Novel Taguchi scheme–based DEMATEL methods and DEMATEL method for the principal performance indicators of maintenance in a food processing industry

PurposeKey performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.Design/methodology/approachThe causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.FindingsThe results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.Originality/valueHardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.

Energy—Water Nexus: Integration, Monitoring, KPIs Tools and Research Vision

The relationship between water and energy is a strong one characterized as having integration and coupling as two important features. While energy is responsible for delivering water to the end-users, it needs energy in order to be generated, and water. In this paper, a thorough review is presented regarding the different relationships between water and energy in terms of (i) the significance of the close relationship between water and energy by means of water/energy generation and consumption. Water consumption, water cooling and heating must be taken into account in order to avoid the obstacles related to future use of water for energy generation; (ii) the measuring and monitoring technologies for the energy-water nexus, focusing attention on the variables that are interrelated in the water and energy sectors. In addition, the consequences of finding several parameters, unknown variables and unclear dependencies in measuring of energy usage in the applications of water usage should also be taken into account. Innovative developments including nanotechnology, biotechnology, and wireless networks, as sensor technologies, may resolve the challenges of sensing; (iii) the different key performance indication tools for assessing and quantifying this nexus by analyzing and categorizing recent case studies of the water energy nexus and applicable evaluation methods; and (iv) the different research dimensions conducted on this nexus. Hopefully, this review will contribute to the development of this nexus adding value to the field while reducing duplication efforts.

Integrated Business and Production Process Data Warehousing

Nowadays tracking data from activity checkpoints of unit transactions within an organization’s business processes becomes an important data resource for business analysts and decision-makers to provide essential strategic and tactical business information. In the context of business process-oriented solutions, business-activity monitoring (BAM) architecture has been predicted as a major issue in the near future of the business-intelligence area. On the other hand, there is a huge potential for optimization of processes in today’s industrial manufacturing. Important targets of improvement are production efficiency and product quality. Optimization is a complex task. A plethora of data that stems from numerical control and monitoring systems must be accessed, correlations in the information must be recognized, and rules that lead to improvement must be identified. In this chapter we envision the vertical integration of technical processes and control data with business processes and enterprise resource data. As concrete steps, we derive an activity warehouse model based on BAM requirements. We analyze different perspectives based on the requirements, such as business process management, key performance indication, process and state based-workflow management, and macro- and micro-level data. As a concrete outcome we define a meta-model for business processes with respect to monitoring. The implementation shows that data stored in an activity warehouse is able to efficiently monitor business processes in real-time and provides a better real-time visibility of business processes.