Supporting Equipment Allocation for Multiple Projects in ERP Systems—Functionality Extension in IFS Applications ^†

Abstract

Many organizations execute multiple projects simultaneously, competing for limited resources, including specialized and expensive equipment. Managing such multi-project environments requires advanced planning and decision-making. An additional difficulty is taking into account the possibility and profitability of using internal and external resources. The construction industry is a particularly demanding example of this scenario, where simultaneously executed projects must share high-value equipment with limited availability. Project management planning with resource allocation is supported by various types of IT tools. ERP (enterprise resource planning) systems are particularly useful in this regard, as they use the organization’s transaction data directly, but only offer basic project support. Therefore, it is necessary to extend their functionality in order to fulfill the expected functional requirements of business users, with particular emphasis on the provision of a consistent, graphically supported interface. This article proposes an algorithm to support decision-making on equipment allocation in a multi-project environment, taking into account the use of own and third-party equipment. A case study is presented demonstrating the practical implementation of the proposed solution in the IFS Applications ERP system. The developed extension supports users through graphical and numerical presentation of machine workloads across multiple projects.

1. Introduction

In many industries, organizations carry out multiple projects simultaneously, competing for limited and often specialized resources. This parallel execution of projects requires effective planning, communication, and coordination to avoid conflicts, ensure timely delivery, and stay within budget [1]. Efficient management of shared equipment and personnel becomes especially critical when projects are interdependent and rely on the same pool of resources. The construction industry is a prominent example of such a multi-project environment, where companies frequently operate under high pressure to optimize the use of expensive and scarce machinery [2]. A further challenge is evaluating not only the availability but also the cost-effectiveness of using both in-house and third-party resources. This adds complexity to the planning process and highlights the need for advanced decision support mechanisms. To remain competitive and reduce operational costs in the face of rapidly changing market conditions, companies in the design and construction sector are increasingly seeking ways to enhance decision-making and streamline resource planning. Managing multiple concurrent projects involves not only scheduling tasks but also allocating resources across initiatives in a way that maximizes efficiency.

Currently, this requires IT tools to support project management, particularly in conjunction with IT systems containing operational data and supporting the management of core business processes. An additional difficulty to consider in project planning is the possibility and cost-effectiveness of using the organization’s resources and resources from outside the organization.

New developments in information technology (IT) offer organizations managing complex projects several tools and methods to facilitate their daily work. Enterprise resource planning (ERP) systems are among the solutions that have significantly improved management in such organizations [3].

ERP systems are developing dynamically, adapting to new technologies and changing business needs. Key trends include migration to the cloud, integration with AI and IoT, process automation, and increasing personalization and mobility of systems [4]. The main task of ERP systems is to optimize business processes within a company. ERP tools provide access to functionalities and solutions that support project management activities at many stages of the project lifecycle. However, ERP systems do not have all the solutions required by the design and construction industry [5]. A particularly incomplete and niche solution is a tool that supports multi-project equipment allocation management [4].

The issue of resource allocation in a multi-project environment (RCMPSP—Resource-Constrained Multi-Project Scheduling Problem) has been extensively studied both in theoretical and practical contexts. In particular, the optimal assignment of equipment and human resources—whose availability and cost vary over time and between projects—has become a critical research focus. Advanced models have been proposed that consider varying skill levels of workers, dynamic budgets, and multi-objective optimization (e.g., minimizing both project completion times and total costs) [6]. Growing interest in this area has also led to the development of new benchmark datasets that more accurately reflect real-world project scenarios and pose greater challenges for modern scheduling algorithms [7]. Systematic literature reviews indicate that the dominant solution approaches to the RCMPSP are approximate methods, especially genetic algorithms and priority rules, while exact methods such as mixed-integer programming are used much less frequently [8,9]. Recent contributions include hybrid metaheuristics incorporating resource buffering strategies [10], as well as distributed approaches in which autonomous project agents make local scheduling decisions in the presence of incomplete information and competition for shared resources [11,12]. Another emerging trend involves integrating project scheduling with supply chain logistics and cash flow optimization [13]. In recent years, the RCMPSP in the construction industry has attracted increasing attention. For example, in [14] the authors proposed a multi-objective MMRCMPSP model that incorporates economic, environmental, and social factors under uncertainty and solved it using evolutionary algorithms. Focusing on financial aspects, the paper [15] proposes a multi-project scheduling model that minimizes negative cash flows while maximizing profit in construction enterprises. From another perspective, in the paper [16] the authors integrated multi-project scheduling with supplier selection and transportation routing, demonstrating the benefits of supply chain integration in reducing total costs and improving customer satisfaction. These studies confirm that the RCMPSP in construction is a multifaceted problem, encompassing sustainability, financial management, and supply chain integration, which highlights the relevance and necessity of further research in this domain. All of these studies emphasize the complexity and multidimensional nature of the RCMPSP, highlighting the need for dedicated tools that support decision-makers, especially in the context of enterprise resource planning systems. Moreover dedicated tools for multi-project management do not implement complex algorithms. They only provide some simple algorithms, such as the critical path and resource leveling algorithm (e.g., [17]). These tools are additional to the core business of the organization, and they do not directly use the collected transaction data. Therefore, they often require complex integration with the organization’s core IT systems. In this case, it seems natural to use the data collected in ERP systems.

