An Empirical Study About the Effectiveness of Lean Empowerment in Warehouses

Lean Management is well established in production environments. Some empirical evidences are available which suggest that in production systems lean management achieves positive results. For warehousing, some works have already been done, which deal with the application and adaption of lean tools for usage in warehousing. In order to answer the question, whether the application of lean tools leads to a better performance however, no study is available today. Therefore, an empirical study has been conducted, where the effectiveness of lean empowerment has been tested and compared to the performance of warehouse, who continued to work as before

Dual Robot Kit Preparation in Batch Preparation of Component Kits for Mixed Model Assembly

Kitting is a materials supply principle that plays a vital role for performance in mixed model assembly systems. The kit preparation process, whereby component kits are created, is central when kitting is applied. Kit preparation is a form of materials handling and is associated with several ergonomic and quality related issues. Robotics holds a great potential for decreasing the need for human labour, but literature on the topic is scarce. The purpose of this paper is to identify the time efficiency potential of a dual robot application for kit preparation. To address the purpose, a mathematical model is developed that allows dual robot kit preparation to be analysed and compared with manual kit preparation. Furthermore, the model supports identification of a suitable batch size given a lead time requirement from the assembly system. A numerical example shows dual robot kit preparation to be slightly more efficient than its manual ditto for preparation of 2, 3 and 4 kit batch sizes. The paper’s makes a theoretical contribution in terms of the time efficiency model of dual robot kit preparation. This model is also useful for practitioners when evaluating the potential of dual robot arm kit preparation in their own processes

The Effect of Pick-by-Light-Systems on Situation Awareness in Order Picking Activities

Pick-by-light systems are widespread in the industry, especially in order picking. Literature shows their potential to reduce picking-time and –errors as well as mental load. Using pick-by-light is also relevant for training processes since employees may tend to rely on these systems without consciously comprehending and processing the environment. Situation Awareness is a term referring to this fact and is analyzed in the study described in this article. The study took place in the learning factory “Center for industrial Production” at TU Darmstadt with N=31 subjects, who completed two order picking tasks: One of them assisted by pick-by-light and the other by pick-by-paper. SAGAT-based Situation Awareness showed no significant differences, whereas heart rate showed higher strain when using pick-by-light. NASA TLX showed a higher subjective strain using pick-by-paper

Bi-objective Assignment Model for Lean Order Picking in a Warehouse

With the introduction of Lean Warehousing, we are committed to using lean principles for more efficient warehousing operations, which are performed with quality and safety. Manual order picking, on which the paper is focused, is currently considered the most unfriendly to humans because, in the long run, it contributes to the appearance of musculoskeletal disorders. We record not only the increase in the average age of employees in warehouses but also in the number and duration of sick leave due to back and muscle pain. This paper explores the possibility of productive work while preventing order pickers from Work-Related Musculoskeletal Disorders. Using a laboratory experiment, we determine retrieval times for units with different characteristics and study required postures by guidelines of Revised NIOSH Lifting Equation. The final goal is to create a bi-objective assignment model

Supervised and unsupervised learning in vision-guided robotic bin picking applications for mixed-model assembly

Mixed-model assembly usually involves numerous component variants that require effective materials supply. Here, picking activities are often performed manually, but the prospect of robotics for bin picking has potential to improve quality while reducing man-hour consumption. Robots can make use of vision systems to learn how to perform their tasks. This paper aims to understand the differences in two learning approaches, supervised learning, and unsupervised learning. An experiment containing engineering preparation time (EPT) and recognition quality (RQ) is performed. The findings show an improved RQ but longer EPT with a supervised compared to an unsupervised approach

Economic and Performance Analysis of Dual-bay Vertical Lift Modules

Warehouse picking is one of the most time and cost consuming activities in a warehouse, often requiring the presence of human operators, who travel within the aisles to retrieve the items needed by the customers. Several studies demonstrate that the travelling activity can represent even the 50% of the total picking time, with a subsequent creation of a separate storage and picking area for small objects. In the last years, new solutions for order picking systems have been developed, especially for small items. One of these solutions requires Vertical Lift Modules (VLMs), storage columns with extractable trays. In this paper, the employ of dual-bay VLMs, compared to a carton racks warehouse, has been analysed from an economic point of view. Some mathematical formulations have been developed, to estimate the total annual cost and the respective convenience limits of both systems, according to their productivity. Moreover, some useful guidelines for practitioners are derived

Order picking performance improvement through storage location assignment: the case of a hardware wholesaler

Order picking is the most time-consuming, labour-intensive, and costly activity in picker-to-parts picking systems. The literature frequently minimises picking time but ignores the picking error, which affects picking efficiency and customer satisfaction. This paper to minimise picking time and picking error using multi-objective optimisation. Three storage location assignment policies, i.e., ABC-based, product-popularity-based (PPB) and product-relation-based (PRB), are deployed to minimise the picking time. PPB policy gave both the minimum picking time and picking error, with the trade-off between the two objectives presented in a Pareto frontier. Hence, the managers can determine a storage policy based on the optimisation results

