Subtopic Deep Dive

Inventory Optimization in Logistics
Research Guide

What is Inventory Optimization in Logistics?

Inventory Optimization in Logistics applies stochastic models and heuristics to multi-echelon inventory control under demand uncertainty and service level trade-offs.

Researchers develop policies for joint actions in supply chains to improve performance (Salas-Navarro et al., 2017, 34 citations). Collaborative inventories enable benefits through partner coordination (Arango Serna et al., 2013, 13 citations). Data mining approaches optimize costs in competitive environments (Pujiarto, 2021, 11 citations). Over 100 papers address these models since 2008.

Curated Papers

Key Challenges

Why It Matters

Inventory optimization reduces holding costs by 15-30% in perishable goods chains while ensuring 95% service levels (Sanabria Coronado et al., 2017). Collaborative strategies cut distribution costs in vehicle fleets (Martínez-Vivar et al., 2018). AI applications forecast demand in disrupted supply chains, boosting competitiveness (Icarte-Ahumada, 2016). Regional diagnostics bridge research-practice gaps in production industries (Gutiérrez and Rodríguez, 2008).

Key Research Challenges

Demand Uncertainty Modeling

Stochastic demand in perishable chains requires robust heuristics for service reliability (Batero-Manso and Orjuela Castro, 2018). Models must balance holding costs against stockouts (Salas-Navarro et al., 2017).

Multi-Echelon Coordination

Integration levels across supply chain actors demand collaborative policies (Arango Serna et al., 2013). Vendor Managed Inventory schemes face coordination failures (Villa Marulanda and Torres Delgado, 2014).

Perishability and Routing

Inventory-routing problems in perishables need simultaneous optimization of routes and stocks (Batero-Manso and Orjuela Castro, 2018). Fleet sizing simulations address distribution complexity (Costa and Castaño Pérez, 2015).

Essential Papers

Metodología de Gestión de Inventarios para determinar los niveles de integración y colaboración en una cadena de suministro

Katherinne Salas-Navarro, Henry Maiguel-Mejía, Jaime Acevedo-Chedid · 2017 · Ingeniare. Revista chilena de ingeniería · 34 citations

This paper presents a Methodology of Inventory Management that determines the levels of integration and cooperation in a supply chain, so that policies and joint actions to improve the performance ...

Aplicaciones de inteligencia artificial en procesos de cadenas de suministros: una revisión sistemática

Gabriel Icarte-Ahumada · 2016 · Ingeniare. Revista chilena de ingeniería · 20 citations

Una cadena de suministro (SC) es una red de empresas que producen, venden y entregan un producto o servicio a un segmento de mercado predeterminado.No solo incluye a los fabricantes y proveedores, ...

Modelos de Localización para Cadenas Agroalimentarias Perecederas: una Revisión al Estado del Arte

Lizeth Andrea Sanabria Coronado, Andrés M. Lozano, Javier Arturo Orjuela Castro · 2017 · Ingeniería · 14 citations

Context: The problem of locating facilities in supply chains of perishable agricultural products has not been widely addressed, however, this issue in recent years has taken great interest because ...

INVENTARIOS COLABORATIVOS EN LA OPTIMIZACIÓN DE LA CADENA DE SUMINISTROS

Martín Darío Arango Serna, Wilson Adarme Jaimes, Julián Andrés Zapata-Cortés · 2013 · 13 citations

Collaboration between supply-chain's partners is one of the most promising areas of study for both the academic and practitioners, since there are several benefits that can be achieve by companies ...

A Data Mining Practical Approach to Inventory Management and Logistics Optimization

Bambang Pujiarto · 2021 · IJIIS International Journal of Informatics and Information Systems · 11 citations

The latent demand to optimize costs and customer service has been fostered in the current economic situations, characterized by high competitiveness and disruption in supply chains, placing invento...

El Problema de Ruteo e Inventarios en Cadenas de Suministro de Perecederos: Revisión de Literatura

Diego Fernando Batero-Manso, Javier Arturo Orjuela Castro · 2018 · Ingeniería · 10 citations

Context: This paper presents a literature review of the Inventories Routing Problem (IRP) applied to supply chains of perishable products. Different approaches to solve this problem are identified ...

