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Changeover time reduction through SMED approach
Published in Rajeev Agrawal, J. Paulo Davim, Maria L. R. Varela, Monica Sharma, Industry 4.0 and Climate Change, 2023
Abhijit Das, Sumer Sunil Sharma, Sushovan Ghosh, Tara Charan Bharti, Sandeep Mondal
Karam et al. carried out research on implementing SMED in a pharmaceutical company. SMED methodology, as a lean tool, was used at a bottle filling line in a Romanian pharmaceutical company to improve the changeover process. Implementing lean principles not only helped decrease the changeover time at the bottleneck process by up to 30% but also brought in added advantages in the form of process quality improvement, standardization of work, and not to mention several economic benefits. These positive results were seen just 12 months from the date of implementation. For the successful implementation of SMED, mapping the define-measure-analyze-improve-control (DMAIC) structure proved to be vital [9]. Vieira et al. [10] conducted a study on the application of SMED in an automotive component manufacturing company that produces air conditioning pipes. The main concern was to reduce the extremely high setup time of the deep drawing machine by about 20%. Deep drawing machine is crucial for the production of tubes, and thus quick setup of these machines is very much necessary. The setup time previously clocked at 47 minutes, and the machine availability was under 95%. The SMED implementation results came out to be quite satisfactory; not only did SMED help the management to standardize the setup operation, but the machine setup time was also drastically decreased by 38%, which was previously aimed at 20%. 53% of internal setup was reduced, and the overall equipment effectiveness availability increased by 7.7%
Standardized Work as a Foundation of Lean
Published in Mark Graban, John Toussaint, Lean Hospitals, 2018
One variation on standardized work is the methodology for quick changeover, also known as setup time reduction. Toyota’s innovation, under the leadership of Shigeo Shingo, was to reduce stamping press changeover times from multiple hours to less than 10 minutes, via what was called single-minute exchange of dies (SMED). Setup times were often reduced by a factor of 40 in a 10-year period.22 It’s not uncommon for a factory, even today, to quickly reduce setup times from hours to minutes, even if they thought it wouldn’t be possible. Challenging assumptions and being creative to change “the way we’ve always done it” can be very powerful. A hospital equivalent example would be significantly reducing the time required to change over an operating room between patients, including all of the cleanup, sterilization, and preparation for the next case.
Heijunka, Planning and Scheduling, Sequencing Activities, Load Balancing
Published in Charles Protzman, Fred Whiton, Joyce Kerpchar, Christopher R. Lewandowski, Steve Stenberg, Patrick Grounds, James Bond, The Lean Practitioner’s Field Book, 2018
Charles Protzman, Fred Whiton, Joyce Kerpchar, Christopher R. Lewandowski, Steve Stenberg, Patrick Grounds, James Bond
To effectively use these types of devices, we must have stability and some predictability in our production operations. If our FPY varies more than 15%, it may be quite difficult to effectively utilize the sequencing/heijunka box. Anything higher than this will result in chaos and staff ignoring the box and doing whatever they think is best to get parts out. Another factor to consider is unplanned machine downtime. If one day the machine runs all day and then the next it is down for 3 hours, then it will be very difficult to follow the sequencing box. Changeover times must also be predictable, and we often have to standardize this process so we can have consistent times to complete the task. Whenever we do a heijunka/sequencing box, it is often precluded by a single-minute-exchange-of-dies kaizen. This helps to bring stability and standardization to the changeover process.
Applying value stream mapping in an unbalanced production line: A case study of a Chinese food processing enterprise
Published in Quality Engineering, 2020
Qingqi Liu, Hualong Yang, Yuchen Xin
Literature also indicates that many operations that are distinctive to the processing industry require implementation of lean concepts to improve performance or quality (Billesbach 1994; Cox and Chicksand 2005; Zanoni and Zavanella 2005). For example, some industries, such as the food processing industry, may have seasonal restrictions of raw materials and huge processing equipment. Such industries have to follow a “make-to-stock” strategy, and consequently, raw materials, finished products and auxiliary materials generally occupy huge spaces. Lean can help achieve better utilization of space and equipment (Billesbach 1994; Cox and Chicksand 2005). Moreover, due to the required cleaning and washing of processing machines, some processing industries are characteristic of long set-up times, which can be reduced through the implementation of lean practices, such as set-up time reduction and quick changeover (Billesbach 1994). Finally, key processes are often time or temperature sensitive and require strict control; examples include drying in article making, cooling in steel manufacturing, and maturing in the food industry. Therefore, lean-manufacturing techniques such as Just-in-time (JIT) and pull production could be effective in achieving timely handling to guarantee the quality of the final products (Zanoni and Zavanella 2005).
