Browsing by Author "Huskova, Katerina"

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    Inventory Control of Products at the End of their Lifecycle Based on Nonparametric Methods
    (Technická Univerzita v Liberci, ) Huskova, Katerina; Dyntar, Jakub; Ekonomická fakulta
    In this article, we verify that the use of the past stock movement simulation with all combination search, where both control variables are fully discretized, compared to traditional parametric methods, which are often used in management of inventory with sporadic demand, brings economic savings in area of holding and ordering costs. We use sporadic demand data coming from a small size e-commerce company to compare the best achieved holding and ordering costs in continuous review fixed order quantity inventory control policy where the reorder point calculation is based on moving average and linear regression. At the same time, we examine how the results are affected by the required fill rate of service level, which we test for four levels in the interval 25 % - 95 %. The results of our experiments show that AC outperforms traditional parametric methods in achieving the best holding and ordering costs. Moreover, as the level of required service level decreases, the success of AC in achieving the best costs increases. Simultaneously, we see that the success of the simulation increases with increasing variability of demand, i.e. in the case when the differences in quantity between individual non-zero demands increase.
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    Navigating urban logistics challenges: An optimized approach to parcel distribution in the Prague city center
    (Technická Univerzita v Liberci, ) Andar, Jakub; Huskova, Katerina; Dyntar, Jakub; Ekonomická fakulta
    In this paper, we focus on metropolitan transport logistics that has always been difficult due to the need to transport goods through a complex urban infrastructure, heavy traffic, and dense populations. The goal of this paper is to describe a micro hub location in the neighborhood of the historic center of the city of Prague, the Czech Republic in a situation where the municipalities consider prohibiting the entry of trucks with combustion engines that currently provide cargo transportation for B2B partners operating in this area. Micro hubs represent the efficient last mile consolidation and distribution facilities located in or near urban neighborhoods and serving a spatially limited, densely populated delivery area. We propose a solution combining facility location problem, vehicle routing problem and balancing with the horizontal and vertical cooperation in supply chains to locate the micro hub in the area with extremely low availability of suitable space while respecting the necessity to connect this facility to an existing network of regional terminals operated by 3PLs to ensure the distribution of cargo across the board according to the requirements of customers. For the collection and distribution of parcels within the area, we suggest to use of cargo bikes and electric vans to provide environmentally sustainable service and minimize the harmful consequences of excessive traffic for residents. We also discuss the economic implications of adopting such innovative sustainable supply chain solutions for involved horizontal and vertical supply chain partners emphasizing different motivation aspects to convince these partners to cooperate and share scarce resources.
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    Towards sporadic demand stock management based on simulation with single reorder point estimation
    (2025-01-08) Huskova, Katerina; Kasparova, Petra; Dyntar, Jakub
    The goal of this paper is to decide whether bootstrapping and/or linear regression are suitable to estimate an initial reorder point in sporadic demand stock management based on past stock movement simulation (PSMS) in combination with neighborhood search-oriented optimization. Thus, we randomly generate demand data including 20–80% zero demand periods and simulate continuous review, fixed order quantity inventory control policy (R, Q) for 4 different arrangements of PSMS combined with local search (LS) with a number of bootstrapping sampling runs ranging from 5 to 500. The original idea of LS is to underestimate order lead time demand using linear regression (LR), overestimate lead time demand with the help of bootstrapping (B) and search the generated interval using PSMS to return R, Q with the optimal trade-off between inventory costs and service level. The outputs gained from simulation experiments show that avoiding the generation of overestimated reorder point with B seems to be a more sensible choice as the consumption of computational time is significantly higher than in case of LR. On the other hand, using an LR based initial reorder point may require the exploration of neighborhood in both directions, while B rather enables a more efficient one-way search as it suffers from significantly less blindness caused by PSMS compensating underestimated order lead time demand with increased replenishment orders. Furthermore, estimating just one initial reorder point brings a better opportunity to control the consumption of computational time through assigning a certain amount of computational time to every change of the initial reorder point, as a time to evaluate a single R, Q combination via PSMS is relatively stable.