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.
Labels:

Comments

Post a Comment

Popular posts from this blog

Data Wrangling vs EDA

  Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
Post a Comment
Read more

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...
Post a Comment
Read more

Infrastructure limitations ESX Server 4 vs ESXI 5

Some limitations in ESX Server 4 may constrain the design of data centers:[28][29] • Guest system maximum RAM: 255 GB • Host system maximum RAM: 1 TB[28] • Number of hosts in a high availability cluster: 32 • Number of Primary Nodes in ESX Cluster high availability: 5 • Number of hosts in a Distributed Resource Scheduler cluster: 32 • Maximum number of processors per virtual machine: 8 • Maximum number of processors per host: 160 • Maximum number of cores per processor: 12 • Maximum number of virtual machines per host: 320 • VMFS-3 limits files to 262,144 (218) blocks, which translates to 256 GB for 1 MB block sizes (the default) or up to 2 TB for 8 MB block sizes.[30] However, on a VMFS Boot drive, it is usually very difficult to use anything other than 1 MB Block size [31]. With ESXI 5 there has been some changes to these limits[32] • Guest system maximum RAM: 1 TB • Host system maximum RAM: 2 TB • Number of hosts in a high availability cluster: 32 • Maximum number ...
Post a Comment
Read more