Companies face a number of issues when planning and executing their routine maintenance programs:
BCG has helped these companies address such issues by taking advantage of big data sets, natural language processing tools, and failure-pattern recognition in order to adjust preventive maintenance schedules and operator routine duties, as well as to optimize maintenance work orders.
Planning site maintenance is complex and often consumes significant overhead. Digital technology can improve schedule quality and execution while reducing the administrative burden on planners and support groups.
BCG has developed Cadence, a schedule optimization and workflow management system that supports maintenance teams throughout the maintenance cycle. Our approach enables teams to increase their schedule efficiency and adherence, reduce maintenance backlogs, and improve plant performance.
Equipment failure can lead to heavy maintenance costs and production losses. But with machine learning and supervised training techniques on big data sets, teams can predict equipment failure in advance.
BCG has leveraged these tools to help companies drive behavioral change by moving from fixed failure-prevention models to a dynamic variable model in which proactive interventions take place before failures occur.