Operational Planning Tool Research Articles

Improving the planning and accounting of material costs for the repair and maintenance needs of an industrial enterprise

The optimal use of material resources is an essential factor in ensuring the profitability of the business of an enterprise in the real sector of the economy. An essential part of the system of material resources of an industrial enterprise is the material costs of repair and maintenance needs. The purpose of the research presented in this article is to structure the information space with a high level of transparency and improve the quality of tools for operational planning and accounting of these expenses of an industrial enterprise at all stages of production activity; The tasks include the development of a model for improving operational planning of the need for these resources for repairs, maintenance and operation of production equipment in the context of production orders and operational accounting, including the preparation of management reports on the implementation of planned plans. The research used methods of systematic and situationally oriented analysis; collection, accumulation and systematization of information about economic activity; economic analysis; classification; aggregation; reduction; generalization; graphical visualization of data; comparison; process approach.

Height-diameter models for King Boris fir (Abies borisii regis Mattf.) and Scots pine (Pinus sylvestris L.) in Olympus and Pieria Mountains, Greece

In forest science and practice, the total tree height is one of the basic morphometric attributes at the tree level and it has been closely linked with important stand attributes. In the current research, sixteen nonlinear functions for height prediction were tested in terms of their fitting ability against samples of Abies borisii regis and Pinus sylvestris trees from mountainous forests in central Greece. The fitting procedure was based on generalized nonlinear weighted regression. At the final stage, a five-quantile nonlinear height-diameter model was developed for both species through a quantile regression approach, to estimate the entire conditional distribution of tree height, enabling the evaluation of the diameter impact at various quantiles and providing a comprehensive understanding of the proposed relationship across the distribution. The results clearly showed that employing the diameter as the sole independent variable, the 3-parameter Hossfeld function and the 2-parameter Näslund function managed to explain approximately 84.0% and 81.7% of the total height variance in the case of King Boris fir and Scots pine species, respectively. Furthermore, the models exhibited low levels of error in both cases (2.310m for the fir and 3.004m for the pine), yielding unbiased predictions for both fir (−0.002m) and pine (−0.004m). Notably, all the required assumptions for homogeneity and normality of the associated residuals were achieved through the weighting procedure, while the quantile regression approach provided additional insights into the height-diameter allometry of the specific species. The proposed models can turn into valuable tools for operational forest management planning, particularly for wood production and conservation of mountainous forest ecosystems.

Development of a health monitoring framework: Application to a supercritical pulverized coal-fired boiler

This work details the development of a physics-based equipment health monitoring framework for a supercritical boiler, using first-principles models to estimate the remaining useful life (RUL) of its components. The framework accounts for fatigue and creep life consumption, generating spatio-temporal variations in mechanical and thermal stress. Analysis of the stress profile throughout the boiler highlights the finishing superheater inlet steam header as a vulnerable location susceptible to damage from cycling operation. The framework also yields quantified uncertainty in the RUL projection for specific locations, accounting for uncertainties in material properties and boiler operation. Results indicate that operational uncertainties (e.g., seasonal variation and operational strategy) and material properties (e.g., rupture time coefficients, Young's modulus, yield strength, and coefficient of thermal expansion) significantly impact the RUL of the finishing superheater inlet steam header. Additionally, case studies demonstrate the use of the health monitoring framework as a predictive tool for operational planning under uncertainty, including scenarios with and without updates on the operation of the boiler.

