Cross-trained Workforce Research Articles

Study on Skills Gap Beyond COVID

Keeping up with the pace of technological advancement is a challenge for companies of all shapes and sizes. It is increasingly crucial to reskill and upskill in the changing era of innovation, especially post-pandemic (Beyond Covid), and acquiring soft skills is imperative for success in the digital era. The importance of soft skills, like teamwork, communication skills, problem-solving, and critical thinking, is a growing demand, heightened especially during the pandemic while working remotely. Upskilling ensures employees’ skillsets won’t become obsolete. As you reskill your employees, you create a more well-rounded, cross-trained workforce, and increase your team’s effectiveness. (itagroup.com, n.d.) According to the United Nations Department of Economic and Social Affairs, the equivalent of 255 million full-time jobs have been lost due to the pandemic, and 1.6 billion informal economy workers lacking a social safety net have been significantly affected. The recovery will be slow; global economic growth is expected to return to pre-pandemic levels only by 2022 or 2023. The pandemic has dramatically accelerated the need for new skills in the workforce, with social and emotional skills high in demand. The proportion of companies addressing empathy and interpersonal skills doubled in 2020, according to our newest McKinsey Global Survey on reskilling. (McKinsey, 2021)

Partitioning methods for pruning the Pareto set with application to multiobjective allocation of a cross-trained workforce

The Pareto (or nondominated set) for a multiobjective optimization problem is often of nontrivial size, and the decision maker may have a difficult time establishing objective criterion weights to select a solution. In light of these issues, clustering or partitioning methods can be of considerable value for pruning the Pareto set and limiting the decision to a few choice exemplars. A three-stage approach is proposed. In stage one, a variance-to-range measure is used to normalize the criterion function values. In stage two, maximum split partitioning and p-median partitioning are each applied to the normalized measures, thus producing two partitions of the Pareto set and two sets of exemplars. Finally, in stage three, the union of the exemplars obtained by the two partitioning methods is accepted as the final set of exemplars. The partitioning methods are compared within the context of multiobjective allocation of a cross-trained workforce to achieve both operational and human resource objectives.

Mathematical models and solution approach for cross-training staff scheduling at call centers

Call centers face demand variations over time across multiple service categories and typically employ a cross-trained workforce with flexible schedules to hedge against these fluctuations. In practice, it is often impossible to cross-train agents in each category, thus partial and limited cross-training are the norm. This adds another layer of complexity to determine the optimal mix of cross-trained workforce (on top of the shift and tour schedules) and has created a challenging problem in the optimization of staff schedules.To solve this problem to its fullest extent, an integer program that addresses cross-training, shift schedule, days off and break assignments across multiple service categories is proposed. The model is hard to solve and a two-phase sequential approach is developed. The first phase is to find the optimal mix of the workforce, i.e., the categories to be cross-trained and the time periods in which they are to be deployed; the second phase is a smaller staff scheduling model to find the composition of the workforce and to construct their weekly tours. For all the test cases, which are of practical sizes, the two-phase sequential approach provided better solutions than the solution of the original model with a state-of-the-art commercial solver subject to imposed time limits.Experimental results with data from a call center with nine categories clearly demonstrate the significance of cross-training. In fact, partial limited cross-training, where 30% of staff is cross-trained with two skills or 10% of staff is cross-trained with three skills, could result in considerable cost savings; however, these savings could diminish quickly with the increase of efficiency loss in secondary skills. Experiments also suggest that cross-training could be a more effective approach than part-time shifts to hedge against fluctuations across service categories.

Team performance in a lean manufacturing operation: it takes the will and a way to succeed

As contemporary manufacturing companies embark upon the lean transformation, they are relying upon a more collaborative, cross-trained workforce that strives to deliver a quality product at the required time. This longitudinal study tracked five multifunctional work teams involved in a pull system implementation effort at an electronics assembly plant. This research examines the relationship between team characteristics including the inclination to collaborate, team flexibility and shared team priorities; and team performance measures that include performance to schedule, product quality and labour efficiency. Team member collaboration was positively associated with schedule performance and labour efficiency. Team flexibility was positively related to product quality. Finally, the concept of team priority consonance is introduced, which showed a positive association with schedule performance.

