How can you use data analysis to identify process inefficiencies?

Data Analysis: Key to Efficiency in Manufacturing Operations - Track Production Data: Monitor machine output and employee productivity for patterns and anomalies. - Benchmarking: Compare your data with industry standards to identify gaps. - Statistical Tools: Utilize Six Sigma and Lean methodologies for detailed data analysis. - Continuous Monitoring: Regularly review processes to maintain efficiency. "The goal is to turn data into information, and information into insight." – Carly Fiorina #DataDrivenManufacturing #ProcessOptimization #OperationalExcellence #ContinuousImprovement I'd love to hear how data analysis has improved your manufacturing operations. Share your stories and let's learn from each other!

Look for dips in production, specs, and revenues. Conversely, look for spikes in costs, breakdowns, environmental emissions, personnel injuries, repairs, etc. Find correlations between the two and the plant operating parameters. Identify preventable dips or spikes and risk rank them. Start with highest risk/opportunity and implement engineering controls to mitigate new risks.

A análise de dados identifica ineficiências ao examinar métricas-chave, como tempo de ciclo, erros e uso de recursos. Utilizando ferramentas estatísticas e de visualiza??o, é possível mapear fluxos, encontrar causas raiz e comparar desempenho com benchmarks. Essa abordagem permite interven??es proativas, previs?o de problemas futuros e melhoria contínua através de ajustes iterativos.

Start with KPIs, if you don't have any. It can be throughput, cost, energy consumption, profit per hour etc. Calculate those and store in your database. Then check the trends. How does the energy consumption increase during a specific operation? Does it change linearly or do you have anomalies? This will help you finding the clues about the inefficiencies. Next, which is often not utilized, is process models. Develop well enough process models to run different cases. Try to optimize the process models against quality/cost/throughput constraints. This is the study that will help you calculating the losses and potential benefits. Then these can be conveyed to the other stakeholders for discussions of process modifications or invesments.

In my experience identifying the root cause of the problem is half the battle. Often times the first issue found is a symptom of another concern which needs to be addressed in order to resolve the issue entirely. Make sure you have a clear understanding of the problem before attacking the issue

In my experience, I've collected time duration data in 2 ways: 1) by asking mechanics for time estimates, or 2) by doing time studies. In a time study, a work order is timed from start to finish capturing all Value-Added and Non Value-Added activity. At Boeing, an Excel template was used where clicking the start button will signal the beginning of the next activity, recording the end time of the previous activity and the start time of the following activity. All times have to be categorized as Value-Added time or Non Value-Added time. A lot of data analytics can be performed including a waterfall diagram showing losses from total available production time to effective time. Another good chart is a pie chart and a stacked column chart.

I agree with the process as outlined in the article. It is important, though, to include colleagues in the process, in other words, this should be a team effort. The value of shared knowledge and insights should never be underestimated.