Joint condition-based maintenance and condition-based production optimization

Developments in sensor equipment and the Internet of Things increasingly allow production facilities to be monitored and controlled remotely and in real-time. Organizations can exploit these opportunities to reduce costs and improve reliability by employing condition-based maintenance (CBM) policies. Another recently proposed option is to adopt condition-based production (CBP) policies that control the deterioration of equipment by dynamically adapting the production rate. This study compares their performance and introduces a fully dynamic condition-based maintenance and production (CBMP) policy that integrates both policies. Numerical results show that their cost-effectiveness strongly depends on system characteristics such as the planning time for maintenance, the cost of corrective maintenance, and the rate and volatility of the deterioration process. Integrating condition-based production decisions into a condition-based maintenance policy substantially reduces the failure risk, while fewer maintenance actions are performed. Interestingly, in some situations, the combination of condition-dependent production and maintenance even yields higher cost savings than the sum of their separate cost savings. Moreover, particularly condition-based production is able to cope with incorrect specifications of the deterioration process. Overall, there is much to be gained by making the production rate condition dependent, also, and sometimes even more so, if maintenance is already condition-based

Joint Condition-based Maintenance and Condition-based Production Optimization

Developments in sensor equipment and the Internet of Things increasingly allow production facilities to be monitored and controlled remotely and in real-time. Organizations can exploit these opportunities to reduce costs by employing condition-based maintenance (CBM) policies. Another recently proposed option is to adopt condition-based production (CBP) policies that control the deterioration of equipment remotely and in real-time by dynamically adapting the production rate. This study compares their relative performance and introduces a fully dynamic condition-based maintenance and production (CBMP) policy that integrates both policies. Numerical results show that the cost-effectiveness of the policies strongly depends on system characteristics such as the planning time for maintenance, the cost of corrective maintenance, and the rate and volatility of the deterioration process. Integrating condition-based production decisions into a condition-based maintenance policy substantially reduces the failure risk, while fewer maintenance actions are performed. Interestingly, in some situations, the combination of condition-dependent production and maintenance even yields higher cost savings than the sum of their separate cost savings. Moreover, particularly condition-based production is able to cope with incorrect specifications of the deterioration process. Overall, there is much to be gained by making the production rate condition-dependent, also, and sometimes even more so, if maintenance is already condition-based. These insights provide managerial guidance in selecting CBM, CBP, or the fully flexible CBMP policy

Condition-Based Production Planning:Adjusting Production Rates to Balance Output and Failure Risk

Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production rate, implying that the equipment's deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront. Academic/Practical Relevance: Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision. Methodology: We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes. Results: The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output. Managerial Implications: Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration

Control of manufacturing systems with a two-value, production-dependent failure rate

In this paper we consider a failure prone, single machine, single part-type, limited inventory, manufacturing system subject to a non-homogeneous Markov failure/repair process with the failure rate depending on the production rate through a two-value function. The problem is to minimize a cost function which includes a penalty for waiting customers. We derive and prove the optimality of a policy which depends on a threshold but is not the so-called hedging point policy, optimal for the Markov homogeneous case