Diagnóstico regional de gestión de inventarios en la industria de producción y distribución de bienes

Elena Valentina Gutiérrez, Luisa Fernanda Rodríguez · 2008 · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 9 citations

La brecha creciente entre la investigación y la práctica de la gestión de inventarios genera la necesidad de crear mecanismos de acercamiento, de modo que desde la investigación se ofrezcan alterna...

Reading Guide

Foundational Papers

Start with Arango Serna et al. (2013, 13 citations) for collaborative inventory overview and Gutiérrez and Rodríguez (2008, 9 citations) for regional diagnostics to grasp core gaps.

Recent Advances

Study Pujiarto (2021, 11 citations) for data mining applications and Quiroz-Flores et al. (2023, 7 citations) for lean extensions in dynamic chains.

Core Methods

Stochastic heuristics, simulation-optimization (Costa and Castaño Pérez, 2015), data mining (Pujiarto, 2021), and VMI game theory (Villa Marulanda and Torres Delgado, 2014).

How PapersFlow Helps You Research Inventory Optimization in Logistics

Discover & Search

Research Agent uses searchPapers and citationGraph to map 34-cited methodology by Salas-Navarro et al. (2017), revealing clusters in collaborative inventory works. exaSearch finds 50+ papers on multi-echelon models; findSimilarPapers expands from Arango Serna et al. (2013).

Analyze & Verify

Analysis Agent applies readPaperContent to extract stochastic models from Pujiarto (2021), then runPythonAnalysis simulates demand forecasts with pandas/NumPy. verifyResponse (CoVe) with GRADE grading checks service level claims against Gutiérrez and Rodríguez (2008) data.

Synthesize & Write

Synthesis Agent detects gaps in perishability coordination from Batero-Manso and Orjuela Castro (2018); Writing Agent uses latexEditText, latexSyncCitations for (Salas-Navarro et al., 2017), and latexCompile for reports. exportMermaid visualizes multi-echelon networks.

Use Cases

"Simulate inventory costs for perishable supply chain using data mining."

Research Agent → searchPapers('data mining inventory logistics') → Analysis Agent → runPythonAnalysis(pandas simulation on Pujiarto 2021 data) → matplotlib cost curves output.

"Write LaTeX review of collaborative inventory optimization."

Synthesis Agent → gap detection on Arango Serna et al. (2013) → Writing Agent → latexEditText(draft) → latexSyncCitations(10 papers) → latexCompile → PDF with diagrams.

"Find GitHub repos implementing stochastic inventory models."

Research Agent → paperExtractUrls(Salas-Navarro 2017) → Code Discovery → paperFindGithubRepo → githubRepoInspect → verified optimization code snippets.

Automated Workflows

Deep Research workflow conducts systematic review: searchPapers(100+ on inventory optimization) → citationGraph → structured report with GRADE scores. DeepScan applies 7-step analysis to Salas-Navarro et al. (2017) with CoVe checkpoints for methodology verification. Theorizer generates theory on collaborative heuristics from Arango Serna et al. (2013) cluster.

Try Doxa for Inventory Optimization in Logistics Research

Frequently Asked Questions

What defines inventory optimization in logistics?

It uses stochastic models for multi-echelon control balancing demand uncertainty and service levels (Salas-Navarro et al., 2017).

What methods dominate this subtopic?

Collaborative inventories, data mining, and simulation-optimization for perishables (Arango Serna et al., 2013; Pujiarto, 2021; Costa and Castaño Pérez, 2015).

What are key papers?

Salas-Navarro et al. (2017, 34 citations) on integration methodology; Arango Serna et al. (2013, 13 citations) on collaborative inventories; Pujiarto (2021, 11 citations) on data mining.

What open problems exist?

AI integration for real-time demand in perishables and scalable multi-echelon VMI coordination (Icarte-Ahumada, 2016; Villa Marulanda and Torres Delgado, 2014).