This further justifies the development of the custom decision support solution presented in this paper, implemented within the IFS Applications ERP environment.

The aim of this paper is to analyze and design a solution to the problem of supporting resource and cost allocation for multi-projects in the context of ERP systems. The presented work is related to the implementation of the IFS Application system, one of the top ERP systems [18]. This study focuses on verifying the solutions available in this area in the IFS Application system and proposing solutions that enable the effective use of hardware resources of an organization implementing multi-projects, in particular in the design and construction industry. The main contribution of this article is to propose an algorithm to support decision-making on equipment allocation in a multi-project, taking into account the use of own and rental equipment, and to develop the implementation of this algorithm in the IFS Application environment.

This paper is organized as follows. In Section 2, we present an overview of project support functionalities in selected leading ERP systems. In Section 3, we describe the problem of allocating shared resources by parallel projects in an organization, particularly in construction projects. In Section 4, we propose an algorithm to support decision-making on equipment allocation in a multi-project, taking into account the use of own and rented equipment. A case study is conducted using the IFS Application environment to demonstrate the practical application of the proposed approach. The last section contains conclusions and suggestions for future work.

Research Methodology

This study is based on the principles of Design Science Research (DSR) [19], which is commonly used in information system research when developing practical, IT-based solutions. The research process was structured as follows:

• Problem Identification—Based on consultations with industry representatives and operational inefficiencies observed in real-world construction and engineering projects, the problem of equipment allocation in multi-project environments using ERP systems was identified.
• Design and Implementation—An algorithm was developed to support resource allocation decisions within the IFS Applications ERP system. The algorithm was iteratively refined in collaboration with business experts from the construction industry.
• Demonstration and Evaluation—The decision support functionality was implemented in a real ERP environment used by a construction company. The solution was piloted on multiple ongoing projects, involving project managers and resource planners. User feedback was carried out as interviews to evaluate the usability and usage data were collected to assess the utility of the solution.

The adopted approach resembles action research, as the development was carried out in close cooperation with practitioners in an organizational context, directly addressing their practical needs.

2. ERP Systems and IT Tools for Project Management

Project-oriented organizations, particularly in engineering, manufacturing, and service industries, require integrated tools that enable the planning, monitoring, and optimization of multiple concurrent projects. IT tools for project management are software that supports project planning, monitoring, and control, in particular, including functionalities for managing tasks, schedules, resources, team communication, and project documentation. Depending on the purpose and type of project, the following applications and systems can be used: for planning and scheduling, Microsoft Project [20], Promavera [21], Jira [22], monday. com, and Asana; for progress tracking and task management, Trello [23] and ClickUp; for communication and collaboration, Slack [24], Microsoft Teams, and Bitrix24; and for documentation management, Confluence [25] and Google Docs. However, these are additional tools for the core business of the organization, and they do not directly use the collected transaction data. Thus, they often require complex integration with the organization’s core IT systems. Traditional project management software often proves insufficient due to its lack of integration with other key business processes such as procurement, production, logistics, and human resource management. In such cases, ERP systems offer a substantial advantage by embedding project and resource management functionalities directly into the operational core of the enterprise.

ERP (enterprise resource planning) systems have evolved significantly beyond their original purpose of integrating financial and operational data. Today, they serve as central platforms that support strategic decision-making, real-time collaboration, and increasingly complex demands related to project and resource management.

Below is a selection of leading ERP systems, identified through market analyses and industry reports [26], that include built-in functionalities for project management, with particular emphasis on resource planning and scheduling.

