. This lack is reflected in the inability to react quickly to demand variations and other factors influencing batch and inventory size determination, whether related to purchased items (raw materials) or manufactured ones (finished product).Therefore, it becomes imperative to explore more advanced and agile solutions based on continuous data analysis and feedback to enable efficient planning and resizing of inventories. The ability to react quickly and accurately to demand fluctuations and other factors is essential to optimize production processes and logistics operations, ensuring more effective and efficient performance in organizations.Thus, Phase II of the proposed PIPE aims to develop a computational system that will automate the analysis of production planning and control parameters of a PPC system, continuing the MVP developed in Phase I. The development proposes incremental deliveries of technology until the module becomes autonomous and capable of assessing new conditions and executing real-time sizing improvements, promoting balanced inventory with high turnover, low cost, and, above all, optimal delivery performance.The addition of the Analytics module to the PCPLean solution will generate significant value for its current and future customers, as well as making E-LEAN more competitive in the market, as validated in the conducted research, and consequently will bring great positive impacts to its business. (AU)