# Smart Manufacturing Application in Precision Manufacturing
## 4 Discussion
### Operational Performance Impact  
The machines' utilization rate improved by 47% over the three-year period from 2020 to 2022. Prior 2020, there were no data available to evaluate the machines' condition status or how they had been utilized. In 2020, the mean machine utilization rate was 37.5%; this increased to 52.5% in 2021 and increased further to 56% in 2022.  
As can be seen from Figure 8b, in the first half of 2020, the machines' utilization rate was low (<45%), and machines' availability was high (>70%), which reflected the reality of the underutilized CNC machines and low productivity in the factory. In late 2020, the machines' availability dropped significantly below 45% due to the impact of COVID-19 on the production orders and the labor resources available. However, in the second half of 2020 and in 2021, the machines' utilization rate increased to 45% and 52% on average, respectively, although the machines' availability stayed low (<45%) due to the limited resources, particularly labor resources during the COVID-19 period. The increase in the machine utilization rate in the second half of 2020 and in 2021 was due to better production time management by the machine operators, which resulted in improvements in productivity, which showed resilience during unstable times. In 2022, machines' availability continued to increase (>50%), as production orders became more sustained, and more people returned to work as COVID-19 restrictions eased. The machines' utilization rate continued to improv to 56% on average, a further improvement from 2021, resulting in more productivity and showing signs of potential growth.  
The factory total downtime cost was reduced by 53% over the three-year period from 2020 to 2022 compared to the base year 2019. As seen in Figure 9a, the total downtime in 2019 was approximately GBP 800 K, the downtime was reduced by 38% in 2020 to GBP 500 K, and dropped by a further 32% in 2021 to GBP 340 K. In 2022, post-COVID-19, the total downtime increased slightly by 12% to GBP 380 K because of a ramp up in orders, particularly in new products of low volume, which required increased planning, testing, and evaluation. It was predicted that the total downtime would drop by 12.5% in 2023 and a further 6.25% in 2024 as the company further embedded a data-driven culture into its operations.  
The reduction in the total downtime over the three-year period gave a total cost saving of GBP 420 K, which may have saved the business. The improvements to the machines' utilization over the three-year period resulted in productivity improvements. As a result, the factory was resilient during a challenging unstable industry period and showed signs of growth during more stable periods.  
The access to live production data provided by the ecosystem of applications that formed the smart production management system in the factory also contributed to the productivity improvements, enabling quick responds and taking actions accordingly. For example, the machines' condition displayed by the Smooth Monitor AX dashboards enabled the maintenance engineer to respond quickly to potential breakdown alarms; the factory efficiency displayed by SMAS dashboards enabled the production manager to monitor production and unplanned downtime to respond quickly to any schedule changes' and the KPIs displayed by the Power BI dashboards enabled company managers to track the financial performance relatively in real time and react immediately when something was wrong.