Milk-run Systems Research Articles

An integer mathematical model proposal for an agile external milkrun system

A milkrun system is a cyclical material delivery system that enables frequent small-batch deliveries with short lead times and low inventories. The literature classifies milkrun systems into external and internal categories. External milkrun systems in agile production environments, on the other hand, have not received much attention in the literature thus far. This study presents an integer mathematical model for the cost minimisation of external milkrun tours and discusses agile external milkrun systems. In addition to minimising costs, the proposed mathematical model can determine the types and number of milkrun vehicles. This is an important advantage for logistics professionals. The proposed model was coded using the Lingo software, and its effectiveness was shown using both a real-world problem and randomly generated test problems. The proposed model found the best solution in a few seconds for some real-world and most test problems. The results have shown that the proposed mathematical model for external milkrun systems in agile environments is useful.

Optimization of Hub-Based Milkrun Supply

Background: Milkrun-based material supply plays an important role in the automotive industry, as it is a material supply concept where high efficiency can be achieved. When implementing milkrun-based material supply, the milkrun supply of the production plant often has to be integrated with an existing warehouse material handling system, which frequently leads to a less efficient solution. Methods: In this paper, the author investigates the impact of a hub-based milkrun supply, where the collection processes in the component’s warehouse and the distribution processes in the assembly plant are connected to a hub, which is responsible for the sequencing of component demands. After a systematic literature review, the paper introduces a novel mathematical model, which makes it possible to describe the conventional milkrun-based solutions, the hub-based milkrun solutions, and to compare them in terms of the length of transportation routes, transportation time, total service time, and virtual emission points of view. Results: The scenario analysis demonstrates that the hub-based solution can lead to an efficiency improvement of about 13% in total service time, 23% savings in transportation time, and 45% savings in transportation time in the component’s warehouse. Conclusions: The article’s findings suggest that implementing a hub-based milkrun system in automotive material supply can significantly enhance efficiency. The described approach could lead to more streamlined operations in production plants by optimizing the integration of milkrun systems.

Route optimization opportunities in logistics

The article describes the process of warehousing complementing with the definition of concepts essential for warehousing and the various areas that can be found in most warehouses. In the rest of the article, the relationship between the warehouse and production will be discussed,then the presentation of milkrun systems and the related milkrun routes will come to the fore. In the last part of this article, we will introduce how to calculate the optimal milkrun path using the Solver add-in for Microsoft Excel.

Reference model of milk-run traffic systems prototyping

A declarative model is developed in this study. It allows us to formulate a constraint satisfaction problem that supports making decisions in the process of seeking a periodic distribution policy with a plan of whom to serve, how much to deliver, by which fleet of logistic trains travelling regularly, and on which routes. Implemented through a constrained, programming-driven solver, it allows for the formulation of forward and reverse milk-run vehicle routing and scheduling problems, subject to the constraints imposed by an in-plant distribution network. Consequently, it can be used to determine the number of transport trips and their organisation in time and space needed for the timely delivery of material to specific loading/unloading points. In this context, the proposed model meets the needs of an interactive search for solutions by the alternate and iterative formulation of successive forward and reverse decision problems.

Modelling material flow using the Milk run and Kanban systems in the automotive industry

AbstractMaterial flow management refers to the analysis and specific optimization of the inventory‐production system. Material flow can be characterized as the organized flow of material in a production process with the required sequence determined by a technological procedure. The Milk run system assures the transportation of materials at the right time and in an optimal manner. It should be combined with the Kanban system to highlight when something is required in the production process. This paper presents biological swarm intelligence, in general, and a particular model, particle swarm optimization (PSO), for modelling material flow using a Milk run system supported by a Kanban system in the automotive industry. The aim of this study is to create a new model for the optimal number of trailers for one train and optimal number of containers in a tugger train system when the route time period has been defined. A new modified PSO approach for integrating inventory‐production in a unique optimization model is used. The major modification to the original PSO is using the capacity of a container instead of a velocity component. Each new Kanban trigger is checked, and the total timing for the Milk run delivery solution is calculated for the necessary raw material capacity for each shop floor.

Digitalized milk-run system for a learning factory assembly line

In order to face current trends and challenges in industrial companies such as increasing number of variants, shorter product life cycles and higher product complexity, production logistics is one of the main areas for optimizing assembly systems. To increase the efficiency of the internal material supply, various lean system such as the milk-run were introduced. It has been shown in literature that digitalization technologies can be used to improve lean systems. Nevertheless, there is currently a lack of knowledge about the digitalization of milk-run systems, especially in learning factories. Therefore, this work is introducing a digitalized milk-run system in LEAD Factory, the learning factory of Graz University of Technology. This system relies on the RFID technology to automatically generate orders from a warehouse to a workstation. In the warehouse a pick-to-light system is used to show the logistics employee on a screen which parts to pick. To efficiently deliver the order to the workstation, the shortest path is calculated using a path planning algorithm that is used in robotics. The paper shows (1) developments of milk-run systems, (2) the concept for a digitalized milk-run system at the LEAD Factory and (3) its application within digitalization trainings.

