Synchronisation of material flows in mass-customised production systems: a literature-based classification framework and industrial application

The mass customisation strategy is needed by manufacturing companies to face the increasing variety and unpredictability of products required by customers. However, mass customisation may increase the complexity of managing manufacturing and production logistics activities, for example due to reduced product batch sizes. The synchronisation of material flows within the factory is emerging as a way to address this complexity, as it enables an effective and efficient implementation of mass customisation. Indeed, the fourth Industrial Revolution introduces new digital levers, which can be combined with traditional managerial levers to achieve the synchronisation of material flows within the factory. This study contributes to the rising stream of research on this topic. A systematic literature review was conducted, leading to the development of a classification framework of the levers supporting the synchronisation of material flows. The identified managerial levers are: storage of materials, feeding policy, and scheduling. The digital levers are: materials tracking, process tracking, data analytics, and assistance systems. The developed framework was operationalised in four industrial cases and applied as a tool to map their levers related to the synchronisation of material flows

A study of a kanban based assembly line feeding system through integration of simulation and particle swarm optimization

With increase in differentiation and decreasing batch size of products, feeding the assembly line at regular intervals is considered to be a critical problem in today's manufacturing sector. Yet no clear solution has been developed for this problem; therefore, the main focus of this research is to discuss the different aspects of line feeding, the latest trend in literature, and to propose an innovative method to support solving the problem. A discrete event simulation model is developed and a mathematical model based on particle swarm optimization is used to support the simulation. The hybrid model is finally applied to practical situations. Results show how different settings of kanban influence the performance of the assembly line feeding system. The biggest novelty item is certainly the recognition of the trade-off between kanban size and number of kanban and the importance of investigating its behaviour during the design of the system. (C) 2019 by the authors; licensee Growing Science, Canad

Value Stream Mapping with Microsoft Dynamics AX

Microsoft Dynamics AX (AX) is an Enterprise Resource Planning (ERP) system, and like ERP systems in general, it supports the lean production philosophy only to a limited extent. One way to provide a better support for lean production would be a feature for value stream mapping with AX as data source. The purpose of this study was to examine if plant level current state value stream maps could be created with an existing software using AX as data source. The relevance of such maps in order to gain a realistic representation of the value stream has also been evaluated. In order to fulfil the purpose, a comprehensive literature study has been conducted. From established theories, the information needed for mapping of a value stream has been identified. Next it has been investigated to what extent this information is available in AX, and how it can be visualized. Finally it is investigated how relevant this visualization is for realistically describing the actual value stream. A number of additional value stream mapping tools are also examined, in relation to the existing data in AX. It is concluded that plant level current state value stream maps can be created with data from AX and that this method of producing value stream maps has some advantages in relation to the conventional method: A time saving potential, clearness, and a single source of data. Disadvantages are also identified, including: Risk for lack of specific data, approximations of data, reliance on user input and frequent updating. It is also concluded that this method of value stream mapping is limited in relation to the lean principles, mainly due to two facts: Strategic product families can not be identified, and AX does not support the way production is controlled in a lean production environment. Therefore further research is suggested, before a feature for value stream mapping is implemented

