Manufacturers who wish to remain competitive in a global economy must overcome many challenges. Customers demand faster lead times, greater product customization, and certifiable quality and collaborative information sharing, as well as lower price.
Automated equipment can help, but as equipment providers create more advanced machinery, manufacturers often struggle to realize measurable and sustainable gain. In fact, manufacturers often discover that automation has become a means of producing nonconforming product faster than ever before. One reason this occurs is the inability to manage the information sent to and received from the equipment.
Manufacturing execution systems (MES) address several core functions specifically aimed at solving these challenges with data collection playing an integral role in support of the core MES functions. Data collection software can provide measurable and sustainable improvements in quality and performance in many applications.
Successful data collection begins with managing operators, lines, materials and recipes used by the operation. Product/process data management allows the data collected to be associated with what is being produced, where it is being produced and who is producing it.
Software recipes for manufacturing, like those used in packaging operations, can include information such as setpoint, upper and lower reject limits, and upper and lower control limits. By having product data management capabilities, data collection software can automatically download recipe settings to the equipment on change of each recipe, eliminating operator setup error. Product data management also can reduce the time and effort needed to change product recipes and disseminate those changes to the production floor-a key to meeting quality systems objectives.
Data collection can be used to record any relevant change or action done on the production floor. Examples of process auditing include operator login and logout events used to relate production history to specific operators, batch or shift changeover events used to relate production history to specific customer shipments or time period, input material changeover events used to create product genealogy reports and to associate production problems back to specific materials in question, line state changes used to generate production analysis reports, and manual setpoint changes used to identify who changed the setpoint, what the setpoint was changed to and why a setpoint change was needed.
Process auditing gives production management a valuable and cost-effective tool to identify the cause of production problems.
A key objective to improving production operations is the continual reduction in the time taken to carry out corrective action when problems occur. Data collection plays a vital role in providing the tools needed to allow operators to react as quickly as possible to abnormal process conditions. For example, when data collection can be linked to the packaging equipment, 100% inspection of packages filled can be accomplished.
Manufacturing companies face two significant issues in packaging-overfilling and underfilling. Overfilling packages produces immediate loss of revenue because the amount of overfill represents lost production yield.
Underfilling packages produces two long-term issues. First, many companies face heavy fines for shipping consumer products that are not filled as labeled. Second, consumer confidence in the item diminishes when consumers feel they are shorted product.
In scenarios where the specification limits can be linked to the packaging equipment, the first out-of-specification package can be immediately routed along a nonconforming path, preventing the package from being shipped to the customer. Additionally, the operator can immediately be notified and the line can be stopped periodically to identify and correct the cause of the problem. The result is immediate notification and faster response to the problem, and immediate increase in time available to make acceptable, as well as product and a sharp reductions in the number of out-of-spec products to be reworked
Data collection can be a fast and effective way to build the necessary data to calculate and plot actual vs. statistical control limits. Data collection software that can plot actual production against control limits can be used for real-time statistical quality control trending and alarming. Based on these criteria, alarms can signal any out-of-control condition so that corrective action can be taken to bring the process back in control. One possibility is the collection of causes assigned to an out-of-control event and the collection of the specific corrective action taken. A history of causes and corrective actions can be used to provide timely and consistent assistance to operators. Once a process has become stable and capable, further adjustments to the recipe can be used to further increase yield. Real-time trending is an effective way to implement quality improvement initiatives such as Six Sigma.
Best manufacturing practices suggest that downstream processes should never become bottlenecks in the overall manufacturing process. By establishing line state and then collecting line state transition events, data collection provides a means for management to review historical production to ensure an acceptable productivity level. As an example, assume a production line is in one of three states: down, idle or busy. Down represents periods when the line has no backlog of production orders or that the operation shuts down-for example, nights or weekends. Idle represents periods when the line has a backlog of orders and is not shut down, however, no order is currently being filled. Busy is periods when the line is filling a production order.
Periodic utilization can be calculated over any suitable production period by dividing busy time by the total of idle time plus busy time. Additional collection of downtime cause, any time the line state changes to down, can provide an assessment of the reasons for lost production time. Based on pre-established production rates, similar statistics can be derived for production efficiency, that is, the actual acceptable production rate as a fraction of standard production rate. Finally, multiplying utilization times efficiency yields an overall productivity value that can be used to monitor and trend production output.
Once established, real-time productivity can be monitored by management as an indicator of whether a line is becoming a bottleneck in the overall operation. Calculating these results by line, material, recipe or operator can provide insight as to which process, product or operator needs the most attention from management.
Data collection software can provide a means to deliver customers certificate of analysis reports with each product shipment. Reports can be used to show the customer how to convert data to information and then act on that information. For example, in the automotive industry, a supply chain improvement initiative has required a customer to provide a complete list of weights for each package shipped so that each shipment received is pre-
certified for weight prior to the material being used in a batch recipe. The result is the elimination of preshipment testing and the ability to base the recipe on the number of packages to use vs. actual weight.
Installing data collection software helps managers improve production downtime while increasing productivity. This combination increases profits, and in the case of some software products, equates to a relatively quick return on investment.
Downtime is a common problem shared by most plant managers, but it can be reduced in several ways by using data collection software. Monitoring downtime can help managers detect and address chronic problems that occur in the production process. The statistics received during a period of time can make problem areas become visible in the reports generated by the software so they can be improved. Along with this, the simple fact that shop-floor employees are aware that the production line is constantly being monitored leads to better overall employee productivity.
A variety of data collection software is available to work with different types of equipment in different industries. These software systems can be put into three basic categories: simple, basic data collection with limited reporting options and easy usage; advanced, software with larger feature sets, reporting capabilities, data analysis and ease of use; and highly advanced, technical software for detailed statistical analysis and usually requiring extensive training.
There are many features to look for in today's data collection software. The software should be easy to use or navigate. Some systems are user friendly and intuitive, requiring little training. This reduces employee downtime, allowing users to get back to work with minimal interruption, along with reducing the costs associated with extended training. User-friendliness is important should the primary software user decide to leave and take all the system training with them.
Some software is easy to install and configure, without the need for extensive configuration, which saves time and money. The system should interface directly with a recognized database to easily work with the data.
Data needs to be shared throughout the enterprise, allowing all relevant operators and managers to be aware of the current manufacturing status. E-mail reports are a convenient way to instantly send report statistics to management.
The software should be flexible enough to easily connect multiple pieces of machinery to one computer. Basic systems are useful, but usually print reports directly on the shop floor, leading to mounds of paper, or lost reports, because of printer breakdown. Reports sent to a computer in a manager's office allow continuous access to information. More powerful systems are capable of analyzing and reporting on one, or hundreds, of combined production runs in real-time with just a mouse click. Many new systems can be customized to the manufacturer's specific needs to further enhance usability.
When choosing a data collection software system, first decide what objectives need to be met. Make sure the chosen software provides the correct features to accomplish those objectives.
Informed with accurate and timely factory floor information, managers and employees can rely on production data to make quicker, more informed decisions about their manufacturing process. Used effectively, the correct data collection software has many positive impacts on manufacturing and increases bottom-line company revenue. Q
• Data collection can be used to record any relevant change or action performed on the production floor.
• Data collection provides a means for management to review historical production to ensure an acceptable productivity level.
• Product data management can reduce the time and effort needed to change product recipes and disseminate those changes to the production floor.
• Installing data collection software helps managers improve production downtime while increasing productivity.