Organizations in the agroindustry sector face shorter delivery schedules; therefore, they are seeking ways to conduct more effective and less costly product distribution. Supply chain management efforts have focused on improving the flow of both products and information. Thus, the aim of this case study was to build a graphical user interface to enable decision-making based on quantitative information for a food distribution process. The problem to be solved was associated with the development of a technological solution to reduce and control variations in transportation times, delivery costs and capacities in cold and dry food distribution. An eight-step system for a dynamics methodology was used: (1) distribution process analysis, (2) route description, (3) variable and parameter description, (4) causal loop diagram creation, (5) current model simulation, (6) validation, (7) quantitative scenario construction based on key performance indicators, and (8) graphical user interface development. The main findings of this research were that the graphical user interface and simulation showed information that represented on average 56.49% of the total distribution costs regarding fuel and that maintenance and tire wearing costs had less of an impact on total costs, representing 9.21% and 3.66% of the total costs, respectively. Additionally, the technological solution—created for the supply chain in the distribution process against the background of changes in policies—makes it possible to improve decision-making based on different scenarios supported by a graphical interface according to key performance indicators. This solution could be used by different organizations who aim to reduce logistics and transportation costs. The main implications of this research were the available and organized information and the restructuring of the distribution process.

This study started from a fig-derived product project in the 2022–2026 Strategic Plan from Sonora, Mexico, to develop technological solutions in complex environments and provide a global panorama from the industry to the prevailing situation in Valle del Mayo. A technological solution is based on a graphical interface to support decision-making in fig supply, production and distribution in the administration process, given that the main problem has been the lack of a processing plant with a sustainable approach. Four options were set up for nine producers—figs in syrup, dehydrated, marmalade, compost—based on the system dynamics methodology to solve the problem of factory installation. Six stages were followed: (1) mapping the system to determine variables and parameters; (2) constructing the causal diagram; (3) developing the flow and level diagram and model equations; (4) simulating and validating the current model; (5) designing policies and evaluating scenarios using a multi-criteria analysis; and (6) developing the graphical interface. The main conclusions show that fig-derived products and their distribution can feature in diverse markets in a graphics environment supported by complex mathematical models in the supply chain along with the capacity to generate income into utilities to support the decision of the physical factory prior to important investments.

This work considers the application of a mathematical model using mixed-integer linear programming for the vehicle routing problem. The model aims at establishing the distribution routes departing from a distribution center to each customer in order to reduce the transport cost associated with these routes. The study considers the use of a fleet of different capacities in the distribution network, which presents the special characteristic of a star network and which must meet different efficiency criteria, such as the fulfillment of each customer’s demand, the vehicle carrying capacity, work schedule, and sustainable use of resources. The intention is to find the amount of equipment suitable to satisfy the demand, thus improving the level of customer service, optimizing the use of both human and economic resources in the distribution area, and leveraging maximum vehicle capacity usage. The MILP mixed-integer linear programming mathematical model of the case study is presented, as well as the corresponding numerical study.

The proposal in the present research study is the result of a more than two-year process developed in a pallet manufacturing company for anchor enterprises in Southern Sonora, Mexico dedicated to beer production and export to the United States of America. Considering the high pallet demand for this supplier, a strategic plan was created in 2021, establishing an important project for developing technological solutions to improve decision making supported by graphical user interface and focused on sustainability. This study shows the application of system dynamics in all the wood and pallet manufacturing processes with a strategic sourcing supply chain. The method used for its development had the following stages: (1) developing the mapping process; (2) creating the causal loop diagram; (3) developing a flow and stock model with the representing mathematical equations; (4) simulating and validating current scenarios; (5) evaluating normal, optimistic, and pessimistic scenarios with multicriteria decision making using Technique to Order Preferences by Similarity and the Ideal Solution (TOPSIS) and Faire Un Choix Adéquat (FUCA); (6) building the graphical interface. The most relevant results for the company were having quantitative information regarding the pallet demand required by the main client for wood availability, which was the main restriction in the supply chain. The solution was based on four validation tests that allowed decision makers to support the production proposals considering the assistance of the dynamic models. The main conclusion demonstrated that using well-defined operation rules and policies—considering the installed capacity and pallet demand through the model solution—allows anticipating decisions on pallet quantity and reducing the risk of out-of-time deliveries.

A cold-chain meat packaging project was developed for a meat product company in northwestern Mexico that moves high volumes of fresh meat into national and international markets. The objective of the present research is to evaluate the supply process for three types of thermo-shrinkable polyethylene bags to provide a technological solution for high-volume meat packaging based on a graphical user interface. A system dynamics (SD) methodology is developed in seven stages to generate a technological solution: (1) system mapping; (2) causal diagram construction; (3) stock, flow modeling, and equations; (4) model simulation; (5) model validation; (6) scenarios and multicriteria analysis; and (7) graphical user interface development. The main result for the company was a technological solution that could communicate with decision-makers and the proposed graphical user interface. Future optimistic and pessimistic scenarios were self-evaluated based on the current situation related to three thermo-shrinkable bags used for selling high volumes of fresh meat. In these solutions, previously simulated costs and savings can be implemented in a real situation. Quantitative graphical user interface data can be observed to adequately manage box and bag inventories and minimize costs. Using SD enables the development of technological solutions in complex environments with robust simulations and models that offer data to people interested in the system under study.

The global automotive industry faces significant challenges with respect to its supply chain, particularly component scarcity and the increasing complexity of modern vehicles, which have severely impacted the production of high-tech harnesses. This study addresses the issues faced by a leading automotive harness manufacturing company in Ciudad Obregón, Mexico (an international company that requested confidentiality), which has suffered considerable economic losses (over USD 2870) and production downtime due to component scarcity and delivery delays in component deliveries, affecting “Crew Grande” harness production. This proposal aims to develop a technological solution with a graphical interface to support decision-making in the face of this scarcity. The methodology employed system dynamics to model the supply chain’s complexity, using software such as Stella^® Architect for Forrester diagrams and equations and Vensim^® PLE for causal diagrams. The model was validated with a relative error, confirming its reliability. Multicriteria decision-making (MCDM) was performed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Faire Un Choix Adequate (FUCA) methods to evaluate 15 scenarios (normal, pessimistic, and optimistic), identifying the four most favorable scenarios for optimizing operational performance. The results demonstrate these solutions’ potential to mitigate losses, improve operational efficiency, and strengthen the company’s position against market and demand fluctuations, especially for its main client, Ford Motor Company, using a graphical user interface (GUI) to support analysis and decision-making.