Cross training efficiency and flexibility with process change

Purpose– The purpose of this paper is to investigate the tradeoffs between efficiency and flexibility in production processes involve a cross-trained workforce. The study quantifies production losses that stem from worker learning and forgetting in a cross-training environment.Design/methodology/approach– The paper examines multiple levels of cross-training in the context of several workplace factors including absenteeism, turnover, process change frequency, and process change magnitude using discrete event simulation. The parameters of the simulation model are informed by data from a operating manufacturing system.Findings– Results suggest that productivity gains obtained from a moderate level of cross-training (e.g. two tasks) can outweigh the production losses from additional training, and that adding further cross-training beyond this may negatively affect system performance.Originality/value– Production systems exist in an environment of process change and competitive pressure. Cross-training is an often-used operational technology for managing process knowledge in an environment of change as well as providing employees with a richer work environment. While the potential benefits of enriching the workplace experience include greater motivation, less boredom and fatigue, greater task vigilance, and other effects generally regarded as beneficial, productivity losses brought about by training and retraining disruptions associated with cross-training have not been examined as widely.

Allocating flexible servers in serial systems with switching costs

Consider a firm that operates a make-to-order serial production system and employs a cross-trained workforce. We model such a firm as a tandem queuing system in which flexible servers can be allocated across stations, and assume that a switching cost is charged when servers move between stations. We show that even in the two-station two-server case the optimal policy follows a complex state-dependent structure that may be difficult to implement in practice. We propose three alternate heuristic policies and assess their performance. We show that a simpler policy which only moves one server can achieve close to optimal results.

Development and application of a worker assignment model to evaluate a lean manufacturing cell

In lean manufacturing environments, cross-training is often used to achieve multi-skilling in order to increase flexibility in meeting fluctuating demand, to create a shared sense of responsibility, and to balance workload between cross-trained workers. This paper presents a model that assigns workers to tasks within a lean manufacturing cell while minimizing net present cost. In determining how to assign workers to tasks, the model addresses production requirements to meet customer demand, skill depth requirements for tasks, varying quality levels based on skill depth, and job rotation to retain skills for a cross-trained workforce. The model generates an assignment of workers to tasks and determines the training necessary for workers to meet skill requirements for tasks and customer demand. While the model can be used in a number of ways, in this paper it is used to generate a worker assignment schedule for cross-trained workers in a dedicated lean manufacturing cell in an electronics assembly plant and to evaluate the effect of increased cross-training on the cell. The resulting worker assignment schedules for the current state and several alternative scenarios for the cell are evaluated using cost results from the optimization model and from a simulation model to assess additional performance metrics. These results demonstrate the usefulness of the worker assignment model and indicate that moderate increases from current cross-training levels are not beneficial for this cell.

An analysis of heuristics for the parallel-machine flexible-resource scheduling problem

We consider the parallel-machine flexible-resource scheduling (PMFRS) problem in which a set of jobs must be scheduled over a set of parallel machines, where the processing time of each job is a function of the amount of allocated resource. Resource flexibility provides the capability to dynamically reassign a renewable resource across machines to break processing bottlenecks and improve system performance as measured by schedule makespan. The PMFRS problem has many important applications, including production scheduling of manufacturing cells where a cross-trained work force can be dynamically reallocated among cells. The problem is also NP-hard, motivating the development of effective heuristics that approximately determine the allocation of resource to jobs, the sequence of jobs on each machine, and the associated job start times that minimize system makespan. This paper explores heuristics for the PMFRS problem, and in particular the application of tabu-search methodology to this problem setting. We review an existing heuristic (SBH), define two tabu-search heuristics, and discuss extensive computational experience with the procedures. The computational results indicate that the heuristics are effective in obtaining approximate solutions to the PMFRS problem. In particular, the approach that uses tabu-search methodology in tandem with SBH consistently yields high-quality solutions with modest computational effort.

Labour flexibility and staffing levels in a dual-resource constrained job shop

In many manufacturing organizations, increasing process flexibility is becoming more important while the reliance on product cost to measure manufacturing performance is being lessened. As a result, companies are placing more emphasis on developing a cross-trained workforce in an effort to improve the flexibility of their operations. Having a cross-trained workforce allows managers to move workers around to adjust to temporary overloads in the shop. Another approach to increasing process flexibility is through the addition of labour to create a capacity buffer. Adding more labour improves flexibility since it reduces the average utilization in the shop thereby reducing the possibility of any overload occurring in the first place. This paper compares the benefits realized by the development of a multi-skilled workforce with the benefits realized by additional workforce staffing. Both strategies exhibit improvement in the simulation of a hypothetical dual resource constrained hybrid job-shop. Results sugges...