Theoretical principles of budgeting as a method of economic management of enterprise’s activity

The paper is devoted to the study of the theoretical foundations of budgeting as a method of economic management of the enterprise. It has been proven that in modern conditions, budgeting is aimed at increasing the efficiency of its activities in the current period and in the future. There are considered modern approaches to the interpretation of the budget and the budgeting process at the enterprise. The analysis of various literary sources regarding the definition of the concept of "budgeting" was carried out. Various approaches to revealing the economic essence of the concept of budgeting have been identified. It was found that disagreements related to the interpretation of the term budgeting system by scientists lead to differences in the identification of its functions. The following approaches to the interpretation of the essence of the budgeting process are distinguished: budgeting as a tool for planning the enterprise's activities; budgeting as a tool for operational and tactical planning; budgeting as a management method; budgeting as a management technology. Also, there are considered the economic essence of budgeting as a method of economic management of the enterprise's activities, aimed at forming a system of budgets for all areas of its activity. The task of budgeting is systematized, the elements of the budget system infrastructure at the enterprise are presented. A sequence of stages in the formation of the budget system aimed at optimizing the economic management of the enterprise is proposed. A sequence of stages in the formation of the budget system aimed at optimizing the economic management of the enterprise is proposed. On the basis of the conducted research, a modern conceptual approach to the definition of the concept of "budgeting system" was formed.

A Reverse Logistics Network Model for Handling E-commerce Returns

E-commerce supply chains are becoming more complex due to increasing global sales, and product returns from these sales are alarmingly high, highlighting the importance of effective return management. This paper proposes a reverse logistics network model to optimize return management. The proposed model applies ward-like hierarchical clustering with geographical constraints to detect return tendencies and utilizes mixed integer linear programming to optimize the network. The decision variables of the model include selection of Initial Collection Centers (ICCs), allocation of customer markets to ICCs, and optimal return volumes to be sent to each fulfillment center and recycling center from ICCs. The validity of the proposed model is established through a case study conducted in the consumer electrical and electronics sector of an e-commerce firm, providing 39.9% cost savings on average compared to the current Reverse Logistics (RL) network operation. This study contributes to the literature by integrating industry 4.0 technologies into the assessment of RL and facility planning with network optimization. The proposed RL network model serves as an operational planning tool, providing directions to e-commerce firms on optimizing RL networks and utilizing partner networks with integrated decision making for product returns.

Patterns of Urban Compactness: Indicators of Balance between Built-Up Area and Voids

An effective response to the current international and EU guidelines of limiting land take and desirable reversal of the phenomenon, is possible for Italy only if it succeeds in setting up centralized regional direction structures for settlement transformations and working on operational planning tools. Precisely at this level, a different control of classic urban planning parameters will have to be expressed, centering attention on the relationship between built-up areas and property areas (coverage ratio) and orienting it to support urban design from a soil-saving perspective. This should apply in all homogeneous zones covered in Italian planning, with a decisive revision of current models. The paper examines the possibilities of technical action in this direction by simulating land-saving solutions corresponding to the progressive reduction of areas committed to construction and formulating proposals to strategically better regulate urbanization arrangements. The method used is based on the implementation of a set of indicators from different institutional databases. These relate the size of the average land area used to locate the individual buildings to the residential loads of the buildings, showing in both aspects an extraordinarily low average density that causes extensive land take phenomena.

A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

Enhancing the operational resilience of the distribution system network (DSN) proactively in a hurricane-prone region requires a pre-hurricane event DSN optimization model, built on accurate hurricane-induced DSN line fault prediction scenarios. In the past, the resilience evaluation methods such as statistical sequential and non-sequential Monte Carlo simulation (MCS) contingency-based technique, and Machine learning-based Bayesian Networks (BN) technique, have been proposed to strengthen the operational resilience of the DSN proactively against the forecasted oncoming hurricane events. However, a comparative study is largely unexplored to evaluate which of these two methods is best for proactive operational planning decision-making against the forecasted oncoming hurricane event. In this paper, the Bayesian network (BN) and combined statistical DSN’s Fragility-curve(FC)-Monte Carlo simulation (MCS)-Scenario reduction (SCENRED) predictive algorithms were developed. The DSN line fault prediction scenarios simulated leveraging the predicted oncoming hurricane Ewiniar data were utilized to perform pre-hurricane DSN optimization to proactively decrease the DSN expected load loss. The pre-event system optimization problems were formulated in a mixed integer linear programming (MILP) approach and solved using a CPLEX solver in general algebraic modelling system (GAMS) on a redesigned 48-bus DSN. The simulated initial expected load loss of 39% of 35 MWh was decreased to 35.34%, and then to 30.71% with the use of combined statistical DSN’s FC-MCS-SCENRED, and the BN-DSN predictive models. These results were validated using the Electrical transient analyzer program (ETAP).This study confirmed that the BN-DSN predictive model is a better operational planning tool compared to combined statistical DSN’s line FC-MCS-SCENRED predictive model.