Efficient Material Flow in Mixed Model Assembly Lines

Effizienten Materialfluss im Mischmodell Montagelinien Diese Arbeit untersucht ein Materialflusssystem für Fließlinien zur Fertigung von variantenreichen Produkten. Sogenannte Routenzüge kommen häufig zur Bereitstellung von Teilen an den Arbeitsstationen einer Variantenfließlinien zum Einsatz. Die Teile werden in kleinen Behältern (Kleinladungsträgern) im Zentrallager oder in verteilten Zwischenlagern, sogenannten Supermärkten, auf den Routenzug geladen. Bei jeder Tour des Routenzugs werden mehrere Arbeitsstationen mit Kleinladungsträgern versorgt. Der zeitliche Abstand zwischen zwei Belieferungen definiert die Zugumlaufzeit. Ein derartiges Materialbereitstellungssystem, bezeichnet als In-Plant Milk Run, reduziert Bestands- und Transportkosten, weil es eine regelmäßige Just-in-Time Anlieferung der Materialien realisiert. Außerdem bringt es aufgrund der Nutzung kleiner Behälter ergonomische Vorteile mit sich. Weiterhin sinkt das Unfallrisiko. Deshalb findet das In-plant Milk Run System als Alternative zu Gabelstaplern in vielen Branchen, insbesondere in der Automobilindustrie, zunehmend Verwendung. Die Untersuchung dieser Systeme und die optimale Auswahl ihrer Parameter sind wichtige Anliegen, um die Transport- und Bestandskosten zu reduzieren. Diese Arbeit unterscheidet bei der Gestaltung von In-plant Milk Run Systemen fünf verschiedene Problemstellungen (Systeme). Für jedes System wird ein bestimmtes Planungsvorgehen zur Minimierung der kritischen Kosten vorgeschlagen. Zwischen den Systemen gibt es sowohl Ähnlichkeiten als auch Unterschiede. Die Methodik verwendet genetische Algorithmen, ganzzahlige und dynamische Programmierung, Simulation und analytische Untersuchung. Die fünf Systeme werden anhand von Kriterien klassifiziert. Als solche werden das Ausmaß von Störungen, die Verfügbarkeit der Routenzüge, die Genauigkeit der Materialbedarfsschätzungen, die Länge der Montagelinien, der mittlerer Teilebedarf und die Verfügbarkeit von technischer Infrastruktur, wie RFID- oder Barcode-Systemen, herangezogen. Unterschieden werden damit das bedarfsorientierte Zentrallager, der dezentrale bedarfsorientierte Supermarkt, das traditionelle Kanban-System, das elektronische Kanban-System und ein hybrides System, bestehend aus bedarfsorientiertem und e-Kanban-System. Dabei kann das Kanban-System sowohl im Zentrallager, als auch im System dezentraler Supermärkte zum Einsatz kommen. In bedarfsorientierten Systemen wird der Materialbedarf der Arbeitsstationen für eine gewisse Anzahl an Schichten aus der Produktionssequenz und den entsprechenden Stücklisten abgeleitet und ist damit exakt bekannt. In allen Systemen werden einige Restriktionen berücksichtigt. Hierunter fallen die Routenzugkapazität, die Dauer einer Tour und die Kapazität der Lagerflächen direkt an der Montagelinie. In jedem System sind drei Entscheidungsprobleme, das Routing, Scheduling und Loading Problem, zu lösen. Das Routing Problem beinhaltet die Zuordnung von Zügen zu Gruppen von Arbeitsstationen. Im Scheduling Problem werden die Zugumlaufzeit und der Zeitpunkt der ersten Belieferung für jeden Routenzug festgelegt. Die Lösung des Loading Problems erfordert die Determinierung von Art und Menge der in jedem Zyklus und an jede Arbeitsstation ausgelieferten Behälter. Im Falle des Vorhandenseins von Zyklen, in denen der Materialbedarf an einzelnen Arbeitsstationen die Routenzugkapazität übersteigt, werden einige Behälter vorzeitig angeliefert. Dieser Fall wird als „Early Loading“ bezeichnet und tritt in Kanban-Systemen nicht auf. Im System dezentraler bedarfsorientierter Supermärkte ist zusätzlich die Anzahl und der Standort der Supermärkte zu bestimmen („Supermarket Location Problem“). Im traditionellen Kanban-System erfolgt die Festlegung der Kanbanzahl basierend auf dem Zielkonflikt zwischen mittlerem Linienbestand und Fehlbestandswahrscheinlichkeit. Im e-Kanban-System wird der Umfang des zirkulierenden Bestands analog bestimmt. Außerdem wird ein neues Konzept, der sogenannte „Adjusted Electronic Kanban“, zur Behandlung von Kapazitätsengpässen des Routenzugs vorgestellt. Die Ergebnisse sind abhängig vom betrachteten System. Die Leistungsfähigkeit des genetischen Algorithmus zur Lösung des Supermarket Location Problems wurde anhand der Ergebnisqualität, CPU Zeit und der Variabilität dieser beiden Größen untersucht. Es wurden akzeptable CPU Zeiten und eine hohe Ergebnisqualität erreicht. Die Leistungsfähigkeit der drei Kanban-Systeme wurde unter Verwendung von Simulation getestet. Hierbei wurde die Vorteilhaftigkeit des Adjusted Electronic Kanban insbesondere im Fall begrenzter Routenzugkapazität bewiesen. Der inverse Zusammenhang zwischen mittlerem Linienbestand und Fehlbestandswahrscheinlichkeit konnte aufgezeigt werden. Im Falle der bedarfsorientierten Systeme wurde der Effekt von dynamischer Disposition, Early Loading und Minimierung der Anzahl zusätzlicher Anhänger deutlich gemacht. Bei Verwendung des hybriden Systems aus e-Kanban und bedarfsorientiertem System, liefert die dynamische Disposition in Bezug auf die Verarbeitung von Störungenerheblich bessere Resultate als die Einzelsysteme, insbesondere bei hohem Materialbedarf an den Arbeitsstationen.This study investigates the material handling system used in mixed model assembly lines which are important to produce diversified product models to satisfy the increasing customer demand. Tugger trains are used to feed by parts the workstations in the assembly lines. These parts are loaded on trains in small containers (bins) from the warehouse or intermediate stores scattered in the factory. These stores are called supermarkets, which are closer to workstations than the main warehouse. In each train tour, several workstations are replenished by bins every a certain time period called train cycle time. This replenishment system is called in-plant milk run which is used to reduce inventory and transportation costs because of its dependence on repetitive just-in-time parts delivery. Besides reducing costs, ergonomic advantages are obtained due to the use of small-sized bins. Safety hazards are also reduced. As an alternative to forklift system, in-plant milk run was used by several industries especially the automotive industry. It is important to investigate this system and to design its parameters to reduce the total material handling and inventory cost. The study divides the general problem to five different problems (systems) based on the situation on the ground. For each system, a certain planning approach is designed to optimize the parameters of the system to minimize its critical costs. There are some similarities and differences between the systems. The methodology is based on genetic algorithm, integer programming, dynamic programming, simulation, and analytical investigation. The five different systems are classified based on factors such as level of assembly line disturbances, availability of tugger trains, accuracy of expectation of workstations demand for parts, the length of assembly lines and their average demand for parts, and the availability of technical infrastructure such as radio frequency identification (RFID) or bar code technologies. These systems are main warehouse demand-oriented, decentralized supermarket demand-oriented, traditional kanban, electronic kanban, and a hybrid system of e-kanban and demand-oriented systems. The two kanban systems can be applied in both main warehouse and decentralized supermarkets systems. In demand-oriented systems, the exact workstations demand for parts is assumed to be known for the next few shifts based on the predetermined sequencing of product models and needed parts for each product model. Generally some constraints are considered in all the five systems. These constraints are tugger train capacity, tour time, and the capacity of area beside stations. There are three general problems that must be investigated in the systems. These problems are routing, scheduling, and loading problems. Routing problem is the assignment of trains to different stations. In scheduling problem, the train cycle time and the beginning of the movement of the each train are determined. In loading problem, the type and quantities of bins delivered in each train cycle to each workstation are determined. In the case that there are some peak demand periods in which the total stations demand for parts is more than the tugger trains capacity, some bins are delivered before they are needed. This case is called ‘early loading’. Early loading does not exist in both the traditional and electronic kanban systems. In decentralized supermarket demand-oriented system, the location and number of supermarkets are determined. In traditional kanban system, the number of kanban is determined based on the tradeoff between the average line-side inventory and workstation starvation. In e-kanban, the size of circulating inventory in the system is determined for the same purpose. A new approach namely, adjusted electronic kanban, is presented to accommodate train capacity problems. The results depend on the systems investigated. The performance of genetic algorithm used in supermarket location problem was tested based on the quality of the results, CPU time, and variability in both of them. Reasonable CPU time and high quality of results were obtained. The performances of e-kanban, adjusted electronic kanban, and traditional kanban were tested using simulation, where the superiority of adjusted electronic kanban was proven especially in the case of limited tugger trains capacity. The inverse relationship between the average line-side inventory and workstation starvation was presented. In the case of demand-oriented system, the effects of using dynamic scheduling, early loading, and the objective of minimizing the number of extra trailers were obvious to reduce the problems of tugger train limited capacity. In the case of using the hybrid system of e-kanban and demand-oriented systems, the dynamic planning approach outperforms the traditional systems to accommodate the line disturbances especially in the case of large workstations demand

