OEE as Your New ROI standard in Pharma Manufacturing OEE as Your New ROI standard in Pharma Manufacturing Olga-Maria Plessa Product Owner / Lead Consultant Return on Investment (ROI) has traditionally been the main driver for investments worldwide. Executives in pharmaceutical manufacturing have been utilizing ROI to evaluate and justify investment decisions. But how can the ROI of pharma manufacturing capital investments be calculated? How can a manufacturing analytics system be integrated into financial analysis to attract the CEO’s attention? OEE (Overall Equipment Effectiveness) has been used so far primarily as a manufacturing Key Performance Indicator (KPI). With OEE, loss analysis can be performed paving the way for root causes to be detected, and production improvement actions to be taken, but can OEE also be used to provide financial insights that drive investment decisions? What is Overall Equipment Effectiveness? Overall Equipment Effectiveness or OEE is a ratio of how well a pharmaceutical manufacturing facility is utilized compared to its scheduled output. It is a conventional manufacturing metric that measures the volume and quality of production compared to the total production. OEE takes into account various key elements of the manufacturing process that reveal the unscheduled downtime, the operating time frame, and the relative impact of minor stops and rejections. How is Pharma OEE Calculated? OEE is measured based on availability, performance, and quality. Available time means uptime. This is the total scheduled production duration minus all planned and unplanned interruptions during the manufacturing process. Performance measures the effect of the average actual speed versus the target / validated production speed of a product. Quality measures how many items passed quality testing the first time compared with the overall number of products produced. In other words, it is a measurement of productivity that calculates the efficiency at which equipment produces finished goods by considering the three fundamental calculations: Overall Equipment Effectiveness (OEE) Availability [A] is the percentage of planned production time that can be allocated for manufacturing or, in other words, the percentage of scheduled time that the production line is available to operate versus the total scheduled time. The losses in this category are unplanned and planned stops of all kinds. Performance [P] is the actual throughput of the line during the time it runs, divided by the maximum throughput that it would have achieved by running at the validated speed. We categorize losses as rate loss, which includes reduced speed either under normal operation or during the ramp-down and ramp-up phases associated with every stop, leading to slow cycles. Quality [Q] is the quantity of actual production output that meets customer specifications and has no defects (good units) on its first pass through a line (first pass yield) compared to the total produced products, including rejections and reworks. You can find more information here on what is OEE and why it is important to pharmaceutical manufacturing. How can OEE Increase Financial Performance? Let us use as an example a Solid Dosage Forms (SDF) contract manufacturing organization (CMO) that has a total of 7 packaging lines that produce on average 300 cartons per minute which equals 300 cartons x 60 minutes = 18.000 cartons/hour. And let us assume that each secondary packaging line operates 5 days per week for 24 hours per day (3 working shifts). This would mean that each packaging line operates 5 days x 24 hours = 120 hours/week and, assuming 50 working weeks per year, for 6.000 hours/year. Now, to calculate the output produced per year, we will multiply the machine runtime in hours with the units produced per hour times the overall equipment effectiveness (OEE) of the packaging line. Output Produced per Year = Machine Runtime in Hours x Units Produced per Hour x Current OEE Assuming the current OEE of the secondary packaging line is 35%, the packaged cartons in a year would be 6.000 x 18.000 x 35% = 37.800.000 cartons/year. In the event of a 10% increase in OEE (38.5% OEE), the produced output in a year would now be 6.000 x 18.000 x 38,5% = 41.580.000 cartons/year. Gained Output = Increased Output – Initial Output 41.580.000 – 37.800.000 = 3.780.000 cartons So a 10% increase in OEE would result in 3.780.000 additional cartons per packaging line, so a total of 3.780.000 cartons per line x 7 lines = 26.460.000 additional cartons. Total Additional Profit = Gained Output x Profit per unit Assuming an estimated 0.50€ profit per carton, then a 10% increase in OEE would result in 24.460.000 cartons x 0.5€ profit per carton = 13.230.000,00€ in incremental profitability. Profitability linked to OEE manufacturing KPI How to Achieve an Increase in OEE? To begin, the current OEE of the business should be known to establish a baseline. Then, OEE metrics and loss analysis need to be calculated in real-time and displayed on the factory floor, among other production data and KPIs, to optimize decision-making. Although measuring the productivity alone is not sufficient to achieve improvement, it is the absolutely necessary first step towards OEE improvement, as it points problem-solving actions to the right direction and shifts focus on the appropriate areas in order to optimize pharmaceutical manufacturing operations. The journey towards a continuous improvement culture starts with the first step of measuring and mapping the current-state Therefore, Manufacturing Analytics & OEE solutions are critical in identifying opportunities, track the actual speed of the line and other site data in real-time by utilizing Industrial Internet of Things (IIoT)-based edge devices. Monitoring and assessing machine data will drive operational efficiencies and better performance, deliver immediate improvements, and create financial gains. The information provided by an IIoT-based Manufacturing Analytics & OEE application can help to: Identify machine bottlenecks and root causes for all machine stops. Provide greater operator awareness about line performance and instant visibility to shift performance for line supervisors. Observe production cycles and track their time, output, and performance. Compare different batches and Stock Keeping Units (SKUs) and gain meaningful insights. Understand the occurrence of scrap, rework, and first-pass yield and

