Connection of short interval control

Important connection of short interval control

Short Interval Control (SIC) is a factory-floor process for driving production improvements during the shift. Each shift is split into short intervals of time, within which plant-floor employees use data to identify and implement improvement actions. These improvement actions may be countermeasures to ongoing or emerging problems, or they may be actions to improve existing production. SIC can be seen as a form of Kaizen, as it encourages teams to work together to achieve regular, incremental improvements to the manufacturing process. A key feature of SIC is the use of real-time production data to guide instantaneous front-line decision making. Teams are trained to collect, analyze, and react to this data in order to drive significant performance improvement. The core principle behind Short Interval Control: the past cannot be changed, but we can learn from it to improve the future.

When AGCO, the world’s largest manufacturer of tractors and third largest supplier of agricultural machinery, decided to build the world’s most modern tractor factory capable of producing 20,000 tractors a year, it knew it had an opportunity to significantly improve upon the performance of its existing factors. AGCO’s factories implemented a Manufacturing Operations Management (MOM) platform which captures real-time production data to enable quick visibility to all production activity with drilldown options to the machine level. A production dashboard provides key performance indicators which are updated continuously for management review. The system automatically captures machine/production states (running, downtime, set-up). AGCO’s system downloads work orders from their ERP system, which are then updated with production data to form a seamless
continuous improvement loop.

With its MOM, AGCO has:

• Documented a doubling of production volume without reducing efficiency, as measured by
fixed cost KPIs (efficiency and productivity) or production time and variable cost per unit,
resulting in a higher profit margin.
• In its first year improved Overall Equipment Effectiveness by 22% in core machining area.
• Projected that assembly line productivity will increase by 25-30% in the first three years.
• With increased transparency improved allocation and utilization of capital invested for
machines.
• Standardized, reliable and objective process performance data such as production times,
setup times, and non-productive times has allowed better management of valuable capital
equipment and product quality.
• Monitoring, improving and accelerating the machine maintenance processes has reduced
downtime leading to a lower operating costs.
• Consistent, automated data acquisition and KPI calculations which provide greater
transparency on the shop-floor in support of waste elimination and performance
improvements – including comparisons across sites.