Berth planning and real-time disruption recovery: a simulation study for a tidal port

With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year’s operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.

Transit Ridership Modeling at the Bus Stop Level: Comparison of Approaches Focusing on Count and Spatially Dependent Data

Boarding and alighting modeling at the bus stop level is an important tool for operational planning of public transport systems, in addition to contributing to transit-oriented development. The interest variables, in this case, present two particularities that strongly influence the performance of proposed estimates: they demonstrate spatial dependence and are count data. Moreover, in most cases, these data are not easy to collect. Thus, the present study proposes a comparison of approaches for transit ridership modeling at the bus stop level, applying linear, Poisson, Geographically Weighted and Geographically Weighted Poisson (GWPR) regressions, as well as Universal Kriging (UK), to the boarding and alighting data along a bus line in the city of São Paulo, Brazil. The results from goodness-of-fit measures confirmed the assumption that adding asymmetry and spatial autocorrelation, isolated and together, to the transportation demand modeling, contributes to a gradual improvement in the estimates, highlighting the GWPR and UK spatial estimation techniques. Moreover, the spatially varying relationships between the variables of interest (boardings and alightings) and their predictors (land use and transport system features around the bus stops), shown in the present study, may support land use policies toward transit-oriented development. In addition, by using an approach with little information, the good results achieved proved that satisfactory boarding and alighting modeling can be done in regions where there is a lack of travel demand data, as in the case of emerging countries.

1033-P: An Interactive Capacity Planning Dashboard for Algorithm-Enabled Telemedicine-Based Diabetes Care

We previously showed that the use of a tool for remote analysis of continuous glucose monitor data, Timely Interventions for Diabetes Excellence (TIDE) , was associated with improved glucose management for patients with type 1 diabetes. To facilitate the deployment of TIDE at two partner institutions, we developed an operational planning tool. We designed an interactive dashboard to match capacity and demand with a variety of operational, population, and workforce parameters. To improve the dashboard’s accessibility, simplicity, and interpretability, we performed iterative design based on feedback from users and two partner institutions. We set parameter values based on the first deployment of TIDE and conducted sensitivity analyses of the capacity-demand match. The dashboard (Figure 1) contains seven modifiable parameters; generates a table illustrating the match between capacity and demand; displays a timeseries plot of capacity and demand; and calculates three alternative capacity plans that satisfy annual patient demand. It is available online (www.bit.ly/surf-tide) . Small absolute reductions in per-patient review time produced the most significant changes in clinic capacity. We developed an interactive dashboard that facilitates data-driven operational planning for clinics seeking to deploy algorithm-enabled telemedicine-based diabetes care. Figure 1. Capacity Planning Dashboard Disclosure A.Chang: None. M.Z.Gao: None. J.Ferstad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. P.Prahalad: None. R.Johari: None. D.Scheinker: None. Funding NIH R18DK122422

Computational Approaches for Grocery Home Delivery Services

The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so that the delivery process can be completed without interrupting the cold chain. Therefore, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the attended home delivery (AHD) problem in the scientific literature. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, the underlying optimization problem is a vehicle routing problem with a time windows. This work is concerned with a case given by an international grocery retailer’s online shopping service. We present an analysis of several efficient solution methods that can be employed to AHD services. A framework for the operational planning tools required to tackle the order placement process is provided. However, the basic framework can easily be adapted to be used for many similar vehicle routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing local search operations and mixed-integer linear programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches.

