DATA AND ANALYSIS
practice . This isn ’ t efficient , nor is it often accurate , and inaccuracy can cripple the power that effective data analysis and governance can wield .
“ Historically , the distribution and fulfillment sector has been slow to recognise the importance of operational data for driving continuous DC performance improvements , increasing system reliability , and transitioning to predictive maintenance programs ,” Blair says . “ But in today ’ s environment , many DC operations are accelerating their digital transformations by implementing connected , internet of things ( IoT ) infrastructures and leveraging the wealth of operational data found in equipment control systems .”
Any system that relies on data for its efficacy is limited by a handful of common factors , namely the size and scope of the dataset , how ‘ clean ’ or accurate the data is , and how recent the data was collected - real-time being the gold standard here .
“ With data collection tools like computerised maintenance management systems ( CMMS ), logistics professionals can streamline workload and staffing projections based on data from other installations and recommended service intervals ,” Blair says . “ Furthermore , integration with original equipment manufacturer ( OEM ) parts databases enables automated stock replenishment , ensuring parts availability without storing excess inventory .
“ Honeywell Intelligrated ’ s CMMS also supports multi-site implementations but adds an extra layer of complexity due to the importance of a scalable , consistent
90 April 2021