Production, Manufacturing and LogisticsCalibrating cross-training to meet demand mix variation and employee absence
Introduction
Demand-mix flexibility, also called product flexibility and process flexibility, consists of the capacity of a production process to produce a variety of products to meet demand mix variability. The benefits of this flexibility have been demonstrated by Jordan and Graves (1995). Cross-training workers can increase production flexibility, thereby helping it to efficiently deliver a broader range of products by increasing overall workforce skills, so that they can cope with a wider range of possible demands (Hopp & VanOyen, 2004). Thus, cross-training workers are potentially an effective source of demand mix flexibility.
The question of ‘who should be trained on which tasks?’ is an important one for many organizations, some of whom adopt cross-training policies not as a response to direct skill requirements, but rather for employee job enrichment, to reduce boredom, or to create greater agility globally. To obtain some of these benefits, the specification of particular levels of cross-training may not be necessary. More often, cross-training is necessary for gaining flexibility in order to cope with demand variations, and redundancy, as a compensation for employee absences. In this case, the effectiveness of cross-training depends in largely upon how cross-training is carried out. When acquiring ability in new tasks requires significant durations and concomitant training cost, establishing the appropriate cross-training goals becomes critical.
The literature distinguishes between cases in which various specific patterns of flexibility are considered, and when full flexibility is assumed or allowed. Furthermore, situations in which the workforce is the only essential resource must be distinguished from those in which other resources are involved (see Section 2.1). The problem addressed here corresponds to full flexibility, with the workforce as the critical resource, as is common in practice. This situation can be found in call centers (Batta, Berman, & Wang, 2007), maintenance service operations (Brusco & Johns, 1998), nurse staffing (Bard & Purnomo, 2005) and retail services (Berman & Larson, 2004), among others. In fact, labor is often the limiting resource in practice (Slomp & Molleman, 2002).
Some literature addresses demand mix variation by considering a set of future demands along with the corresponding probabilities of occurrence (see Section 2.2). In this paper, the demand scenarios to be covered will be defined by establishing the degree of variability that the organization wants to be able to meet. This approach had not been previously dealt with in the literature and has practical applicability, as we will support below.
When addressing cross-training and demand coverage, different complementary characteristics can be considered. The skills involved can be either categorical or hierarchical. Categorical skills are binary in nature, and as such are either possessed or not possessed at all (De Bruecker, Van den Bergh, & Demeulemeester, 2014). When skills are hierarchical, they can be performed at different levels, as it has been assumed by some previous research (Azizi and Liang, 2013, Pinker et al., 2009). Similarly, the literature has considered homogenous or heterogeneous workers from the point of view of learning capacity (Shafer, Nembhard, & Uzumeri, 2001).
In addition, cross-training in a single department or between departments can be considered (Van den Bergh, Beliën, De Bruecker, Demeulemeester, & De Boeck, 2013), the possibility of overtime can be included (Wright & Mahar, 2013) and, for each worker, primary and secondary skills can be distinguished (De Matta and Peters, 2009, Guerry et al., 2013). In this paper, several straightforward assumptions regarding these options have been adopted: categorical skills, homogeneity of workers regarding learning, one single department, with no overtime considered. Primary and secondary skills are not differentiated when defining cross-training objectives.
The objective of this paper is to develop a method for determining cross-training goals for a work team in order to meet a certain level of demand mix variation, which is established by using the time devoted to each product. It is assumed that there is some level of worker absenteeism and that all workers can be trained to perform each task. Previous cross-training is taken into account to consider cases wherein there may be preexisting teams and cross trained skills. The problem is analyzed and solved via the development and use of a constraints-based selection procedure, which we term CODEMI. We will examine this novel approach using several computational cases. A primary contribution of the paper is the development of this novel and practical approach for addressing the cross-training problem.
In the remainder of this paper we discuss the relevant literature review in Section 2, followed by definitions and description of the problem, along with definitions of the relevant variables for our modeling approach in Section 3, the model itself and discussion on scope and disaggregation. Section 4 presents a novel procedure for generating solutions, illustrative examples, an evaluation of the solution approximation obtained, and computational performance. We report our conclusions in Section 5.
Section snippets
Flexibility and cross-training
The operations management literature on flexibility can be classified into two main streams (Chou, Chua, Teo, & Zheng, 2010): (1) work that describes and examines the value of different patterns of flexibility and (2) work assuming potential full flexibility, in which any resource, such as machines or workers, can eventually perform any task. The first group of work focuses on schemes that, with limited resource flexibility, provide outcome flexibility that is not far from optimal. This result
Solution procedure algorithm
We propose a procedural algorithm (COnstraints for DEmand MIxes, CODEMI) for solving the general problem posited that applies the concept of constraint selection, which is based on the idea that only a few constraints bind the optimal solution. Various general algorithms have been developed for solving a range of linear problems (Arsham, 2007, Myers, 1992). The idea of adding constraints based on partial results obtained follows the classical work of Dantzig, Fulkerson, and Johnson (1954). We
Conclusions
We addressed the problem of determining a cross-training skill matrix that a work team must have in place in order to meet a level of demand mix variation and workplace absences. Demand mix variations are defined in a straightforward manner in order to relate well to common business practices, thereby allowing for practical use and future improvements of the proposed approach. This paper contributes to the literature on determining appropriate cross training levels and skill matrices for groups
References (44)
A computationally stable solution algorithm for linear programs
Applied Mathematics and Computation
(2007)- et al.
Preference scheduling for nurses using column generation
European Journal of Operational Research
(2005) - et al.
Balancing staffing and switching costs in a service center with flexible servers
European Journal of Operational Research
(2007) - et al.
A queueing control model for retail services having back room operations and cross-trained workers
Computers & Operations Research
(2004) - et al.
Human resource planning in knowledge-intensive operations: A model for learning with stochastic turnover
European Journal of Operational Research
(2001) - et al.
Cross-training policies in field services
International Journal of Production Economics
(2012) - et al.
Developing work schedules for an inter-city transit system with multiple driver types and fleet types
European Journal of Operational Research
(2009) - et al.
Impact of productivity on cross-training configurations and optimal staffing decisions in hospitals
European Journal of Operational Research
(2014) - et al.
A measure of cross-training benefit versus job skill specialization
Computers & Industrial Engineering
(2009) The examination of worker cross-training in a dual resource constrained job shop
European Journal of Operational Research
(1991)
Modeling the benefits of cross-training to address the nursing shortage
International Journal of Production Economics
Personnel scheduling: A literature review
European Journal of Operational Research
Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction
Omega
Recent developments in dual resource constrained (DRC) system research
European Journal of Operational Research
Cross-training decisions in field services with three job types and server–job mismatch
Decision Sciences
Workforce cross-training decisions in field service systems with two job types
Journal of the Operational Research Society
Design principles for flexible systems
Production and Operations Management
Forming effective worker teams for cellular manufacturing
International Journal of Production Research
An integrated approach to worker assignment, workforce flexibility acquisition, and task rotation
Journal of the Operational Research Society
Staffing a multiskilled workforce with varying levels of productivity: An analysis of cross-training policies
Decision Sciences
Staffing multiskill call centers via linear programming and simulation
Management Science
Optimal Workforce Mix in Service Systems with Two Types of Customers
Production & Operations Management
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