Analysis and approximation for the performance of a workstation with various types of setups
Published in International Journal of Production Research, 2018
Changeover setups are induced by switching manufacturing processes between products (Culley et al. 2001). When a machine begins producing a new product, it may need to change parts or some parameter settings. As the machine settings are switched to make different products, this kind of setup is called a product-induced setup caused by changeover, or simply a changeover setup. The reduction of changeover setup time has been addressed extensively in the lean manufacturing and TPS. Changeover setups commonly exist in flexible manufacturing systems. For example, a photolithography machine in a semiconductor fab needs to change masks when processing different products. Or, a chemical mechanical planarisation (CMP) machine can process products with different recipes, such as Oxide, Trench and Tungsten. The changeover time from Oxide to Tungsten, for example, is often shorter than the changeover time from Oxide to Trench. Hence, setup time distributions can depend on the product type to be processed and the previous product type, i.e. the changeover setup is sequence dependent. If the setup time only depends on the job type to be processed, it is called sequence independent (see, e.g. Lee and Pinedo 1997; Allahverdi 2000; Cheng, Gupta, and Wang 2000; Ruiz and Maroto 2006).
Production scheduling of flexible continuous make-and-pack processes with byproducts recycling
Published in International Journal of Production Research, 2021
Apostolos P. Elekidis, Michael C. Georgiadis
Méndez and Cerdá (2002a), developed a general-precedence MILP model for the planning and scheduling of multiproduct make-and-pack continuous processes. Intermediate storage limitations are taken into account by introducing efficient mass balance constraints, without relying on the concept of time-slots or event points. (Méndez and Cerdá 2002b) proposed a general precedence-based MILP model for a make-to stock production facility. Unlimited storage capacity has been assumed for both intermediate and final products. Giannelos and Georgiadis (2003), proposed a slot-based, MILP mathematical framework for the planning and scheduling of continuous processes. The mathematical framework is based on the STN representation and includes efficient intermediate storage constraints. Medium-sized problem instances can be optimally solved in reasonable computational times. Janak, Lin, and Floudas (2004) proposed a continuous-time MILP model for the scheduling of batch processes. The model is based on the STN representation using the idea of event time points. Günther, Grunow, and Neuhaus (2006) presented two different approaches for the production planning and scheduling problem of a hair dyes industry, by introducing the concept of block planning. Castro, Westerlund, and Forssell (2009) proposed an RTN-based MILP framework considering the scheduling problem of a tissue paper mill. Baumann and Trautmann (2013), proposed a continuous-time, general-precedence MILP model for semi-continuous, make-and-pack production processes. Aguirre, Liu, and Papageorgiou (2017) introduced a decomposition algorithm based on the concept of the rolling horizon approach, considering multistage continuous processes. The algorithm is based on a general precedence MILP model, assuming unlimited intermediate storage capacity and same production sequence throughout all stages. Elekidis, Corominas, and Georgiadis (2019) presented two MILP-based solution strategies for the scheduling optimisation of a real-life, large scale, consumer goods industries. The proposed approaches lead to significant productivity gains by reducing the total changeover time. Vieira et al. (2020), proposed a two stage MILP model for the design and scheduling problem of multipurpose industrial facilities, considering the uncertainty via the Conditional Value at Risk (CVaR) measure. Recently, Yfantis, Corominas, and Engell (2019) presented a discrete-time, MILP-based decomposition algorithm for continuous make-and-pack production plants with a large intermediate buffer tank. Extending this approach, Klanke et al. (2020) integrated a precedence-based, pre-sorting MILP model to improve the obtained solutions.