Redesigning the Current Inventory Management Process for an SME

Cecilia Montt, Paloma Lillo, Luis Quezada, Astrid Oddershede, Alejandra Valencia

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Companies have different types of processes in their logistics chain. Thus, the added value delivered to each link generates a fundamental competitive tool, such as excellence and differentiation in the delivery service, coupling with technology and anticipating what customers need or expect, which means that companies must have an efficient inventory and resource management. On the other hand, companies need to procure goods and services for the development of their activities. These supplies are accumulated in the companies and must be managed for their correct handling and conservation. The problem that the SME has is that when customers buy in the store, in several cases, there is no product so sometimes the sale is lost, or they wait and return the day that the product is in the store. So in this work we study the case of an SME, companies that need inventory management to avoid losing customers. In this case, the inventory and sales process is modeled and simulated using Bizagi Modeler, where it was possible to obtain the breaks. Demand forecasting was studied with neural networks. Since the behavior of the demand is variable during the year, mainly December, February, Mother’s Day and others, which causes peaks in demand, to analyze this behavior we used the Periodic Review P Model, where we obtained the quantity to be requested to the supplier in different periods of the year, which increases sales, since 100% of the demand is covered. The results of the simulation with BIZAGE with the current and proposed situation show that in the proposed situation the loss of customers is between 0 and 0.5%. In relation to costs and profits, these were obtained with model P, although the costs increase by about 5% since a larger inventory is maintained than in the current situation, but customers will be 100% satisfied, which leads to an increase in profits of between 15 and 20%.

Original languageEnglish
Title of host publicationLecture Notes in Production Engineering
PublisherSpringer Nature
Number of pages7
StatePublished - 2023

Publication series

NameLecture Notes in Production Engineering
VolumePart F1164
ISSN (Print)2194-0525
ISSN (Electronic)2194-0533


  • BPM
  • Inventory management
  • Neural networks
  • Simulation


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