SPOWTT: Improving the safety and productivity of offshore wind technician transit

AbstractThis paper describes the SPOWTT project. The intention of this project was to understand how sailing by crew transfer vessel (CTVs) to offshore wind farms affects the mental and physical wellbeing of individuals on board. The focus was on quantifying this impact, understanding the key drivers, with an aim to ensuring personnel can arrive to the wind turbines in a fit state to work safely and effectively. Impacts looked at subjective state beyond simply vomiting. Key results include the ability now to predict vessel motions from given Metocean conditions and vessel designs. We also discovered that the impact of vessel motions on seasickness is different for different symptoms and is driven not only by vertical z‐axis accelerations but also by certain frequencies of motion in the y‐axis. Frequencies other than 0.16 Hz were found to be impactful, and x‐axis movements appeared to have a longer‐lasting effect on the day's work. Through the formulation of a new, evidence‐based understanding of seasickness, we have created an operational planning tool, designed to have a direct benefit on the safety and productivity of offshore wind farm operations.

The cost of health workforce gaps and inequitable distribution in the Ghana Health Service: an analysis towards evidence-based health workforce planning and management

BackgroundDespite tremendous health workforce efforts which have resulted in increases in the density of physicians, nurses and midwives from 1.07 per 1000 population in 2005 to 2.65 per 1000 population in 2017, Ghana continues to face shortages of health workforce alongside inefficient distribution. The Ministry of Health and its agencies in Ghana used the Workload Indicators of Staffing Needs (WISN) approach to develop staffing norms and standards for all health facilities, which is being used as an operational planning tool for equitable health workforce distribution. Using the nationally agreed staffing norms and standards, the aim of this paper is to quantify the inequitable distribution of health workforce and the associated cost implications. It also reports on how the findings are being used to shape health workforce policy, planning and management.MethodsWe conducted a health workforce gap analysis for all health facilities of the Ghana Health Service in 2018 in which we compared a nationally agreed evidence-based staffing standard with the prevailing staffing situation to identify need-based gaps and inequitable distribution. The cost of the prevailing staffing levels was also compared with the stipulated standard, and the staffing cost related to inequitable distribution was estimated.ResultsIt was found that the Ghana Health Service needed 105,440 health workers to meet its minimum staffing requirements as at May 2018 vis-à-vis its prevailing staff at post of 61,756 thereby leaving unfilled vacancies of 47,758 (a vacancy rate of 41%) albeit significant variations across geographical regions, levels of service and occupational groups. Of note, the crude equity index showed that in aggregate, the best-staffed region was 2.17 times better off than the worst-staffed region. The estimated cost (comprising basic salaries, market premium and other allowances paid from central government) of meeting the minimum staffing requirements was estimated to be GH¢2,358,346,472 (US$521,758,069) while the current cost of staff at post was GH¢1,424,331,400 (US$315,117,566.37), resulting in a net budgetary deficit of 57% (~ US$295.4 million) to meet the minimum requirement of staffing for primary and secondary health services. Whilst the prevailing staffing expenditure was generally below the required levels, an average of 28% (range 14–50%) across the levels of primary and secondary healthcare was spent on staff deemed to have been inequitably distributed, thus providing scope for rationalisation. We estimate that the net budgetary deficit of meeting the minimum staffing requirement could be drastically reduced by some 30% just by redistributing the inequitably distributed staff.Policy implicationsEfficiency gains could be made by redistributing the 14,142 staff deemed to be inequitably distributed, thereby narrowing the existing staffing gaps by 30% to 33,616, which could, in turn, be filled by leveraging synergistic strategy of task-sharing and/or new recruitments. The results of the analysis provided insights that have shaped and continue to influence important policy decisions in health workforce planning and management in the Ghana Health Service.

