Missing performance management and measurement aspects in performance-based contracting

Abstract

Purpose Performance-based contracting (PBC) is a business model for the adaptive and innovative delivery of product-service systems. In PBC, the provider is paid according to the service performance with the aim of providing monetary incentives to safeguard possible outcomes as much as possible for the PBC customer. Performance measurement and its management are crucial for PBC success and, in particular, for the pay-for-performance link. However, the literature on PBC performance management is rather sparse, and there has been no systematic review on the topic. Thus, the purpose of this paper is to fill that gap and to present a comprehensive and systematic review of performance measurement and management in the PBC context. Design/methodology/approach The paper builds on a literature review based on a sample of 102 subject-relevant articles from academic journals. The content analysis follows a two-step procedure. First, the articles are coded following a process-based research framework. Second, the content of each process step is assessed in a qualitative text analysis. Findings The results show a surprising scarcity of papers that explicitly address performance management topics in the context of PBC. Only the topics of performance specification and performance indicators are broadly addressed, whereas in all of the other areas, e.g., strategic alignment, data capture and reporting, only limited specific findings could be found. Research limitations/implications The paper concludes that future research on performance management in PBC should expand its theoretical framework and empirical efforts in four specific proposed directions. Originality/value The paper provides an up-to-date review that is focused on performance management and measurement in the emerging context of PBC.

Analyzing the effect of road performance indicators on penalties for late fulfillment in road service levels

“…The results of the analysis of PLRSL in the research sites can be concluded that the following indicators are the determining factors for the success of PLRSL: (1) community reports/complaints, which are statistically significant at 91.6%; (2) response time, which is statistically significant at 90.6%; (3) the value of contract, which is statistically significant at 88.4%; (4) the length of the road that does not meet the road performance indicators/defect, which is statistically significant at 84.0%; and (5) statistically significant traffic volume at 80.2%. Performance measurement and the selection of appropriate indicators are crucial for the successful establishment and implementation of a Performance-Based Maintenance Contract since performance indicators and objectives direct contractors in obtaining the desired maintenance results [37]. In addition, Tahan et al [38] emphasized the importance of developing suitable metrics for evaluating performance-based maintenance contracts.…”

The impact of monetary incentives and regulatory focus on worker productivity and learning in order picking

“…To enhance the performance of warehouse workers, managers often deploy monetary incentive schemes (Glas et al. , 2018).…”

“…To enhance the performance of warehouse workers, managers often deploy monetary incentive schemes (Glas et al, 2018). Warehouse managers confirm that workforce turnover, lack of qualified workforce, high peak periods and the use of temporary workforce is forcing them to invest in incentive plans to improve resilience and productivity.…”

Laboratory Performance Modeling using Petri Nets in National Standardization Agency in Indonesia

“…The early observation is conducted to capture the process performance from 32 testing stations in the PTL by using the Colored Petri Net (CPN) tools to model and simulate the business process. The use of CPN tools allows the products/services providers to create a compact representation of states, actions, and events of the modeled system (Glas et al, 2016;Mukhlash et al, 2018). The early result shows that some stations have work-in-progress (WIP) higher than the others and the oven testing station has the highest WIP performance.…”

“…The dominant use of some stations generates a higher level of WIP because they perform the testing methods for more product types than others. As a consequence, the other testing stations have to wait accordingly to perform which requires workflow evaluation of the current scheduling strategy (Glas et al, 2016). By the use of CPN, the shortest flow processing time (SFPT) scheduling algorithm is developed in accordance to challenge the current first-come-first-served (FCFS) scheduling strategy to minimize maximum WIP in the complex stations.…”