A tier-two automotive supplier wanted to use a market dip to prepare for the next upturn: lower costs and more capacity. At the first plant, OEE rose in eight months from 67% to 79%, above the 75% target, and average daily production from 275 to 335 tonnes. The client then gave us eight further projects in five countries.<\/p>\n\n\n\n
Duration: eight months<\/p>\n\n\n\n
Demand for and prices of polypropylene, used in car parts such as battery housings, dashboards, running boards and bumpers, had fallen sharply. The client wanted to use the dip to prepare for the next upturn.<\/p>\n\n\n\n
That meant lower fixed costs, and an organisation and equipment ready for more output at lower cost. The first plant was the pilot for the collaboration with Axisto.<\/p>\n\n\n\n
The plant had no clear daily production performance data. As a result, management couldn’t see the root causes of performance problems and couldn’t take the right action. Preventive maintenance data was missing too, leading to frequent and unpredictable breakdowns.<\/p>\n\n\n\n
Once the losses were actually measured, the 22% downtime turned out to consist of product changeovers (11%), breakdowns (7%), colour adjustment (2.5%), staff shortages (1%) and other causes (0.5%). Because of the way scheduling worked, colours weren’t always run from light to dark, which cost extra cleaning and changeover time.<\/p>\n\n\n\n
Analysed performance together.<\/strong> Not a study handed over to the site, but work done together with the teams. That produced insight into and acceptance of the problems, and a growing willingness to tackle them, with their own ideas about how.<\/p>\n\n\n\n Introduced short-interval control.<\/strong> Output was tracked every hour, and downtime was logged as it happened. For the first time, the team could see where production was being lost.<\/p>\n\n\n\n Reduced changeover times.<\/strong> Through better scheduling rules, better communication between teams and a schedule for pre-blending.<\/p>\n\n\n\n Set up preventive maintenance.<\/strong> A preventive maintenance schedule reduced unplanned downtime early in the project. Calculating mean time between failures required historical data, so that paid off later.<\/p>\n\n\n\n Made the shop floor cleaner, safer and better organised.<\/strong> As part of Total Productive Maintenance, the operators ran a 5S programme, followed by autonomous maintenance and safety work: machine guarding, safe working procedures around running equipment, and training in maintaining and cleaning key machine parts. A refurbished control room gave the team visibly more pride and engagement.Got workforce planning in order.<\/strong> An effective staff planning process put an end to downtime caused by understaffing.<\/p>\n\n\n\n Financially: the investment paid back four times over.<\/p>\n\n\n\n Together we drew up a complete roadmap for further improvements. The line organisation took it forward independently, and grew to an even higher level of performance.<\/p>\n\n\n\n The client then asked us to carry out eight further projects, at plants in Germany, England, Italy, the Netherlands and France.<\/p>\n","protected":false},"featured_media":307,"template":"","service-types":[93],"divicion-categories":[],"class_list":["post-917","case","type-case","status-publish","has-post-thumbnail","hentry","service-types-manufacturing"],"yoast_head":"\nWhat it delivered<\/h2>\n\n\n\n
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What happened afterwards<\/h2>\n\n\n\n