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September, 2017 Production Saving Time: Collecting Data


to Improve Manufacturing By Jeff Taylor, Regional Sales Manager, Kolver USA


O


ne step every manufacturer can take to help the bottom line is to im- prove the manufacturing process. Starting with a solution-based men- tality or “putting out fires as they appear” neglects examining the


process itself. A manager might begin by implementing a program based on a school of thought — lean manufacturing, key performance indicators (KPIs), Kaizen, poka-yoke, etc. This is done in the hopes that an improved mindset will eventually address all of the problems. To significantly improve a process, to see all of its flaws and to take ac-


tion in the most meaningful way, a plant manager must start with visibility in the manufacturing process. Access to live data collection provides that vis- ibility at a lower recurring cost than does manual collection. Often, instincts fail at ad hoc prioritization, resulting in some beautifully-crafted solutions to unimportant or even non-existent problems. A timestamp can pinpoint an exact moment at which a single event occurs,


but when combined with larger data sets will tell definitive stories about the en- tire process. Time can be examined in a snapshot to make sure that an individ- ual task, such as a pickup, a stamp, or a weld, was completed correctly, to a wholistic view of the current production capacity of the existing process.


Studying Time Examples of the crucial nature of small differences in time can be found


across the factory and even on the workbench. The difference in 0.1 seconds in a high-speed screw-capping operation can mean the difference between a viable and a spoiled product. The cooling time of heated products, such as sol- der paste and plastics, is critical to the material properties and the future per- formance of the product. Dr. Micah Eckhardt, IoT lead at Tulip, helps manufacturers end costly


IT’S TIME TO RISE UP AGAINST HIGH HEAT, WARPING AND DEFECTS.


time studies, instead collecting and analyzing data live with the Tulip plat- form. A spinout from MIT, Tulip brings the power of the Industrial IoT and analytics to the shop floor through a platform of apps. “Time studies are crucial to allow process engineers to collect the data


they need to perform their jobs. Since they are expensive to conduct, many manufacturers cannot perform them frequently enough. Without frequent time studies, it is impossible to continually improve processes or identify op- erators in need of training,” says Eckhardt. This problem is further compounded for products or processes that require


customization. Performing time studies for each and every product/process com- bination is infeasible. Tulip’s platform allows engineers to set up continuous time studies. Data is collected through interaction with Tulip’s manufacturing apps. Process engineers can visualize production data through the built-in manufac- turing analytics engine, segmenting the step times by a variety of fields, includ- ing operator, product variety, and time of day.


Productivity Benefits What benefits are reaped from such an intense focus on quality control?


Quality control reduces scrap waste and has the added benefit of improving customer faith in the product. In 2011, Alcoa Power and Propulsions set out to continuously improve their process with a focus on quality control. They began by attacking problems in the factories that had produced the highest levels of scrap, and over the course of a few years, saved millions of dollars. Even in cases where scrap can be prevented by incorporating rework into


the facility, the cost associated with rework can be very high. Rework includes the costs of labor, energy and the cost of losses. In 1999, at Boeing’s Wichita division, rework was the single largest contributor to failure costs, totaling $1.3 million per quarter. By monitoring the beginning and end of each process, insight can be


gained not just into the time it takes for the application to run, but also to see how long the product had to sit idle before continuing on. Mapping these wait times between workstations is an easy way to identify bottlenecks in manu- facturing. When stacks of product build up before they can effectively complete the


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next stage of production, a bottleneck is formed, slowing down the flow of product through assembly. Many bottleneck and starvation issues have their roots in less visible


problems, such as training issues, poorly-designed work instructions, or process starvation. An untrained operator will have step times that are con- siderably longer than trained operators. With data, it is easy to see exactly which steps in a build take the longest and which specific operators require training.


Tulip’s solution gives manufacturers more data. With a granular under-


standing of process and step times a process engineer can maximize produc- tion and improve quality. For example, with data from Tulip’s apps, an engi- neer can determine the root cause of delay in a step that is taking longer than the target time. With this insight, engineers can simplify and redesign specif- ic work steps. Time is only one example of many factors in the manufacturing process


that can be examined to improve productivity. Whether it is collected from a single moment, or on a larger scale, time is often one of the most readily avail- able variables for engineers to extract. This makes it an excellent starting


point for managers and engineers looking to begin a data collection process. Contact: Kolver USA, 8D Industrial Way, Suite 1, Salem, NH 03079


See at SMTAI, Booth 1016


% 603-912-5886 E-mail: kolver@kolverusa.com and Tulip, 561 Windsor Street, B402, Somerville, MA 02143 E-mail: hello@tulip.co Web: www.tulip.co r


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