Microsoft Dynamics offers three different solutions for project planning: Universal Resource Scheduling (URS), Resource Scheduling Optimization, and Copilot for Dynamics 365. The Universal Resource Scheduling (URS) solution enables organizations to assign resources to jobs and tasks based on availability, skills, promised time, business units, geographical locations, and more. It supports the resource planning process from the Projects, Service, and Customer Service modules. The URS solution provides a planning board (in the form of a Gantt chart or overview list) where demand is analyzed and resources are manually assigned to tasks and work. The solution only supports single planning [27].

Resource Scheduling Optimization automatically plans multiple tasks simultaneously, considering task requirements and the unique attributes of resources such as people, equipment, and facilities. Among others, it helps solve the well-known traveling salesman problem, considering additional parameters such as skills, territory, and promised time windows. But it does not consider the cost-effectiveness of using resources from outside the organization [27].

Microsoft Dynamics Copilot includes AI solutions developed for selected Dynamics system modules. For example, artificial intelligence automatically completes basic data in reported service requests, pre-creates responses to customer queries, and automatically creates descriptions of newly launched products, financial analyses, and supply chain analyses.

SAP S/4 HANA ERP has a built-in module for production/resource planning and optimization (APO PP/DS). It enables advanced planning based on ready-made heuristics, as well as changes and modifications to standard algorithms for product supply planning and other planning tasks, detailed scheduling, orders for repetitive manufacturing, taking into account resource capacity in all periods, or optimizing the sequence of configurable products, taking into account resource constraints and linear networks. The SAP S/4 HANA system also provides support for the resource planning process. The resource planner can use the capacity planning tool (CS capacity planning). It presents the data in the form of Gantt charts. Making changes automatically reschedules tasks according to the changes made. The tool allows work to be assigned directly to requirements and supports planning, including periodic reviews. The solution also enables schedule monitoring, real-time schedule tracking, proactive problem-solving, and performance optimization through data-driven analysis [28,29,30,31].

IFS Application 10 has a built-in project management module that, in addition to the basic functionalities required for project management, also enables effective resource management. It supports resource planning, resource forecasting, and analysis. It allows resources to be assigned to project roles and tasks, ensuring that assigned resources have the required skills, competencies, and preferences. The functionality also provides management of multi-discipline resources such as equipment, materials, and facilities that are required to complete the project. Users can also track and control resource inventory and maintenance. It also helps users standardize resource master data across the enterprise and find and reserve the right resources when needed. The solution only allows users to assign resources to projects manually [32].

Table 1. Comparison of ERP systems in terms of project, multi-project, and decision-making support.

ERP System	Project Support	Multi-Project Support	Decision-Making
Microsoft Dynamics 365	Yes, assignment of resources to tasks and projects (URS manual, RSO automatic)	Insufficient for full multi-project support in the analyzed process	Automatic task and resource planning in RSO, AI supporting processes in Copilot
SAP S/4 HANA	Yes, detailed planning and scheduling of resources in production projects as well as other projects	Insufficient for full multi-project support in the analyzed process	Advanced scheduling algorithms; optimization of production sequence and resource allocation; AI supporting processes in SAP AI Core/SAP AI Foundation
IFS Applications	Yes, assignment of roles and tasks to resources in projects	Insufficient for full multi-project support in the analyzed process	Advanced scheduling algorithms for task and resource scheduling in PSO, AI support processes in IFS.IA

presents a comparison of ERP systems in terms of project, multi-project, and decision-making support. It highlights the areas in which scheduling algorithms and AI tools are implemented. At present, solutions optimizing resource allocation across multi-projects are still lacking.

The research conducted on the above systems shows that none of the analyzed ERP systems has a tool in its standard configuration that supports the allocation of resources to tasks in a multi-project environment, taking into account the possibility and cost-effectiveness of using the organization’s own and rented resources. In particular, there are no such solutions in the IFS Application system. Therefore, it is reasonable to develop a solution that supports resource management in project-oriented production in an ERP system. The development of such a solution required, on the one hand, the development of appropriate methods and algorithms to support the solution of the problem and, on the other hand, the proposal of modifications to the ERP system.

3. Problem of Allocating Shared Resources in Parallel Construction Projects

In this paper, we consider a real problem that occurs in project planning in the construction industry, which uses heavy construction equipment.

The company performs concurrent construction projects spread over different geographic locations. Heavy construction machinery is required to carry out activities on the projects, and it must be effectively assigned to tasks holistically across all projects. The duration of the project and its individual activities are defined, which means that each machine must be assigned to a specific project for a specific time slot, with the possibility of reallocating equipment to maximize overall utilization. The project company has a certain number of machines, which can be deployed between projects or rented externally. A key aspect is that the depreciation of in-house equipment and the cost of transporting heavy equipment between locations can exceed the cost of renting heavy equipment on site, further complicating the issue. With machinery-related costs accounting for a significant portion of project costs, the company aims to optimize equipment management as much as possible.

