Bhavesh Wakde

Drum-Buffer-Rope (DBR) is a method derived from the concept Theory of constrain(TOC) from the book written by Eliyahu Goldratt . As book indicated that slowest scout of mountaineering team decided the pace of climbing on mountain of the troupe. Troupe had identified that scout was loaded heavily (constrain) which decides the pace of team. After load of the scout was distributed to the team & same scout had been put up to lead the team. Overall pace of the troupe got increased and scout boy (Drum) become the frontier to lead the troupe & decide the pace which others follows easily . Similar pattern is being co-related with the methodology of DBR. Terminology are : Drum : The pace setting resource/Production pace(Constrain) Buffer : The amount of protection in front of bottleneck resource Rope : The scheduled staggered release of material to be in the line with the Drum’s schedule. As shown in the picture(Source: https://www.marris-consulting.com/en/training-news/training/training-theory-of-constraints-in-production) considering the industrial scenario set of processes are involved to make the output from the system. In which one of the process will be considered as bottleneck which is having higher cycle time or lesser throughout , compare to other processes is being considered as constrain(Drum).This bottleneck decides the throughput or rate of production of entire cycle/system. To avoid shortages at bottleneck process buffer is getting planned ,as an hour lost for the sake of inputs bottleneck process will affect the entire output of the system .Once the buffer depleted in front of bottleneck process signal (though rope) is getting pass on to non- bottleneck process to initiate to feed up to bottleneck process. This systematic approach protects weakest link in production system against process variation and dependency, which maximize system’s overall production effectiveness. The Drum is a schedule for the bottleneck process in entire system and the Rope is a schedule for releasing raw materials to the shop floor and derived according to the Drum and Buffers. The Rope ensure the proper subordination of non-bottlenecks. Any operational plan needs to be productive, reliable, realistic and robust. - If it aligns with market requirment, we consider it as productive. - If it is aligned with our resource (capacity and add-on capability), we consider it reliable as well as robust. in case of inevitable disturbances or disruptions that will hit it; “realistic” means that it is capable of being done with the available resources, including material supply, maintenance etc. In Toyota Production System. Rope (signal) concept is similar to KANBAN (signal-card) which provides inventory details at different process & refurbishing the stock at operational levels . Both the concepts provide the system for the proper planning of inventory management except the buffer description in DBR is taken up w.r.t. time whereas in KANBAN relevant to parts quantities. Since KANBAN provides better trace-ability which consider physical count of parts it gives faster and better result than Drum-Buffer-Rope method.

Variance calculation is used to find out dispersion of population(N) which contents entire set of data. This can have multiple sub set of data which can be represented by sample size(n). Formula for the population of variance : σ2 = Σ ( Xi - μ )2 / N N = Population σ2 =population variance X =Set of data which can range from i=1 to the population(N) μ = mean of population Formula for the sample of variance is similar but it can be categorized as biased and unbiased : Biased Unbiased s2n = Σ ( xi - x_bar ) 2 / n n = Sample size of the Population s2 = Sample variance x =Set of data which can range from i=1 to the sample (n) x_bar = mean of sample s2n-1 = Σ ( xi - x_bar ) 2 / n-1 n = Sample size of the Population s2 = Sample variance x =Set of data which can range from i=1 to the sample (n) x_bar = mean of sample If sample drawn data are evenly getting picked up from population then its mean will be very much close to true population mean, whereas sample data drawn are picked up from particular zone there are high chance of sample mean will be far away from population mean such cases leads to underestimation to the true variance of the population. This leads to sample mean will seats between the sample data but far away from population mean which also indicates that gap between sample variance to the population variance. This phenomenon is known as biased. To overcome this issue unbiased variance formula is getting used to find out the sample variance . This also provides the high liberty of degree of freedom during sample data collection which is getting restricted during biased variance calculation.

"Good Session on DMAIC methodology to enhance the practical applicability." Bhavesh Wakde, Sen Manager Operations, CMSIPL