This paper outlines approaches for assessing and classifying manufacturing and service operations in terms of their suitability for use of cross-trained (flexible) workers. We refer to our overall framework as agile workforce evaluation. The primary contributions of this paper are: (i) a strategic assessment framework that structures the key mechanisms by which cross-training can support organizational strategy; (ii) a tactical framework that identifies key factors to guide the selection of an architecture and worker coordination policy for implementing workforce agility; (iii) a classification of workforce agility architectures; (iv) a survey of a broad range of archetypical classes of worker coordination policies; (v) a survey of the literature with an operational perspective on workforce agility; and (vi) identification of opportunities for research and development of architectures for specific production environments.

/pdf/agile-workforce-evaluation-a-framework-for-cross-training-1ay4oehvee.pdf

Agile workforce evaluation: a framework for cross-training and coordination

Call centers usually handle several types of calls, but it is usually not possible or cost effective to have every agent be able to handle every type of call. Thus, the agents tend to have different skills, in different combinations. In such an environment, it is challenging to route calls effectively and determine the staff requirements. This paper addresses both of these routing and staffing problems by exploiting limited cross-training. Consistent with the literature on flexible manufacturing, we find that minimal flexibility can provide great benefits: Simulation experiments show that when (1) the service-time distribution does not depend on the call type or the agent and (2) each agent has only two skills, in appropriate combinations, the performance is almost as good as when each agent has all skills. We apply this flexibility property to develop an algorithm for both routing and staffing, aiming to minimize the total staff subject to per-class performance constraints. With appropriate flexibility, it suffices to use a suboptimal routing algorithm. Simulation experiments show that the overall procedure can be remarkably effective: The required staff with limited cross-training can be nearly the same as if all agents had all skills. Hence, the overall algorithm is nearly optimal for that scenario.

/pdf/a-staffing-algorithm-for-call-centers-with-skill-based-3xtqzllvtd.pdf

A Staffing Algorithm for Call Centers with Skill-Based Routing

In this paper, we present a new perspective on flexibility in manufacturing and service operations by exploring a type of operational flexibility that we term "structural flexibility (SF)." We focus on strategic-level issues of how flexibility can be created by using multipurpose resources such as cross-trained labor, flexible machines, or flexible factories. The proposed structural flexibility method uses the structure of the capability pattern to generate indices that quantify the ability of a system to respond to variability in its environment. Simulations of serial and parallel queueing networks provide evidence that this index is useful in predicting the performance rank of alternative designs for implementing multifunctionality in the face of variability. The proposed methodology supports managerial insight into structural design of manufacturing and service systems at the strategic level.

http://users.iems.northwestern.edu/~iravani/Structural_Flexibility.pdf

Structural Flexibility: A New Perspective on the Design of Manufacturing and Service Operations

The concept of chaining, or in more general terms, sparse process structure, has been extremely influential in the process flexibility area, with many large automakers already making this the cornerstone of their business strategies to remain competitive in the industry. The effectiveness of the process strategy, using chains or other sparse structures, has been validated in numerous empirical studies. However, to the best of our knowledge, there have been relatively few concrete analytical results on the performance of such strategies vis-a-vis the full flexibility system, especially when the system size is large or when the demand and supply are asymmetrical. This paper is an attempt to bridge this gap.

We study the problem from two angles: (1) For the symmetrical system where the (mean) demand and plant capacity are balanced and identical, we utilize the concept of a generalized random walk to evaluate the asymptotic performance of the chaining structure in this environment. We show that a simple chaining structure performs surprisingly well for a variety of realistic demand distributions, even when the system size is large. (2) For the more general problem, we identify a class of conditions under which only a sparse flexible structure is needed so that the expected performance is already within e optimality of the full flexibility system.

Our approach provides a theoretical justification for the widely held maxim: In many practical situations, adding a small number of links to the process flexibility structure can significantly enhance the ability of the system to match (fixed) production capacity with (random) demand.

/pdf/design-for-process-flexibility-efficiency-of-the-long-chain-df4n0oqh34.pdf

Design for Process Flexibility: Efficiency of the Long Chain and Sparse Structure

The long chain has been an important concept in the design of flexible processes This design concept, as well as other sparse designs, have been applied by the automotive and other industries as a way to increase flexibility in order to better match available capacities with variable demands Numerous empirical studies have validated the effectiveness of these designs However, there is little theory that explains the effectiveness of the long chain, except when the system size is large, ie, by applying an asymptotic analysis

Our attempt in this paper is to develop a theory that explains the effectiveness of long chain designs for finite size systems First, we uncover a fundamental property of long chains, supermodularity, that serves as an important building block in our analysis This property is used to show that the marginal benefit, ie, the increase in expected sales, increases as the long chain is constructed, and the largest benefit is always achieved when the chain is closed by adding the last arc to the system Then, supermodularity is used to show that the performance of the long chain is characterized by the difference between the performances of two open chains This characterization immediately leads to the optimality of the long chain among 2-flexibility designs Finally, under independent and identically distributed iid demand, this characterization gives rise to three developments: i an effective algorithm to compute the performances of long chains using only matrix multiplications; ii a result that the gap between the fill rate of full flexibility and that of the long chain increases with system size, thus implying that the effectiveness of the long chain relative to full flexibility increases as the number of products decreases; iii a risk-pooling result implying that the fill rate of a long chain increases with the number of products, but this increase converges to zero exponentially fast

Understanding the Performance of the Long Chain and Sparse Designs in Process Flexibility

We consider queueing systems with multiple classes of customers and heteroge- neous servers where customers have the flexibility of being processed by more than one server and servers possess the capability of processing more than one customer class. We provide a unified framework for the modeling and analysis of these systems under arbitrary customer and server flexibility and for a rich set of control policies that includes customer/server-specific priority schemes for server and customer selection. We use our models to generate several insights into the effect of system configuration and control policies. In particular, we examine the relationship between flexibility, control policies and throughput under varying assumptions for system parameters. © 2004 Wiley Periodicals, Inc. Naval Research Logistics 51: 755-782, 2004. In this paper, we consider the modeling and analysis of flexible queueing systems. We use the term flexible queueing systems to refer to systems with heterogeneous servers and multiple customer classes where customers have the flexibility of being processed by more than one server and servers possess the capability of processing more than one customer class. Customer classes can vary in demand rate and routing flexibility. Servers can vary in service rates and service flexibility. The assignment of customers to servers is determined by a server selection rule, that can be customer-specific, and the selection of the next customer to serve is determined by a queue selection rule, that can be server-specific. An example of a flexible queueing system is shown in Figure 1. Flexible queueing systems arise in a variety of contexts, including manufacturing (Buzacott and Shanthikumar (8)), telecommunication networks (Ross (23)), computer systems (Kleinrock

Suri Gurumurthi

Papers

Modeling and analysis of flexible queueing systems