Development of an Operational Planning Tool for Geothermal Plants With Heat and Power Production

Abstract A short-term operational planning tool for geothermal plants with heat and power production connected to large district heating systems is developed. The software tool contains, among other features, a heat demand forecasting model for district heating systems. Two options, such as linear regression and artificial neural networks, are compared. As the result shows, artificial neural networks with the Bayesian Regularization Backpropagation Algorithm have a high generalization capability and are suitable to forecast the heat demand of large district heating systems with high accuracy. Data from a district heating system with about 70-MW load supplied by a geothermal plant in the south of Munich (Germany) are used for comparison and assessment of all methods. After developing a suitable heat forecast, the heat and power production site is modeled by using mixed-integer linear programming. Mixed-integer linear programming has proven to be a suitable method to model the operation of geothermal plants with heat and power production as well as to solve the planning optimization problem. As the results show, the short-term operational planning tool can optimize the operation of single components as well as of the overall geothermal plant with regard to various objective functions. The tool maximizes the revenues from the sold heat and electricity minus the costs for the boiler fuel and the heat purchased from a connected adjacent geothermal plant. A retro perspective operation investigation has proven that the profitability of the considered geothermal plant could be significantly increased by using the developed software.

Operation Planning Tool Improves Quality, Efficiency of Offshore Risk Management

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 195750, “Improving the Quality and Efficiency of Operational Planning and Risk Management With Machine Learning and Natural Language Processing,” by Claire Emma Birnie, SPE, Jennifer Sampson, and Eivind Sjaastad, Equinor, et al., prepared for the 209 SPE Offshore Europe Conference and Exhibition, Aberdeen, 3-6 September. The paper has not been peer reviewed. The complete paper discusses Equinor’s operation planning tool (OPT), developed to present planners with the technical conditions of a platform, identify potentially dangerous combinations of concurrent activities, and propose learnings from 8 years of incident recordings. The OPT provides a single interface detailing a plant’s technical conditions, all planned work orders, and relevant lessons from previous incidents. By reducing reliance on personal experience, the tool has improved risk identification and handling, achieved faster knowledge transfer to new employees, and focused cross-platform knowledge sharing. Introduction The operator’s requirement that all incidents or near-misses be reported has resulted in a database of over 108,000 incidents. Alongside lessons learned from previous incidents, planners must consider the suitability of concurrent jobs and any technical status that may affect the safety of executing a planned job at a specific time. Before 2018, planners had to manually consult eight data sources, each with a separate user interface (UI), to obtain an overview of the plant’s technical status, concurrent jobs planned, and relevant lessons learned from previous incidents. This work flow had two clear bottlenecks: Manual investigation of the separate data sources was time-consuming, often involving keyword searches or queries on structured data. No easy way existed to obtain an overview of full asset status, with discipline leaders focusing individually on their jobs. A high volume of planned jobs and the time-consuming nature of investigating multiple data sources dictated heavy reliance on personal experience of the production platform to accelerate searching of relevant data segments. However, this posed challenges to rapid onboarding of new employees and capture of cross-platform learnings. The OPT was developed to provide a single interface displaying data from all eight data sources. Using natural language processing (NLP) techniques, the tool leverages unstructured data to supplement structured data, creating additional information that previously did not exist in a machine-readable format. Because of the technical vocabulary used to describe offshore operations, language models had to be generated to capture descriptions of equipment used and tasks being performed. This information was used to help determine relevance of an incident to a planned activity.

Sequence iterative method-based steady-state analysis of integrated electricity, gas and heating networks

The increasing linkages and interdependencies between multiple energy systems and the demand for higher overall energy efficiency of these systems put into force their combined management as multi-energy systems (MESs). This study comprised the development of a method for the steady-state analysis of the multi-energy flows of electricity, gas and heating systems, which is a fundamental tool for real operation and operational planning. A sequence iterative method was introduced for sequentially determining the energy flows of each system. This method is a relaxation of an existing uniform Newton’s formulation for handling the negative effect on convergence due to the hybrid quadratic and exponential 1/2 gas equations. Industry-based software for electricity simulation and pipeline programs enabling gas and heating analysis were incorporated into the method. Moreover, the mechanism of a distributed regulating unit scheme was developed for data-exchange between sub-networks under analysis. The applicability of the proposed method was demonstrated by analysis of representative small-scale and large-scale systems.

