Lean PCP Project: analytical intelligence for Lean PCP system

Abstract

The Production Planning and Control (PCP) process and, consequently, inventories have been facing challenges in all supply chains in recent times. Despite advances in the management system in order to assist in this process, it is evident the inefficiency, in small, medium and large corporations, related to ruptures or excesses of items in stock, caused by both internal and external factors to the organization. Proof of this was the collapse in 2021 in the supply chain of microprocessors for various segments of the industry, compromising the economic resumption of companies throughout the chain after a year of pandemic caused by COVID-19. Truckers' strike, changes in customs or government policies, climate change, changes in the behavior of competitors, wars, among others, are also examples of external factors that act as offenders to a balanced stock, that has a high turnover, low cost and that ensures above all a great delivery performance. It is known that the production chains are going through a process of digital transformation strongly sidewalk in the so-called fourth industrial revolution, a theme that has been widely highlighted in the manufacturing industry in recent years and that is guided by machine-to-machine communication and the application of enabling technologies. Among the enabling technologies the concept of Big Data including AI-based techniques are being developed and deployed around the world in a variety of applications, due to their superior ability to handle the complex relationship of data entry and information output. In the scenarios experienced two decades ago by the company proposing this project, which works with the implementation of lean thinking in its customers, one of the most successful operations management systems in the world, there are several difficulties in the effective operationalization of the pulled production system, which is one of lean's most important tools for the process of planning and control of production and inventories. Among them: Process of collection and compilation of data from various sources of information, and highly dependent on human intervention; Analysis and sizing performed in spreadsheets, with complex formulas and errors of difficult to locate, without any integration with ERP systems, and under the domain of few people (knowledge is not in the structure of the company, but in people's minds); Excessive time spent by planning teams in the process, which updates planning at time intervals away from ideal; and, consequently, low speed of response to changes in demand, production capacity, and cycle and replacement times. In this way, this project proposes a way to automate the analysis of the parameters of planning and control of the production of a PCP system through technical. (AU)