# Smart Manufacturing Application in Precision Manufacturing
## 2 Methodology
### Strategy Stage  
The business realized that to stay competitive in the precision manufacturing industry, it needed to improve productivity and reduce waste. However, the digital and smart industry readiness was immature. In particular, there was no connectivity to the production machines on the factory floor; therefore, the performance or the production process was not visible. Additionally, there was no digital system in place to manage production orders, so the processes were carried out manual and fully dependent upon the individual skills of the production management team. This made measuring production using KPIs difficult as they were calculated from manual data, which were subject to human error and out of date because they were assessed after the event. This made the factory and workforce performance less efficient. As a result, more downtime waste (i.e., lost production time) was produced, which impacted the cost of production as well as limited the production capacity, agility, resilience, and growth.  
To achieve the business objectives, it was required to build a smart production management ecosystem. Improvements to the IT infrastructure and secure connectivity of the production machines were initially required to have full visibility of what was happening on the factory floor. The exchange of relevant production information required automation so information could be shared with the enterprise IT system in the office for the real-time management of the production process and for daily repetitive tasks to be eliminated. Business KPIs were also required to be digitally reported in real time to rapidly manage deviation and adapt to changing needs. The strategy was to adopt industry  
Figure 2: Traditional ISA-95 framework diagram of manufacturing systems’ integration (authors’ creation).  
4.0 concepts and technologies to transform into smart manufacturing using off-the-shelf available technologies for rapid implementation.  
Improvements to productivity and reductions in downtime waste can be achieved by automating the repetitive daily tasks and by having access to real-time production information to provide actionable insights to quickly determine operational bottlenecks. Improvements to KPI reporting can be achieved by improving their collection method and the speed they are reported to the relevant stakeholders so actions can be taken rapidly.  
Improvements to manufacturing capabilities can be achieved by better monitoring the asset's health as well as the performance and use of the production machines in the factory so that unscheduled downtime can be reduced, and production time management can be improved. As a result, a market advantage can be achieved for a smart production line and speed to market can be increased by bringing improvements quickly to production planning and job scheduling.  
Figure 3a shows the ISA-95 manufacturing system integration architecture of the base state of the factory (before smart manufacturing implementation). Figure 3b shows the connectivity and integration architecture between the factory floor and office. In the factory layer, fifteen CNC manufacturing Mazak machines were networked via ethernet cables to the network mainly for the purpose of storing and transferring CNC programs across the network. The grinder machine (Machine 16) was not connected to the network because of the complexity involved due to it being a legacy machine with the only Fanuc controller in the factory, which required a separate computer interface for communication and a configuration setup, neither of which were available at the time. The manufacturing IT system in the network was an enterprise resource planning (ERP) system. The ERP system in the network was directly connected to multiple PCs on the factory floor that are used as a human-machine interface (HMI) for production reporting by the operators of the machines.  
Figure 3: _Cont._