The organisation that I’m employed with is a world leader in the paper & packaging industry. The company specialises in manufacturing paper-based packaging, with a network of paper, recycling and forestry operations. It is an integrated producer, with packaging plants sourcing the major part of their raw material requirements from the company's own paper mills. In turn, the sourcing of recovered fibre and wood for the mills is managed through a combination of reclamation and forestry operations and purchases from third parties. It has around 41,000 employees across 32 countries - 21 in Europe, 11 in the Americas. The factory I work in produces corrugated packaging mostly for the food and drink industry, employing approximately 170 people. Description of the problem
One of the production converting machines was not performing to the required run rate per hour and was constantly behind its production capability putting pressure on the business to meet tight customer demands. The impact of this was that the machine was only producing at an average run rate of 1700 boards per hour against a target of 2000 boards per hour and producing at a 90% customer delivery rate. The product due to be converted on this machine was constantly having to be switched to other machines to ensure customer deliveries. The machine in question was a BOBST 1600 die-cut converting machine with a BOBST 160 2 colour flexo (printing) machine prior to this. Both machines have a vacuum cup feed plate for the initial feed. The printing machine; then has rollers and pull collars to take to board through. A set of gripper bars mounted on chains pull the board through the die-cut machine to be cut to shape, the grip waste is then ejected onto a conveyor system. Analysis of the problem
The machine efficiency data was analysed, this included machine running speed and machine operational stoppages. Machine stoppage data for the previous 6 weeks was downloaded from the machine efficiency collection computer. This showed all the stoppages in hours and the reasons that had been inputted by the operators. The stoppages were put into a Pareto graph (appendix 1). The Pareto principle (also known as the 80–20 rule) states that, for many events, roughly 80% of the effects come from 20% of the causes. Business-management consultant Joseph M. Juran suggested the principle and named it after Italian economist Vilfredo Pareto, who observed in 1906 that 80% of the land in Italy was owned by 20% of the population; he developed the principle by observing that 20% of the pea pods in his garden contained 80% of the pea. The analysis, using the top 20% of downtime, showed that that 80% was due to the top four downtime reasons. The machine stoppages were micro stops (stoppages below 2 minutes), sorting bad board, fine tuning of the machine / board jams and meal breaks. These all added up to 180 hours of downtime out of a possible number of operating hours of 864 hours. The next step was to gather the machine operators and area team leaders together to discuss and brainstorm the findings, the results were put onto a fishbone diagram (appendix 2) in order to show what category the various reasons were associated with. This categorisation allows the problems to be grouped and can lead to different solutions being determined quickly, i.e training, engineering solutions etc. Fishbone diagrams originally called Ishikawa diagrams (also called herringbone diagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa (1968) that show the causes of a specific event. Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation. The categories typically...