The Dynamic Vehicle Routing Problem (DVRP) is one of the most important problems in the area of enterprise logistics. DVRP problems involve these dynamics: the appearance of customers, travel times, service times, or vehicle availability. One of the most often considered aspects of the DVRP is the availability of customers, in which a part or all of the customers are revealed dynamically during the design or execution of the routes. A classification of the DVRP problem due to various elements causing dynamism is proposed. The aim of the paper is to distinguish dynamic VRP, which takes into account the dynamic appearance of customers to serve during the design or execution of the routes. In particular, the difference between the predictive and unexpected aspects of the customer’s availability is considered. Above all, the variant of customer’s availability which is predicted according to an appropriate general rule is modeled using the algebraic-logical meta-model (ALMM). It is a methodology which enables making collective decisions in successive process stages, not separately for individual vehicles. The algebraic-logical model of the dynamic vehicle routing problem with predicted consumer availability is proposed. The paper shows the possibilities of applying the ALMM approach to dynamic problems both with predicted and unexpected customer availability.

The paper deals with problems in the post-implementation phase of management Enterprise Resource Planning (ERP) systems. Proper management of the system maintenance stage is a basis for efficient system development in terms of business needs. Based on the research and analysis of collected materials, it turns out that making a decision to upgrade the system is equally crucial. We present revealed mechanisms determining the post-implementation approach to upgrade or reimplement the ERP system. The main aim is to determine the methodology and difference understanding to achieve success in the post-implementation stage. The paper shows that the systemic approach to the maintenance stage of the ERP system affects its further decisions: upgrade or reimplement. It has a direct impact on future maintenance costs and the scope of new business demands. This research is an outcome of industry–academia collaboration and based on several developed implementation systems, achieved upgrade and reimplementation projects. Based on case study analysis, we show that reimplementation means an evolution of the current ERP processes rather than another attempt to “reimplement” an unsuccessful system implementation. On the other hand, upgrades are not only a tool or system actualization but the easiest way to bolster company sustainability and to have the information system up to date. The issues discussed in the article will be used to develop changes in the implementation methodology of ERP systems.

Corporate budget planning involves forecasting expenses and revenues to support strategic goals, resource allocation, and supply chain coordination. Regular updates to forecasts and collaboration across organizational levels ensure adaptability to changing business conditions. Long-term sales forecasts form the foundation for budgeting, guiding resource allocation and enhancing financial efficiency. The budgeting process in organizations is complex and requires data from various operational areas, which is collected over a representative period. Key inputs include quantitative sales data, direct costs indirect costs, and historical revenues and profitability, which are often sourced from ERP systems. While ERP systems typically provide tools for basic budgeting, they lack advanced capabilities for forecasting and simulation. We proposed a solution, which includes dynamic demand forecasting based on time series methods such as Build-in method in Power BI (which is ETS—exponential smoothing), linear regression, XGBoost, ARIMA and flexible product groupings, which are simulations for cost changes. The case study concerns a manufacturing company in the mass customization industry. The solution is designed to be intuitive and easily implemented in the business.

A defect prevention is a part of manufacturing company practice. Paper proposes a formal approach for solving scheduling problems with unexpected events as extension of general frameworks for Zero-Defect Manufacturing (ZDM) strategy. ZDM aims to improve the process efficiency and the product quality while eliminating defects and minimizing process errors. However, most of ZDM applications focus on using the technological achievements of Industry 4.0 to detect and predict defects, forgetting to optimize the schedule on the production line. We propose formal method to create predictive-reactive schedule for problems with defect detection and repair. Our proposal is based on the formal Algebraic-Logical Meta-Model (ALMM). In particular, it uses the model switching method and combines defect detection, heuristics construction and decision support containing predictions of disturbances in the production process and enabling their prevention. Production defects are detected and repaired, and consequently, production delivers components without defects, and in the shortest possible time. Moreover, the collection and analysis of data related to the occurrence of disturbances in the production process helps the management board in making decisions based on analysis gathered and stored data. Thus, the proposed method includes strategies such as detection, repair, prediction and prevention for defect-free production. We illustrate the proposed method on the example of a flow-shop system with different types of product defect problem.