Analysis of real-time tour building and scheduling strategies for in-plant milk-run systems with volatile transportation demand

In modern manufacturing systems, milk run (MR) systems represent route-based, cyclic material-handling systems that are used widely and enable frequent deliveries of containerized parts in small lot sizes from a central storage area to multiple points of use on the factory floor. Usually, deliveries take place at predetermined, constant time intervals. As in real-world MR systems, the number of containers to deliver in each interval varies in the short term, peaks in the number of deliveries may lead to capacity bottlenecks and late deliveries. In this paper, we develop three alternative strategies to determine starting times of MR cycles in real-time based on the current number of delivery orders in the system aiming to better handle variations in the number of orders. We evaluate the three strategies for different system load scenarios with respect to delivery service levels, order lead times, systems utilization and necessary number of MR trains using a discrete-event simulation model.

Efficiency in in-plant milk-run systems—The influence of routing strategies on system utilization and process stability

In-plant milk-run systems represent transportation systems, where materials are delivered from a central storage area to several points of use on fixed routes and in short and defined intervals. Milk-run systems generally enable frequent deliveries in low lot sizes with short lead times and low inventories at the points of use. Thus, stable and reliable system operation is crucial to avoid delays and material shortages. In industrial practice, milk-run trains usually share resources, for example, loading areas and technology and use the same tracks, leading to dependencies between routes and possible traffic jams and blockages, which significantly affect cycle times and may lead to instabilities in the system. We present a simulation model to analyse in-plant milk-run systems with a focus on typical traffic situations. We describe its application to a large industrial case study in detail and derive recommendations for designing routes with low risk of delays.

Operating Strategies for In-Plant Milk-Run Systems

In-plant milk-run systems are transportation systems, where materials are delivered from a central storage area to several points of use on defined routes and in short intervals. Milk-run systems generally enable frequent deliveries in smaller lot sizes with short lead times and low inventory at the points of use. In real milk-run systems, the number of deliveries per interval varies due to, e.g., variations in the production program. To enable efficient and stable milk-run system operations, these systems therefore must be able to cope with peaks in the number of deliveries. We develop different strategies for handling these delivery peaks and evaluate them with respect to delivery cost, lead time and service level using real material consumption data from two large companies from the automotive industry.

A Two-Phase Heuristic Algorithm for the Common Frequency Routing Problem with Vehicle Type Choice in the Milk Run

High frequency and small lot size are characteristics of milk runs and are often used to implement the just-in-time (JIT) strategy in logistical systems. The common frequency problem, which simultaneously involves planning of the route and frequency, has been extensively researched in milk run systems. In addition, vehicle type choice in the milk run system also has a significant influence on the operating cost. Therefore, in this paper, we simultaneously consider vehicle routing planning, frequency planning, and vehicle type choice in order to optimize the sum of the cost of transportation, inventory, and dispatch. To this end, we develop a mathematical model to describe the common frequency problem with vehicle type choice. Since the problem is NP hard, we develop a two-phase heuristic algorithm to solve the model. More specifically, an initial satisfactory solution is first generated through a greedy heuristic algorithm to maximize the ratio of the superior arc frequency to the inferior arc frequency. Following this, a tabu search (TS) with limited search scope is used to improve the initial satisfactory solution. Numerical examples with different sizes establish the efficacy of our model and our proposed algorithm.

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 2: number of Kanban and workstation starvation

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. In the first part of this two-part paper, a basic, single-tugger MR system is described, and stability conditions as well as the probability of exceeding either the physical capacity of the tugger or the prescribed cycle time are derived. Given the stability conditions, and the distribution of the number of containers requested per MR, in the second part of the paper, the number of Kanban required in the MR system is examined, and analytical approximations are derived both for the number of Kanban required and for predicting workstation starvation. The latter is a key concern when designing a MR system that will support workstations in a manufacturing plant. The performance of the analytic approximation is evaluated by simulating various MR systems. Our results suggest that, in a stable MR system, the number of Kanban and the physical capacity of the tugger have a bigger impact on workstation starvation than the prescribed cycle time.

Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 1: system stability and distribution of container starts

In lean manufacturing, milk run (MR) systems represent route-based, cyclic material-handling systems that are used widely to enable frequent and consistent deliveries of containerised parts on an as-needed basis from a central storage area (the ‘supermarket’) to multiple line-side deposit points on the factory floor. MR systems generally result in short lead times, low variability, and low line-side inventory levels. In the first part of this two-part paper, a basic, single-tugger MR system is described. Stability conditions are derived with respect to the ‘physical capacity’ of the tugger, and the ‘time utilisation’ of the driver/material handler, both of which are key criteria since the number of containers that can be delivered during each MR is limited, and each MR targets a prescribed cycle time to ensure timely and consistent deliveries. We also derive the distribution of the number of containers requested per MR, which allows the model to estimate the probability of exceeding the physical capacity of the tugger or the prescribed cycle time, under the assumption that sufficient kanban are available for each part type delivered by the MR system. The results of the study facilitate the design and analysis of MR systems used in industry.

Redesigning an assembly line through lean manufacturing tools

The implementation of a lean manufacturing strategy represents a robust contribution to the phase sequence that leads to operational excellence and the continuous improvement through the elimination of nonvalue-added activities. Therefore, lean practices contribute substantially to plant operational performance. This paper studies the use of value stream mapping (VSM) as a tool in lean manufacturing implementation and a framework of improvement activities, in particular for an efficient introduction of kanban and milkrun techniques. A case study illustrates VSM use, as well as kanban and milkrun systems application on an assembly line. Finally, the results obtained show the path of improvement, measured through the lean rate (LR) and dock-to-dock time (DtD).