Heavy equipment can move between projects without returning to base, and the assignment schedule can be dynamically modified depending on the current situation.

The goal is to assign heavy equipment to an entire project (multi-project) so as to minimize the cost of implementing all projects. The objective function is calculated as the sum of the task costs. The cost of a single task for in-house equipment is the sum of machine depreciation, maintenance costs, consumable costs, and transportation costs, which depend on the type of equipment and the distance. The cost of a single task for rented equipment corresponds to the value of the provided offer. Companies have a rigidly defined amount of equipment to use, which should be optimally utilized. The cost of transporting equipment between projects or the base and project can exceed the cost of hiring external heavy equipment at the site of the work. This problem is particularly important because a large part of project costs are machinery-related. Because the problem is so important, design firms are putting considerable effort into optimizing the process. The design of the solution should facilitate the decision-making process of assigning heavy equipment to a reported demand with a fixed time window and a known geographic location. The solution should provide the expert handling the request with quick information on equipment deployment and availability and should propose equipment assignments. Heavy equipment is machines working in parallel, not needing to return to base, and able to move between projects with known locations. Machines can change the time slot on a given project if the analysis shows the profitability of shifting to another project with information about the profitability of renting equipment for the remaining working time on the project of the shifted machine.

The problem is particularly significant because a large part of construction costs are related to machinery. As a result, design firms are putting considerable effort into effective equipment planning and scheduling. Heavy equipment is a category of machines used to perform construction tasks, such as earthmoving, lifting, and transportation of materials. In multi-project environments, effective use of machinery is crucial due to limited availability and high operating costs. Scheduling the use of construction equipment becomes more challenging when multiple projects share the same resources. Machines may be reassigned between sites, potentially altering time windows for specific tasks. Decisions regarding equipment deployment directly impact both timelines and costs. In this context, optimization models have been developed to support planners in making informed decisions, taking into account constraints such as resource availability, transportation time, and project priorities.

4. Resource Allocation Algorithm and Its Implementation in the IFS Application System

Based on the analysis of the IFS Applications system, it can be said that there is no tool available in the system standard in the project area to support the management of equipment in project activities and communication between the project teams responsible for indicating equipment needs in the project and handling demands on the procurement side. The system only allows assigning a selected piece of equipment directly to a specific activity. The process involves selecting equipment from a list and specifying the expected number of hours it will be used. The user does not have any tools to support them in deciding which equipment to select. The existing functionality is shown in Figure 1, where in Section 1 there is an option to manually assign equipment to tasks of a single project.

The IFS system also does not provide any tools to support collaboration between the project manager and the person responsible for handling demands. This type of solution is insufficient for companies with multiple projects running simultaneously.

Keeping in mind the needs of IFS users and the aforementioned shortcomings, a system expansion was designed and implemented to increase the efficiency of project implementation. The solution presented below was preceded by numerous consultations with business representatives in order to best match the newly proposed solution to the real needs of users.

The analysis identified the necessary master data to support the reported demand for equipment required for the project task. These data include

• Demand number;
• Project number;
• Subproject number;
• Activity id;
• Geographical location of action;
• Resource type;
• Quantity of equipment required;
• Demand start date;
• Demand end date;
• Amount of work to be performed;
• Unit of work measurement;
• Does the work include weekends (yes/no).

In addition, a full needs analysis of the implementation of the process was conducted. As part of it, the following functional requirements were identified, which the newly designed solution must take into account:

• Graphical presentation of demands;
• Quick access to resources suitable for the request and automatic filtering of equipment;
• Graphical presentation of resource availability;
• Feedback on the assigned equipment visible to the requester;
• Pre-estimation of the costs associated with the equipment;
• Support for the equipment rental process by both requesting and operating departments.

As a result of the analysis, a complete decision tree was defined to fully handle the reported demand and implement all the functional requirements set for the process. The proposed decision-making algorithm is comprehensively illustrated in Figure 2, where the constituent blocks of the algorithm are clearly numbered.

The process begins with the entry of the demand for the execution of the project activity (Block 1.1). The data is entered by project managers using a specially created screen called “Equipment Requisition.” The newly designed solution is shown in Figure 3. This screen allows information to be entered on the demand for equipment—this information is provided by the department responsible for the implementation of the project. The submitted demand is then handled by the department responsible for the execution of requests.