Decision Support Tool for Operational Planning of Field Operations

Precision Farming (PF) and Controlled Traffic Farming (CTF) are well known concepts within agriculture, but the adoption rate of these practices by farmers is still very low, because farmers lack the needed skills or fail to see the benefits of using these practices. If farmers want to reap the full benefits, operational planning must be carried out in advance for the entire crop cycle, before the crop season begins. However, operational planning across the entire crop cycle is a non-trivial task, since the efficiency of each operation is determined by a range of selected operational features (e.g., wayline direction, operational speed, vehicle capacity, wayline sequence, and turn type). To that end, we present, in this paper, an application that can support farmers with operational planning of field operations with CTF, by automating the process. It provides the farmer with an overview of all his field operations, and acts as a decision support tool during the operational planning process. The application allows farmers to store and manage field and equipment information, which is used as input, when setting up CTF and generating way lines and route plans for the individual fields. One of the key benefits of the application is the provided comparison feature, where farmers can compare alternative solutions, based on Key Performance Indicators (KPIs). Results from an example field, for operations with different machine setups, are presented to illustrate how KPIs and visualisations can support farmers during the decision process.

A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods

Paper aims Propose a continuous decision support system, a Digital Twin, integrating two widely used techniques, Discrete Event Simulation and forecasting methods. Originality With the evolution of the industry, there is a growing need for increasingly agile and assertive decision support systems. Also, familiar tools and techniques tend to change over time to suit such a scenario, supporting new researches on their use in the modern industry. Research method The proposed method allows the use of simulation, with the aid of forecasting methods, for continuous decision making, composing the so-called Digital Twin. The method was applied in a real process to validate it. Main findings The Moving Average, Single Exponential Smoothing, and Double Exponential Smoothing forecasting methods were used to supply the simulation model in order to test scenarios and guide decision making. The developed system enabled a virtual copy with a certain degree of intelligence and that provides answers to make the constant decision-making process more efficient. Implications for theory and practice The proposed method can be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the shop floor and at managerial levels.

A Generic Framework for Analytical Probabilistic Assessment of Frequency Stability in Modern Power System Operational Planning

In view of modern power system characterized by significant inertia reduction and booming uncertainty, this letter proposes an important operational planning tool to comprehensively analyze frequency stability including both steady-state frequency and rate of change of frequency issues in a probabilistic manner for the first time. The proposed generic framework can tackle various frequency-related uncertainties and accommodate different system frequency response models. The effectiveness and efficiency of the proposed analytical probabilistic assessment are demonstrated by comparing with numerical scenario-based simulation.

Operationalized psychodynamic diagnosis: a systematic review of the literature.

The Operationalized Psychodynamic Diagnosis (OPD-2) is an operational multiaxial diagnostic assessment and treatment planning tool. This systematic review sought to analyze empirical studies that used the OPD as an instrument. In addition to identifying the studies, we analyzed the topics covered and the results of research that used the OPD empirically. Articles, dissertations and empirical theses that mentioned or used the OPD instrument in the last five years (2012-2017) were included in this review. The strategy included searching with combinations of the descriptors "Operationalized and psychodynamic and diagnosis" from the Portal de Periódicos CAPES on the PubMed, Google Scholar, and ResearchGate databases for work published in English, Portuguese or Spanish in the last five years. The search returned a total of 189 papers, but only 20 were included. The studies selected discussed the validity and reliability of the instrument, the therapeutic process and analysis of outcomes, assessment of different mental disorders, and also included studies comparing different instruments and techniques. Considerable scientific effort has evidently been dedicated to accumulating more consistent data on psychodynamic diagnosis. It was demonstrated that the OPD is an essential clinical tool for dimensional comprehension of the subject and for scientific research. However, the number of publications on the subject is not yet significant and the methods employed are diverse. Nevertheless, there is a growing body of data on topics such as applicability in different contexts and to different pathologies, promoting greater visibility and with greater representativeness of professionals who have experience with the instrument.