by Ahmad Al Hanbali, Sajjad Rahimi-Ghahroodi, and Henk Zijm

The MLOG (Optimal Exploitation of Resources in Maintenance Logistics) project, executed jointly by the University of Twente and the University of Qatar, focuses on an integrated planning of resources needed for asset maintenance.

In many Western countries, maintenance and overhaul of capital assets constitute some 15 % of their GDP. Smart sensor and data gathering techniques, as well as advanced decision support systems, are exploited to design integrated logistics support (ILS) systems. In the MLOG project we focus on an integrated planning of resources needed for asset maintenance rather than the piecemeal approach usually considered in the literature and in practice. The goal is to minimize overall resource investments subject to agreed service level constraints. Exact methods are only computationally feasible for very small problems, hence we have developed fast but highly accurate approximation methods. In an actual case study, our results show that integrated planning can achieve an overall cost reduction of up to 27 % without sacrificing the offered service quality, when compared with the common practice solution used in companies. Combining our approach with smart sensor and condition monitoring data (Internet of Things) may further enhance asset availability and hence industrial production both in Qatar and in the Netherlands.

Maintenance logistics has received considerable attention in recent decades owing firstly to the significant investments associated with capital-intensive assets, which in turn require a high operational availability, and secondly to the need to prevent environmental damage – for example, the BP accident in 2010 in the Gulf of Mexico - or safety incidents – for example in medical equipment and aircraft. Unplanned downtime of advanced capital equipment can be extremely expensive; for an average aircraft it is estimated at some S10,000 per hour and for a high-tech lithography system in the semiconductor industry it may amount up to $100,000 per hour. Consequently, unplanned downtime should be prevented as much as possible, for example, by exploiting advanced condition monitoring techniques and preventive maintenance policies, and if they occur, they should be kept as short as possible by using optimal corrective maintenance policies [1]. The latter implies that malfunctioning parts or components causing the system breakdown are immediately replaced by ready-for-use ones, since repair of the complete system on site induces unacceptable long downtimes. Typically, a system failure induces a set of actions as depicted in Figure 1.

Figure 1: In the MLOG project, we are developing fast approximation algorithms to optimize service supply chains, while reducing the overall costs with 27 % in an actual case study.
Figure 1: In the MLOG project, we are developing fast approximation algorithms to optimize service supply chains, while reducing the overall costs with 27 % in an actual case study.

The availability of resources needed for repair (spare parts, service engineers, and tools) in fact defines the operational availability of the complete asset. However, both spare parts and skilled service engineers often require high investments (parts worth $50,000 are no exception for capital-intensive assets), hence there is a trade-off between resource investments and service provided. Much research has been carried out on spare parts management see [2]. So far the planning of resources has largely been performed independently (per resource) while their simultaneous availability is essential to minimize downtimes.

Contribution
As part of this project, we have been studying a service region in which a local spare parts inventory supplies different types of parts. Failures occur randomly and are often due to one malfunctioning part that needs replacement. In addition, a skilled service engineer is needed to complete the repair and replace the defective part. Malfunctioning parts are repaired off-line and subsequently stocked for future use. The focus here is on the integrated, multi-resource approach, for which different service policies are available: (i) complete backlog in case of unavailability of either parts or engineers, (ii) an emergency service fulfillment from an external source in case of unavailability of either parts or engineers, (iii) and the heterogeneous policy with backlogging for one resource and emergency for the other.

The most realistic heterogeneous policy would involve having parts supplied by an emergency service and engineers backlogging. This is due to the long spare parts replenishment time that is often encountered compared with the engineers’ service time. To evaluate such a heterogeneous policy, we have developed accurate methods using Mean Value Analysis and Laplace Transform techniques that can handle practical problems with a high number of different types of parts [3]. Our methods can tackle these problems quickly as opposed to the standard Matrix-Geometric approach, which is only computationally feasible for very small problems. This is due to the detailed description of the system state needed in the standard approach. In addition, we also consider the overall logistic support system optimization, i.e., we determine optimal stock levels and service engineer crew size, such that the average total costs (including spare parts holding costs, hiring cost of service engineers, and emergency costs) are minimized subject to pre-specified service level constraints. For real problems with high number of part types, our methods yield solutions very fast while the total cost error is negligible when compared with an exact method (only verifiable for small problems). The optimization algorithm demonstrated an overall cost reduction of 27% in an actual case study, when compared with the separated optimization, which is common practice in companies. Future research may include advance information based on condition monitoring data (smart sensors, IoT) to move to preventive maintenance, thereby further increasing asset productivity.

MLOG was awarded by the NPRP program, 7th cycle in 2014, and funded by the Qatar National Research Foundation for a period of three years, starting from January 2015.

References:
[1] M.A. Cohen, N. Agrawal, V. Agrawal: “Winning in the aftermarket”, Harvard Business Review, 84(5), p.129, 2006
[2] C. C. Sherbrooke: “Optimal inventory modeling of systems: multi-echelon techniques”, Springer, 2004.
[3] S. R. Ghahroodi et al.: “Integrated Field Service Engineers and Spare parts Planning in Maintenance Logistics”,. under review..

Please contact:
Ahmad Al Hanbali, Sajjad Rahimi Ghahroodi, Henk Zijm
University of Twente, The Netherlands
This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

Next issue: October 2018
Special theme:
Digital Twins
Call for the next issue
Image ERCIM News 105 epub
This issue in ePub format

 
Get the latest issue to your desktop
RSS Feed