Skip to main content

Is Robotic Automation competitive with BPMS?

No, Robotic automation extends and complements BPMS and SOA initiatives which are attacking the automation challenge from a different, top down, IT driven angle. Robotic automation is aimed at small-to-mid size automation initiatives. Where speed and size and agility are major factors, then robotic automation is often the fastest and most efficient approach. When larger initiatives are required with a fuller “Business Process” character then BPMS may be better suited.
This difference in scale is illustrated with the so called Long Tail of Automation Requirements. This says that core IT deals with the high volume bulk processing requirements an organisation may have. Typically, these are core ERP systems, mainframe accounting and core data bases. As we move towards the middle of the graph requirements become more specialist and diverse. This is where an organisation often differentiates its product and service offerings. Typical technologies here are workflow, desktop integration, BPMS, agent acceleration. These are large IT control programs that service to offer a platform for automation and work management.

Finally we have the third section of Long Tail – these tasks are characterized by their diversity. Often they are too diverse to make an IT change program, and may be too small to justify IT project costs. Here traditional approaches have been to outsource, or offshore in order to adjust labour rates to make the task more competitive. Robotic automation offers an alternative to off shoring or outsourcing – presenting a new cost-band of labour based on robots.

Comments

Popular posts from this blog

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...