Search results

Taken very much for granted, the common or garden pallet is still one of the most versatile items of materials handling equipment. It comes in all shapes and sizes and there are countless variations on a theme: timber, metal and plastic are the most popular materials of construction, while post pallets, roll pallets, expendables, converters, collars and cages are just a few of the pallet's relations. As one of the most innovative sectors in the total handling/storage spectrum, palletisation still provides the vital link in the strength and well being of our distribution networks.

Pallets ensure efficient processes in logistics and are exchanged between the different actors, while passing through various supply chains several times. In common practice, the exchange is often not directly carried out on site, e.g. due to a lack of time, so that additional trips and new pallet purchases become necessary. To reduce these negative effects, a digital cross-actor platform is designed, and its potential is investigated.

The authors developed an agent-based simulation model with mathematical optimization. Using experience from practitioners, as well as real-world datasets which were analyzed, the authors ensure a realistic model of the pallet exchange system in Germany.

The authors demonstrated that, with the help of this platform concept, transport routes can be shortened, debts and receivables can partly be equaled out through balancing, and the quantity of pallets in the overall system can be reduced.

The results are not directly transferable to pallet exchange systems in other countries without considering their general settings.

Digital networking increases the efficiency of the existing pallet exchange system. Even small collaborations prove to be reasonable.

The authors developed new mechanisms for a digital pallet exchange platform, which takes on the role of a central planning instance, in addition to recording pallet receivables and debts. It enables the planning of the commodity flow of empty pallets, which are transported by the forwarders on regular routes, and distributed between the platform participants.

This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet.

Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a “T-shape” without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a “proof-of-concept” to test PILA’s performance.

Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm.

PILA’s performance is limited by the current depth camera’s range (< = 3 m), and this is expected to be improved by using a better depth measurement device in the future.

The results demonstrate that the pallets can be located with an accuracy of 1cm along the x, y, and z directions and affording an angular resolution of 0.4 degrees at a distance of 3m in 700ms.

Saudi Arabia's 2030 vision targets an increase of 34% in non-oil revenue participation in the GDP, thus the need for automation and digital transformation. The Company ER is a market leader producing high-quality dairy products in the Kingdom and is a pioneer in the production industry. The company has recently increased the capacity of its milk factory to meet its vision. An investment was made to automate the pallet handling procedures at the milk factory to provide increased production for daily consumption. The new automation transition in Company ER's milk factory provides a unique opportunity to utilize lean management tools to improve the current automated processes before commercialization.

OEE (overall equipment effectiveness) will monitor losses for different operational losses in the new automated system and indicate system improvements, with 85% as the target. Based on DMADV (design, measure, analyze, design and validate) methodology, this study analyzes the entire automated pallet handling system. It uses lean tools to identify areas for improvement, identify waste elements and propose solutions to achieve Company ER's OEE targets.

In this paper, the outcomes will be presented as documented solutions that address the losses encountered in the production system, showing a 12.8% increase in the system's OEE.

Owing the time and resource constraint, this study only involved automated pallet handling procedures in a milk production facility. Hence, the generalization of the result is slightly limited. More studies in several different processes and sectors are required.

This study provided a valuable tool for researchers for gaining deeper understanding regarding the lean manufacturing and its implementation. For practitioners, it is useful to evaluate the degree of lean manufacturing tools in their material handling systems.

This study is the first attempt to develop lean manufacturing constructs for evaluating the automated pallet handling procedures in a milk production facility.

The success of the pallet began with the development of the fork‐lift truck and with increased mechanisation of piece‐good transport since the Second World War. This brought with it a world‐wide revolution in distribution which has still today not yet run its full course. Specialised educational faculties, technical magazines and industrial management positions have been established for the “TOTAL DISTRIBUTION CONCEPT” discipline since the latter part of the Sixties and there is talk of enormous unexploited rationalisation reserves in this field which commences at the end of the extraction or manufacturing process and terminates either with the subsequent processor or the final consumer.