OEE Fundamentals OEE Fundamentals Olga-Maria Plessa Product Owner / Lead Consultant What is OEE and why is it important in pharmaceutical manufacturing? Fundamentals of OEE= availability, performance and quality Overall Equipment Effectiveness (OEE) Overview In an ideal pharmaceutical or biotech factory, equipment would operate 100% of the available time, at 100% capacity based on machines’ validated performance, and with an output of 100% quality products. In reality however, there are losses and stops during production that can be caused by several parameters such as machine failures or underperformance or quality issues, and those parameters can be linked to financial KPIs directly impacting profitability. What is OEE For? With the Overall equipment effectiveness (OEE) KPI, pharmaceutical manufacturers can evaluate how effectively manufacturing operations are executed. Through OEE tool, root causes can be detected and action can be decided to analyze and improve processes. By using machine sensors, OEE data collection can be done in real-time by providing an efficient way to monitor process-related downtimes and enable immediate improvement in efficiency and machine performance. OEE Calculation OEE or Overall Equipment Effectiveness is a ratio of how much a pharmaceutical manufacturing facility is utilized for, compared to the full output it was designed for. In other words, it is a measurement of productivity that calculates the efficiency at which equipment can produce finished goods, by considering the three fundamentals of OEE: OEE = Availability x Performance x Quality OEE Fundamentals Availability, Performance and Quality Availability of OEE Availability is the uptime, meaning the total time the pharmaceutical manufacturing facility is available for, minus maintenance and unscheduled downtimes. Availability is the percentage of planned production time that can be allocated for manufacturing or in other words is the percentage of scheduled time that the operation is available to operate. The losses in this category are unplanned and planned stops. Unplanned stops consist of the time where equipment is scheduled to produce but is not running due to unplanned occurrences such as equipment breakdowns, unplanned maintenance or materials shortage. On the other hand, planned stops take into account the time where equipment is scheduled to produce but is not running due to planned occurrences such as changeover, tooling adjustment, cleaning, planned maintenance and quality inspections. Performance of OEE Performance is the actual throughput of the plant during the time it runs, compared to the maximum throughput that it could have achieved running at the validated speed. We categorize losses as micro stops and slow cycles. Micro stops are the time when the equipment stops for a very short period (typically 1 – 2 minutes or less) with the operator typically resolving the issue. Some examples of micro stops are material jams, incorrect settings on the machine, misaligned or blocked sensors, equipment design issues, and periodic quick cleaning due to misfeeds. Slow cycles are the time when equipment runs slower than the validated speed. This might be a consequence of not properly cleaned equipment, poor machine lubrication, substandard materials, poor environmental conditions, wrong settings/adjustment in PLC or other human factors such as training, experience and internal processes. Quality of OEE Quality is the number of actual production output, which meets customer specifications and has no defect (good units) on its first pass through a line (first pass yield) compared to total products that are being produced. Quality can be lost in two ways – by producing rejects, or start-up rejects that exist at the beginning of any given process. Production rejects are defects that we have produced during stable production – including those that we can rework as OEE measures quality based on Right First Time”. Examples include under or overweight bags, label problems, chemical or physical conformity issues, broken packaging and more. Start-up rejects are defects that we have produced from start-up until stable production. They can occur after any equipment start-up. However, most of the times shop floor teams tend to track them after changeovers. Examples include suboptimal changeovers, equipment that needs “warm-up” cycles, or equipment that inherently creates waste after start-up. Each of the OEE parameters is furtherly broken into subcategories to identify the root-cause of downtimes and plan on how to improve or eliminate them, if possible. The Value of Overall Equipment Effectiveness (OEE) Calculating OEE in Pharmaceutical Manufacturing is critical to profitability! Overall equipment effectiveness (OEE) monitoring and optimization is critical as the market leading pharmaceutical manufacturers are 53% faster in getting product to the market.OEE data helps in data-driven decisions for: Investments in operators training. Investments in manufacturing infrastructure. Enabling Continuous Improvement and Lean Manufacturing value. Calculating ROI and profit gain from process and infrastructure improvement. Comparing Investments through accurate ROI data. Minimizing efforts to follow up production performance. Digitizing plant and getting quick and reliable data. Increasing and securing manufacturing output. Getting full control over operations and machine performance. READY TO AUGMENT YOUR SHOP FLOOR OPERATIONS? READY TO AUGMENT YOUR SHOP FLOOR OPERATIONS? Vimachem Manufacturing Analytics – OEE The Manufacturing Analytics – OEE module is an intelligent Pharma OEE Cloud solution that allows you to collect, store and visualize data across your site / enterprise and apply AI algorithms to optimize production efficiency and product quality. Get started with real-time manufacturing analytics today! Book a Demo