Jeffrey M. Alden

Throughput analysis of production systems: recent advances and future topics

Throughput analysis is important for the design, operation and management of production systems. A substantial amount of research has been devoted to developing analytical methods to estimate the throughput of production systems with unreliable machines and finite buffers. In this paper we summarise the recent studies in this area. In addition to the performance evaluation of serial lines, approximation methods for more complex systems, such as assembly/disassembly systems, parallel lines, split and merge, closed-loop systems, etc., are discussed. Moreover, we propose future research topics from the automotive manufacturing systems perspective.

Rolling horizon procedures in nonhomogeneous Markov decision processes

By far the most common planning procedure found in practice is to approximate the solution to an infinite horizon problem by a series of rolling finite horizon solutions. Although many empirical studies have been done, this so-called rolling horizon procedure has been the subject of few analytic studies. We provide a cost error bound for a general rolling horizon algorithm when applied to infinite horizon nonhomogeneous Markov decision processes, both in the discounted and average cost cases. We show that a Doeblin coefficient of ergodicity acts much like a discount factor to reduce this error. In particular, we show that the error goes to zero for any fixed rolling horizon as this Doeblin measure of control over the future decreases. The theory is illustrated through an application to vehicle deployment.

General Motors Increases Its Production Throughput

In the late 1980s, General Motors Corporation (GM) initiated a long-term project to predict and improve the throughput performance of its production lines to increase productivity throughout its manufacturing operations and provide GM with a strategic competitive advantage. GM quantified throughput performance and focused improvement efforts in the design and operations of its manufacturing systems through coordinated activities in three areas: (1) it developed algorithms for estimating throughput performance, identifying bottlenecks, and optimizing buffer allocation, (2) it installed real-time plant-floor data-collection systems to support the algorithms, and (3) it established common processes for identifying opportunities and implementing performance improvements. Through these activities, GM has increased revenue and saved over 2.1 billion in over 30 vehicle plants and 10 countries.

Comparisons of two-machine line models in throughput analysis

Throughput analysis is important for the design, operation and management of manufacturing systems. Due to the large state space, exact analytical results only exist for two-machine serial lines. To analyse longer lines or more complex systems, the two-machine line model becomes the building block for further development. Therefore, many two-machine line models have been presented in the literature. A thorough understanding of the nature and differences of the various two-machine line models is critical for future analysis and development. In this paper, we study eight different two-machine models, categorized by synchronous and asynchronous lines with both time-dependent and operation-dependent failures, respectively. For each model, we introduce and compare the assumptions, calculation formulae, and their performance in system throughput. The results show that all the models exhibit similar performance with small differences, comparable to (or less than) the accuracy of data collection on the factory floor.

Approximating feeder line reliability statistics with partial data collection in assembly systems

Due to the difficulty and high cost of data collection, many feeder lines in assembly systems lack full collection of data. However, reliability statistics of feeder lines are important in throughput analysis and continuous improvement of manufacturing systems. In this paper, a simple approximation approach is presented to estimate the reliability statistics of feeder lines from the associated assembly stations collected blocking and starving information. It is shown that the approach is helpful for accurate throughput estimation and sensitivity analysis. We also show how feeder line speed can be used to improve the approximation.

Product-to-plant allocation assessment in the automotive industry

Abstract This paper presents a product-to-plant allocation process and a decision support computer tool to accelerate and document the task of assessing the cost and time to allocate a new product to a manufacturing plant. The process and computer tool were designed in the context of the automotive industry, but the process and decision support tools functionality apply to any industry that needs to make product-toplant allocations or make-or-buy decisions. The heart of the process is a list of descriptive plant and product characteristics, compiled from planners and manufacturing experts, whose comparison measures the cost and time to put a product into a plant. The process accelerates the allocation effort by iteratively screening plants while additional information is gathered so that incompatible plants can be eliminated from consideration before investing considerable time collecting detailed plant specifications or developing workarounds to incompatible characteristics.