production logistics

The ongoing automation of production logistics through driverless transport systems (DTS) can significantly enhance the efficiency and quality of transport processes. Despite these advantages, many companies still choose manual tugger train systems for material supply. Semi-structured interviews with industry experts provide insight into the reasons behind these decisions, with particular emphasis factors that extend beyond purely economic assessment. The findings indicate that the lack of flexibility of driverless transport systems and the effort required for implementation effort are key reasons why manual transport solutions are often preferred in intralogistics.

The digital transformation of production logistics processes promises great potentials for increasing their efficiency. The processes can thus be better controlled and existing capacities better utilized. For a successful digital transformation, the quality of the information that will be collected and processed is a key factor. This paper presents a method for a systematic digital transformation of production logistics processes, with a focus on information flows and their quality.

The efficient performance of service tasks on high-value capital goods like maintenance, repair and overhaul of aircrafts and railway vehicles is influenced by a turbulent environment. In this context, excellent production logistics in maintenance shops can be a way out to cope with this turbulence. This contribution describes a bottom-up approach which is the basis for improving the logistical processes by dimensioning material flow-oriented buffer stocks.

environment poses huge challenges to production enterprises, especially with regards to logistics. The Logistic Operating Curve Theory, developed at the Institute of Production Systems and Logistics (IFA), is a recognized approach to describing logistic interactions, nevertheless, it reaches its limits when it comes to the dynamic aspects. In order to facilitate a timely and optimal Logistic Positioning a method is developed for quickly and reliably identifying dynamic processing states.

Logistic Performance has become a key factor for the success of producing companies. As shown by several surveys, it is particularly challenging for small and medium sized enterprises to reach their logistic objectives. This has various reasons. Therefore a new method is required in order to evaluate the level of maturity in production logistics and identify essential adjusting levers. On this basis it is possible to deduce consistent measures to improve the logistic performance.

This paper focuses on the application of a bee-like autonomous control method to a matrix-like shop floor model with setup times.Apparently present planning and control systems are unable to cope with the new needs for flexiblity and process reliability resulting from dynamics and complexity in the environment. Autonomous control means de-centralized coordination of intelligent logistic objects in a dynamically changing environment. By the aid of a continuous flow simulation the system’s performance will be analyzed in regard to the application effect on throughput times and inventory levels.

In production logistics of the machine and tool manufacturig industry RFID-systems are implemented more and more. Enterprises expect an improvement of efficiency in tracing containers and material provision. Implementation of RFID often fails because the processes should be adopted as they are and limits of the technology are not known sufficiently. This article shows the limits of RFID in tracing containers in production and introduces an approach of implementing RFID-supported processes.

Production logistics has seen the introduction of autonomous control strategies, which had been implemented with different modelling methods. Additionally a standardising body of tools for the evaluation of autonomous control strategies has been set up. This article presents a framework, which helps to identify the appropriate autonomous control strategy together with the corresponding modelling approach for given production logistics scenarios.

In 2020 production and logistics are faced by a changed periphery of manufacturing. New manufacturing technologies, machine concepts and groundbreaking manufacturing control will be the main fields of innovation in the range of production and logistics. Starting from a scenario of production in 2020 the following concept identifies the drivers of innovation for industrial engineering and in particular for production logistics, that reflect the increasing dynamics and complexity of the changing environment, and points out the resulting fields of innovation.

Increasing competition in product costs leads worldwide to the need of process improvement - especially in small and medium-sized enterprises. Projects of production and logistics, with the aim of optimising process organisation and manufacturing control are assumed to have the knowledge of decisive system parameters and adequate possibilities in interference. Against this background the efficient use of the resource information is very important in future. Precisely because the use of RFID in production control has even more rationalisation potentials, when the process monitoring of production process must be fast, safe and efficient real time close controlled.