Alp, Arda

2014, Alp, Arda, Reiner, Gerald

Motivated by the strategic importance of reduced lead times in today’s competitive business environment, this doctoral dissertation analyzes the dynamic impacts of lead time reduction (LTR) improvements on customer satisfaction and related financial performance metrics. The core thesis is centered on development of an integrated dynamic performance measurement framework which covers operational, customer-oriented and financial performance dependencies over time. The framework is demonstrated through two empirical industrial cases.
Effective reduction of lead time is possible through understanding the relationship between lead time and lead time related factors, and the implications of these relations on system performance. Reducing lead time can have direct and indirect effects, improving overall company performance in short-term and long-term. Due to certain system interactions, not only does operational performance improve, but so do customer satisfaction and financial measures which are affected in terms of, e.g., reduced inventories and inventory carrying costs; improved service quality, diminished cancelled orders and reduced penalty costs; increased sales, improved market shares and profitability.
In particular, this research targets to identify which situational factors play a critical role between lead time reduction strategies and related effect on performance, and to understand how reduction of lead time impacts long-term performance compared to short-term effects.
In this direction, an integrated performance measurement framework has been developed by considering mathematical principles of lead time reduction and covering dynamic dependencies between financial and non-financial performance dimensions. The framework is comprehensive yet simple enough to consider trade-off characteristics among both time-based and non-time-based metrics. The application of the framework was based on hybrid use of two methods: Queuing Theory Based Modeling (QTM) and System Dynamics Modeling (SD) .
Illustration of the lead time reduction framework is provided through two interrelated studies based on industrial applications done collaboratively with two international manufacturing companies. In this regard, two studies summarize these stages:
• The first study focuses on integrated analysis of some lead time reduction strategies on system performance (locating bottleneck capacity buffers, eliminating sources of waiting, setup time and reducing variability). In particular, we focus on the dynamic dependencies between bottleneck buffer configuration and station loading policies in order to analyze how those dependencies affect operational performance improvement: Throughput increase and reduction of lead time while considering various levels of demand variability. In particular, our analysis provides evidence for performance improvement without needing to invest to increase the bottleneck resource. Application of a particular station loading strategy and usage of multiple buffers (moving from a single common buffer to multi-buffers) yields better performance when variability increases.
• The second study focuses on analyzing the dynamic impacts of lead time reduction approaches on customer satisfaction and financial performance based on an industrial case created in joint collaboration with a European-based international company operating under make-to-stock manufacturing strategy. Based on the system’s characteristics some lead time reduction strategies are selected (i.e. optimization of batch size, reallocation of system resources by pooling labor and improving the setup time) and the industrial production process is successfully improved without significant cost and time investments. Subsequently, related effects on customer-based performance and corresponding financials such as capacity investment (i.e. buying a new machine) are analyzed. Key cost figures, such as processing, inventory, labor costs and others were determined by evaluating the underlying cost accounting system. Later, motivated by the industrial application, the framework was further analyzed using sensitivity analysis. The insights gathered through industrial applications are used to present a sensitivity analysis based on short and long-term demand and lead time interaction. The sensitivity analysis is used for two main purposes: (i) to analyze the long-term performance impacts of lead time reduction under the extreme conditions of market demand; (ii) to question the conditions to maintain the long-term sustainability of lead time reduction. The sensitivity analysis provided additional insight into the dynamic behavior of the demand and lead time interaction.