The screen is divided into two sections. The first one (Section 1) is responsible for entering the demand and allows all the required information necessary for its operation to be entered, such as

• Geographical location of action;
• Resource type;
• Quantity of equipment required;
• Demand start date;
• Demand end date;
• Amount of work to be performed;
• Unit of work measurement.

The entered demand has a status of “Planned.” After completing the required data in the first section, the system automatically calculates the preliminary cost of the demand based on the estimated price of proprietary equipment multiplied by the number of days specified in the request. This calculation is performed based on the prediction and allocation of proprietary equipment.

The second section (Section 2) displays the machines and resources assigned by the department handling the request. It includes information about the assigned equipment, the assignment date, and additional comments provided by the responsible department.

The screen was initially designed in a previous study [33]. As part of the current work, it has been expanded with new features to analyze the equipment assigned by the department handling the requests, presented in Section 2.

An entered demand with the status “Planned” is not included in the analysis process. It can be freely edited, canceled, or deleted (block 1.1.1, block 1.1.2). The transformation of a demand into a ready-to-execute request is performed by changing its status to “Activated” (Block 1.2). This functionality also allows correcting activated orders (block 1.2.2) and cancellation of the demand (block 1.2.1). In case of cancellation, the status of the demand is changed to “Canceled” (block 3.1).

Activated requests are then visible on a screen that aggregates requests from across the company (Figure 4). This screen allows the demand handling department to quickly and graphically analyze the submitted demands. The process of handling a demand begins with its analysis. If the analyzed request is deemed unreasonable or impossible to handle due to lack of required data (block 1.3), a button is selected: Reject by base. This function is only available for demands that do not have custom equipment assigned. When the button is selected, the window is closed and the status of the requisition is changed to “Reject by base” (block 3.2). The screen was designed during previous research on the problem. The colors on the screen correspond to the following meanings [33]:

• Red—Demand without allocation, requiring further action.
• Green—Demand fully served, requiring no further work.
• Orange—Demand with partial allocation of equipment, requiring additional actions, such as renting equipment or contacting the person making the demand.
• White—Demand not fully served, but currently requiring no action.

On the screen, for the selected data, there is a function that allows the user to open a window for performing a detailed demand analysis. Access to this functionality is shown in Figure 5.

Thanks to the data contained in the triggered window, the user can make decisions about relocating equipment or allocating proprietary equipment (block 2.1). The screen also provides data for deciding whether it is more cost-effective to lease external equipment (block 2.2). The design of the context screen is shown in Figure 6.

The screen consists of five sections.

Section 1—Contains input data on the demand to be served, including

• Resource type;
• Quantity of equipment required;
• Demand start date;
• Demand end date;
• Amount of work to be performed;
• Unit of work measurement.

Section 2—Presents assigned resources, both those currently assigned and those assigned during previous demand handling.

Section 3—Shows the available hardware resources, filtered by the system according to the compatible hardware type. In addition, the data is presented graphically, illustrating the degree of occupancy of each resource:

• Green—Resource available for the reported demand.
• Yellow—Resource partially available; has other allocations on demand date.
• Red—Resource fully allocated within the demand deadline.

Section 4—Presents other existing reservations of the selected equipment indicated in Section 3.

Section 5—Contains function buttons for operating the process.

The process begins with an analysis of the profitability of renting equipment for the demand (block 2.2). The analysis is carried out by a person reviewing the reported demand based on expert knowledge. If renting is found to be more cost-effective, a decision is made to lease external equipment despite the possibility of servicing with in-house equipment (block 2.2.). If in-house equipment is available and a decision is made to use it, the resource is selected from Section 3 and added to the demand using the function shown in Figure 7.

When the assigned resources fully cover the demand, the user selects the function.

• Reserved—This function is available only after full assignment of in-house equipment. When the button is selected, the window is closed and the assigned resources are transferred to the equipment requisition format. The status of the demand is changed to “Reserved” (Block 3.3).
  If the selected resources do not fully cover the demand, and depending on whether the rental of the remaining equipment will be managed by the request handling department (block 2.4), the estimated rental cost must be provided. The window is closed using one of the following buttons:
• Partially reserved lease base—When this button is selected, the window is closed and the status of the request is updated accordingly. Rental handling is carried out by the department responsible for servicing the demand, after obtaining approval from the requesting department (block 2.4.1).
• Partially reserved lease project—When this button is selected, the window closes and the status of the request is updated accordingly. The rental process is then handled directly by the requesting team (block 2.4.2).
  If no proprietary resources are selected, depending on who will be responsible for renting the equipment, the window should by closed using one of the following buttons (block 2.3).
• Lease required base—After selecting this button, the window is closed and the status of the request is updated accordingly. The rental service is handled by the department processing the demand (block 2.3.1).
• Lease required project—After selecting this button, the window is closed and the status of the request is updated accordingly. The rental service is handled by the requesting department (block 2.3.2).