An optimization model for material supply scheduling at mixed-model assembly lines

This study is motivated by a real case study and addresses the material supply problem at assembly lines. The aim of the study is to optimally schedule the delivery of raw material at assembly lines while using the minimum number of vehicles. To cope with the problem an original mixed integer linear programming model has been proposed based on the assumptions and constraints observed in the case study. The validity of the model has been examined by solving several real cases and analysing different scenarios. The results of the study show the efficiency and effectiveness of the model.CC BY-NC-ND 4.0</p

Improvement in planning and resource management for an automotive company’s parts feeding system

The increasing sophistication of the automotive market and the constant change in customer requirements increases companies’ concern to ensure efficient internal logistic flows in line with Just-In-Time philosophy and Lean principles, to deal with wastes and variability. Variability arises from the growing differentiation of products, from the adoption of multi and mixed model assembly lines, and from the uncertainty in customer demand resulting from the worldwide outbreak of COVID-19. Considering the automotive supplier company as research subject, several problems were found to be compromising the efficiency of one of its in-plant parts’ feeding systems, the most critical problem being the lack of planning and management of resources (human and material) needed to perform the logistic service. Through Action-Research methodology stages, the actions taken culminated in the development of a simulation and decision-support tool for the component supply system resource management and efficiency improvement. The simulations made revealed reliable and adjusted results of workload and workforce to face the variations in customer demand and the existing product mix. After the tool creation, resource planning and balancing was no longer based on managers experience and empirical knowledge only but based on scientific knowledge: concise and reliable data from information systems, measurements, study of times, and literature review on in-plant milk run systems, lean, just-in-time and continuous improvement techniques.info:eu-repo/semantics/publishedVersio

Re-balancing problem for assembly lines: new mathematical model and exact solution method

The purpose of this study is to develop a new mathematical model and an exact solution method for an assembly line rebalancing problem. When an existing assembly line has to be adapted to a new production context, the line balancing, resources allocation and component management solutions have to be revised. The objective is to minimize the number of modifications to be done in the initial line in order to reduce the time and investment needed to meet new production requirements. The proposed model is evaluated via a computational experiment. The obtained results the efficacy of the proposed method