The Fourth International Biennial Conference on Warehousing and Centralised Distribution, organised by the International Association of Chain Stores (CIES), presented delegates with three days of intensive instruction on everything from increasing productivity to the role of the security officer. Held in Zurich towards the end of last year the conference attracted speakers from France, Italy, Germany, Netherlands, Switzerland, North America and Britain—Oliver Richter of GKN. His paper on the development of a national pallet pool is published below. It is followed by the paper given by W.H. Jonkers of van der Berghs who urges less emphasis on low prices and a move to greater efficiency fostered by close links between manufacturers and retailers.

The purpose of this paper is to detail the design and prototyping of a smart automation solution for de-strapping plastic bonding straps on shipping pallets, which are loaded with multiple containers secured by a top-cover as they move on a conveyor belt.

The adopted design methodology to have the system perform its function entails using the least number of sensors and actuators to arrive at an economic solution from a system design viewpoint. Two prototypes of the robotic structure are designed and built, one in a research laboratory and another in an industrial plant, to perform localized cutting and grabbing of the plastic straps, with the help of a custom-designed passive localizing structure. The proposed structure is engineered to locate the plastic straps using one degree of freedom (DOF) only. An additional strap removal mechanism is designed to collect the straps and prevent them from interfering with the conveyor.

The functionality of the system is validated by performing full-process tests on the developed prototypes in a laboratory setting and under real-life operating conditions at BMW Group facilities. Testing showed that the proposed localization system meets the specified requirements and can be generalized and adapted to other industrial processes with similar requirements.

The proposed automated system for de-strapping pallets can be deployed in assembly or manufacturing facilities that receive parts in standard shipping pallets that are used worldwide.

To the best of the authors’ knowledge, this is the first mechanically smart system that is used for the automated removal of straps from shipping pallets used in assembly facilities. The two main novelties of the proposed design are the robustness of the strap localization without the need for computer vision and a large number of DOF, and the critical placement and choice of the cutting and gripping tools to minimize the number of needed actuators.

This paper seeks to frame and model the environmental issues and impacts associated with the management of pallets throughout the entire life cycle, from materials to manufacturing, use, transportation to end‐of‐life disposal.

A linear minimum cost multi‐commodity network flow problem is developed to make pallet‐related decisions based on both environmental and economic considerations.

This paper presents a review of the environmental impacts associated with pallets by life cycle stage. The types of materials used to fabricate pallets, the methods by which they are treated for specific applications, and various pallet management models are described with respect to embodied energies, toxicity and emissions. The need for companies to understand the cost, durability, and environmental impact tradeoffs presented by pallet choices is highlighted. The paper introduces a model to assist in choosing both how pallets are managed and the material they are constructed of that balances these tradeoffs.

There is limited research on the environmental impact of different management approaches of large‐scale pallet operations. The proposed model and approach will provide companies seeking to engage in more sustainable practices in their supply chains and distribution with insights and a decision‐making tool not previously available.

This paper aims to discuss some relevant issues in the design and operation of material handling and storage systems (MH&SS) characterized by complex material flows and high‐traffic intensity. The paper seeks to provide solution examples and an analysis methodology to face large increases of materials flows through a redesign of the material handling and storage system.

At first, possible strategies to improve system performances when facing strong increments of material flows are presented and discussed. A significant case study is then analyzed in order to present a practical application of the proposed methodology. Resorting to discrete‐events simulation, the alternatives are verified, correct design choices are identified, and the resources are properly sized to develop a streamlined layout.

The paper recognises that design and upgrade of intensive material handling systems is a complex task asking for a careful study of alternatives and detailed system analysis, otherwise capacity problems and bottleneck phenomena may not be effectively solved.

This work focuses on a specific case study. The paper, therefore, will be of interest mainly to managers and designers of similar plants and large – intensive material handling systems.

The paper shows how the correct planning and analysis of design alternatives integrated with a detailed system simulation enable a drastic reduction of bottleneck phenomena, thus meeting the required capacity improvement goals when upgrading and redesigning complex and high‐volume material handling systems.

The paper, while providing insights to practitioners engaged in design and management of complex MH&SS, outlines a methodological approach which can be useful when facing major capacity improvement projects.

The contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making.

The research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings.

The provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis.

Warehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management.

The paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support.