Forecasting the demand for transport services on the example of a selected logistic operator

Abstract

The number of shipments is growing every year, and as a result, new transport companies arise. The increase in competition requires from entrepreneurs to apply solutions increasing the level of services provided in order to best satisfy the needs of the customers. In this aspect, minimizing the time of deliveries is extremely important, and it can be achieved, for example, by implementing the cross-docking method. It consists in consolidation of cargo from different shipment locations that is delivered in the same direction. The main feature of the above method is to keep the labor intensity of operations and the interference in the cargo to the minimum. The purpose of this article is to present a research on a logistic operator working based on a cross-docking warehouse with a capacity significantly lower than the average daily quantity of shipments handled. This requires both effective management of the available space and minimizing the time spent on manipulation activities. Therefore, it is important to know the expected number of parcels that are planned to be received and shipped on a given day in order to coordinate the work in the warehouse. It is possible to estimate it by using mathematical methods of forecasting. One of them - the multiple regression - is presented in this article. The calculations were made on the basis of collected empirical observations concerning orders for pallet spaces placed by customers. Such a forecast allows for improvement of the processes of planning and management of the possessed resources. It allows to adjust the number of warehouse workers or vehicles necessary for internal transport to the expected needs. Ultimately, it may translate into more efficient functioning not only of the surveyed branch, but also of the whole network.

Quality of Forecasts as the Factor Determining the Coordination of Logistics Processes by Logistic Operator

“…In the literature, forecasting and 3PL are usually related to supporting transport [78] or specialization [79]. In the opinion of the authors, there is a reason and a possibility to take a fresh look at this problem and confront it with the concept of a distribution network.…”

“…The networks with the highest accuracy of forecasts created using the developed forecasting tool and the networks with the best rating in terms of product characteristics are DN.5; DN.2; DN.4; DN.1 and DN.3. Papers often take into account only the best-fit forecast according to the analysis of its algorithm fitting only to the learning part [78,80]. In this paper, the authors propose a calculation of forecast verifiability based on real-time data collected from WMS.…”

Logistics Coordination Based on Inventory Management and Transportation Planning by Third-Party Logistics (3PL)

“…In this way, a 3PL entity is able, for example, to support the transportation planning and inventory management for a manufacturer or even for the whole DN. There is also a possibility to support the current actions of 3PL in the area of transportation issue forecasting [39] by providing more extended information and overlook on the other nodes in the network. In the presented case, the 3PL is able to provide the plans for transportation in EUR pallet quantity, providing managerial information about the load for particular destination points.…”

“…On the other hand, now, we are dealing more and more often with a one-day-delivery standard in transport operations [39]. As mentioned by some authors, through appropriate transport planning, 3PL could reduce flow times and reduce inventory levels [17] by increasing the speed of reaction and eliminating the need to maintain high-safety stocks.…”

Untitled

“…Demand forecasting for some authors is considered as one of the major area of machine learning application (Hirt et al, 2020), which is one of the most important trends in nowadays science. Forecasting skills of logistics operator was considered in the literature a skills which could support logistics operator in inventory management in spare parts (Ayiomamitou, 2016;Dombi et al, 2018), in prediction of transportation service demand (Grzelak et al, 2019), in forecasting dedicated to own fleet optimization (van Aarde, 2017) and in forecasting supporting last mile deliveries (Sirikulvadhana et al, 2019). However, forecasting proposed by author, assumes complex solution conneted with demand for products forecasting for whole distribution network.…”