Lead time modeling and acceleration of product design and development

Abstract

Abstract-New product development is an important business process and constitutes a major contributor to the business excellence of any manufacturing firm. Designing an optimized new product development process is an important problem in itself and is of significant practical and research interest. Lead time is an important performance metric for a product development organization. In this paper, we develop lead time models for product development organizations that involve multiple, concurrent projects with contention for human/technical resources. The objective is to explore how the lead times can be reduced using efficient scheduling, input control, load balancing, and variability reduction. The models are based on single class and multiclass queueing networks and capture important facets of a product development organization, such as: concurrent execution of multiple projects, contention for resources, feedback and reworking of project tasks, and variability of new project initiations and task execution times. Within the new product development process, we focus attention on the product design process, which is an important subprocess. Two product design organizations, which we call Company ABC and Company XYZ, provide the real-world setting for our model-based lead time reduction. First, we present a coarse, conceptual queueing network model of Company ABC and show how rapid performance analysis can be used to explore opportunities for accelerating the design process. In particular, we show how effective input control, process control, load balancing, and cross-functional work can cut the lead times. Next, we present multiclass queueing network models (re-entrant lines) for both the companies ABC and XYZ. The re-entrant line models show up certain scheduling issues pertaining to internal flows in the product design network. Using a class of fluctuation smoothing scheduling policies, we demonstrate how lead times can be reduced appreciably, without committing additional resources. The models presented are sufficiently generic and conceptual, and will be of much value in project planning and management in product design organizations and also more generally in product development organizations.Index Terms-Design cycle time, lead time reduction, multiclass queueing networks, new product development process, product design process, re-entrant lines, resource contention.

Robust Control Strategies for Multi–Inventory Systems with Average Flow Constraints

“…Such systems are met in several different contexts, such as manufacturing [2,7,8,14,16,20,21], network routing [13], communications [9], water distribution [15], logistics and traffic control [18]. Hence, their control is of relevant economic interest.…”

Process models in design and development

“…Adler et al (1995) argue that their model can accordingly predict cycle time more accurately than single-project approaches and, through what-if analysis, can provide useful insight into resourcing and congestion effects. Narahari et al (1999) build on this work, arguing that similar insights can be gained through a simplified model in which each project is represented as a single job that flows through a network of processing stations, each representing a project stage. The stations are organised in a reentrant line to simulate loop-backs between stages.…”

“…Although it does not represent complex concurrency within each project, this model can be used to assess project processing times and indicates how they can be improved through insights from queueing theory. In particular Narahari et al (1999) recommend that workers prioritise jobs using policies designed to reduce variability, and that companies throttle the number of projects they take on concurrently.…”

An Optimization-Based Approach for Design Project Scheduling

“…Problems of larger sizes with more than 100 activities were solved, however, solution qualities were hard to evaluate. In addition to optimization, heuristic procedures were developed by Ahn and Erenguc [2], Narahari et al [24], Yan and Wu [33], and Merkle et al [22]. These procedures are computationally efficient, however, results are often of questionable quality, and there is no good way to systematically improve the quality.…”