In the case of a partial reservation or rental request, the demand continues through the handling process (block 2.4.1.1 2.4.2.1 2.3.1.1).

For the statuses Lease Rejected (block 2.3.1.3 2.4.2.3) and Partially Reserved Lease Rejected (block 2.4.1.3 2.4.2.3), the demand handling process is completed and the status of the demand should be changed to “Closed” (block 3.7 3.9 3.11).

For the statuses Partially Reserved Lease Accepted (block 2.4.2.2) and Lease Accepted (block 2.4.2.2), the responsible person from the request handling team creates a purchase requisition by selecting one or more request lines and activating the contextual function “Create purchase requisition”. This function is shown in Figure 8.

Further handling of the demand follows the standard process path. The demand can be directly transformed into a customer order or converted into an order inquiry and then, after approval, into a purchase order (block 2.4.2.4).

Once the related purchase order is created, the status of the requisition is automatically set to Purchase pending.

When the related order is fully completed, the status of the requisition is automatically changed to “Closed” (Block 3.8). For statuses Partially reserved lease project and Lease required project, the person responsible on the project side creates a purchase requisition. The requisition is created by selecting one or more lines and calling the context function “Create purchase requisition.” This function is shown in Figure 9.

Further handling of the demand follows the standard path. The requisition can be directly transformed into a customer order, or into an “Order Inquiry”, and subsequently into a purchase order (block 2.4.1.4).

Once the associated purchase order is created, the status of the requisition is automatically set to “Purchase in progress.”

When the related order is fully completed, the status of the requisition is automatically changed to “Closed” (block 3.6 3.10).

It should be emphasized that, in the designed solution, the entire process of equipment allocation and thus cost optimization rests on the person responsible for demand analysis and resource assignment. The provided tool supports this process by

• Pre-filtering machines types available for assignment;
• Graphic representation of equipment availability;
• Dynamic presentation of other demands related to the analyzed resources.

In addition, the system allows equipment rental requests to be handled by both the requesting department and the operating department. Thanks to the extended functionality, which enables the creation of purchase requisitions linked directly to a specific equipment requirement, the requester has real-time visibility into the status of the order.

The integration of both departments activated within a single module enables efficient information exchange, translating into faster and more effective decision-making.

The designed solution has significantly increased the functionality of the IFS system in terms of equipment fleet management. With the implementation of this new functionality, companies in the design and construction industry can manage their resources more efficiently, leading to real cost optimization—a key aspect in a highly competitive market.

5. Discussion

The findings confirm the assumption that ERP systems, although powerful, require functional enhancements to effectively support industry-specific needs—particularly when it comes to complex discrete optimization problems. The proposed algorithm complements existing ERP functionality by enabling a structured, semi-automated approach to equipment allocation in a multi-project environment.

The solution was tailored to the IFS Applications system due to the context of the project implementation. However, it can be adapted to other ERP systems as well. Future research will focus on integrating machine learning models to suggest optimal allocations based on historical patterns and contextual data.

6. Conclusions

This paper presents a proposed solution for a real problem that occurs in multi-project planning in the construction industry, which uses heavy construction equipment. For effective project task planning, ERP information systems that contain transaction data and knowledge about the company should be used, although most of them only have basic functionalities in their project planning modules. This article shows how missing functionalities can be supplemented to enable and facilitate the work of planners. In particular, we propose an algorithm to support decision-making on equipment allocation in a multi-project, taking into account the use of own and rented equipment. Moreover, we present the proposed implementation of this algorithm in the IFS Application environment.

The problem of resource allocation in task scheduling in multi-projects considered in this paper is an extension of the classic RCPSP (Resource-Constrained Project Scheduling Problem), which belongs to the class of NP-Hard optimization problems. Our future work will be related to applying heuristic methods to optimize resource allocation to provide even stronger support to planners in their decision-making. Implementing such a solution in the IFS Application should facilitate work and minimize multi-project costs.