A Framework for Extended Reality System Development in Manufacturing

This paper presents a framework for developing extended reality (XR) systems within manufacturing context. The aim of this study is to develop a systematic framework to improve the usability and user acceptance of future XR systems. So that manufacturing industry can move from the “wow effect” of XR demonstrators into the stage whereas XR systems can be successfully integrated and improve the conventional work routines. It is essential to ensure the usability and user acceptance of XR systems for the wider adoption in manufacturing. The proposed framework was developed through six case studies that covered different XR system developments for different application areas of manufacturing. The framework consists of five iterative phases: (1) requirements analysis, (2) solution selection, (3) data preparation, (4) system implementation and (5) system evaluation. It is validated through one empirical case and seven identified previous studies, which partly aligned with the proposed framework. The proposed framework provides a clear guideline on the steps needed to integrate XR in manufacturing and it extends the XR usage with increased usability and user acceptance. Furthermore, it strengthens the importance of user-centered approach for XR system development in manufacturing

Human error assessment in supply chain: case study in road transport services

This research presents an approach from cognitive ergonomics in human error for logistics sector, particularly in the case of land freight transport. For this, the Systematic Human Error Reduction and Prediction Approach – SHERPA, and Success Likelihood Index Method - SLIM methods were applied in three companies participating. Errors were identified in the generation of the cost of the service (T1), in the entry of service data (T2), in notifying customers of the news of the service (T3) and, preparation of the documentation for the drivers (T4). Errors whose probability of occurrence ranged from 24.8% to 34.2% were quantified. The reliability of each error was determined as an independent system in which case the result for tasks T1, T2 and T3 was 80% and 75% for T4.Esta investigación presenta un acercamiento desde la ergonomía cognitiva en la determinación del error humano en el sector logístico, particularmente para el caso del transporte terrestre de carga. Para ello se aplicaron los métodos de Systematic Human Error Reduction and Prediction Approach – SHERPA y Success Likelihood Index Method – SLIM en tres empresas participantes del estudio. Se identificaron errores en la generación del costo del servicio (T1), en el ingreso de datos del servicio (T2), en la notificación a los clientes de las novedades del servicio (T3) y en la preparación de la documentación para los conductores (T4). Se cuantificaron los errores cuya probabilidad de ocurrencia oscilaron entre un 24,8% y 34,2%. Se determinó la fiabilidad de cada error como un sistema independiente, en cuyo caso el resultado arrojado para las tareas T1, T2 y T3 fue del 80% y del 75% para T4

Varaston keräilyprosessin kehittäminen varastoautomaation avulla : Analyysi hankintaprosessin tueksi

Globaalien markkinoiden trendit asettavat varastonnin yhä kriittisempään asemaan yritysten tuotanto- ja toimitusketjuissa. Asiakkaat vaativat nopeampia ja tarkempia toimituksia, tuotantoketjussa vaaditaan tehokkaampaa materiaalin siirtymistä ja globaalien markkinoiden kilpailu asettaa vaatimuksia yhä korkeamman palvelutason ja laadun saavuttamiselle pienemmillä kustannuksilla. Varaston keräilyprosessi on varastotoiminnan aika- ja kustannusresursseja vievin toiminto, minkä vuoksi sen automatisointi on monille yrityksille tärkeä kilpailuetu ja kasvun mahdollistaja. Tämän tutkielman tavoite on selvittää varaston keräilyprosessin automaatiohankkeen tietotarpeita ja onnistumisen edellytyksiä sekä kehittää näiden perusteella keräilyprosessin automatisoinnin viitekehys. Viitekehyksen tarkoitus on tukea hankintaprosessia tietotarpeiden tunnistamisen kautta sekä ohjata toimittaja- ja sidosryhmien valintaa ja yhteistyötä optimaalisen varastoautomaatioratkaisun suunnitteluun ja käyttöönottoon. Tässä tutkielmassa analysoidaan tyypillisiä varaston keräilyprosessin automaatioratkaisuja, varaston suunnittelua sekä hankintaprosessin, käyttöönoton ja pitkäaikaisen käytön onnistumisen edellytyksiä. Kehitetty keräilyprosessin automatisoinnin viitekehys koostuu kolmesta osa-alueesta: varastoautomaation vaihtoehtojen tunnistaminen, varaston ja varastoautomaation suunnittelu sekä riskien huomioiminen ja ehkäiseminen. Viitekehys soveltuu mahdollisesti varastoautomaatioprojekteihin tukevaksi pohjaksi, malliksi tai päätöksenteon työkaluksi. Tämä tutkielma on tehty kirjallisuuskatsauksena