Suresh Kumar Gupta

The number of bot licenses required for an RPA deployment depends on several factors such as the number of processes to be automated, the complexity of those processes, the frequency of execution, the number of employees involved in the process, and the desired throughput.
To illustrate, let's consider an example of a financial organization that wants to automate its invoice processing system.
Suppose the organization receives 10,000 invoices per month, and the process involves data extraction from invoices, data validation, and invoice payment processing. The process takes an average of 5 minutes per invoice for a human worker. So, it takes around 50000 minutes (833 hours) per month for processing invoices.
Now, if we use an RPA bot that can process an invoice in 1 minute, it would take only 10,000 minutes (167 hours) per month, which is a significant reduction in processing time.
Assuming that the bot works for 8 hours a day, five days a week, and four weeks a month, it can process 9600 invoices per month. Therefore, the organization would need at least two bots to process all 10,000 invoices per month.
However, the organization may also consider other factors such as the scalability of the process, the likelihood of increased volumes of invoices, the risk of bot failures, and the potential impact of human errors on the process.
In conclusion, the number of bot licenses required for an RPA deployment depends on several factors, and organizations must carefully evaluate these factors to determine the optimal number of licenses needed to achieve the desired ROI.

First in Still Here (FISH) is a term that refers to excessive and a very slow-moving inventory. The acronym (FISH) is a take-off on the FIFO (First In, First Out) and LIFO (Last In, First Out) acronyms to describe inventory cost layering systems.
The concept of FISH is important to understand as it can indicate whether your business has enough money in its operating capital account or not. If the inventory on hand is too much which is not selling quickly then some of it needs to written off as obsolete or defective and you need to replace it with new items. If this inventory is left unchecked, it can lead to serious financial troubles for your company since cost is involved with old stuff sitting on the shelf.
FISH is one of the Six Sigma terms used to describe the movement of products through our supply chain and this concept relies on the fact that if we are able to keep our product moving at a steady pace through our supply chain, it will increase our chances of success by preventing bottlenecks and reducing inventory costs.
If a business has a large amount of FISH inventory it indicates excessive working capital has been invested due to which it poses a high risk for outdated inventory which needs to be written off.
Industry Examples
1. The food industry is famous for overstocking unpreserved food items and the waste is not only financial but environmental as well. Every year around 30% of food purchased and 45% of crops harvested are wasted industry-wide and this translates to over $160 billion wasted annual in the US alone.
2. Fashion and beauty industries have a disturbing trend with increasing loss figures year over year due to unsold inventory and wasted products
This is not the case with only individual companies that struggle with their inventory, it is to do with entire industries. Businesses that are Item-based are made or broken almost exclusively on the back of their inventory management practices. So to reduce the amount of First in Still Here (FISH) inventory, companies need to consider their products’ life cycles and the costs involved with keeping the product on hand as inventory and also costs associated with ordering more when needed.
Challenges
Some of the causes for Fish Inventory could be due to unexpected low sales, excessive inventory purchases and Poor management of materials.
There are three main challenges why FISH can be harmful problem for many businesses.
1. If there is an excess amount of inventory on hand then there is a possibility the item may not be sold and may go out of date
Products having long shelf lives and/or low demand or sitting on your shelf for long time are less likely to get sold at full price.
2. Poor materials management within the business itself.
Over-ordering materials and/or making mistakes in purchasing decisions leading to have more than what you need for current production levels.
3. Sales are lower than expected—or that they will be.
Due to unexpected low sales in future businesses will want to reduce their current stock levels in order to avoid writing off outdated product later down the line
Best practices - FISH
The world of business is all about staying ahead of the competition and maintaining a competitive advantage. In order to overcome the above challenges, it’s important to think about first in, still here (FISH) inventory in terms of tested and proven below best practices.
1. Consider re-evaluating your inventory management system.
This practice can tell us if there is a way to reduce the amount of time it takes for products to move through our warehouse and into customers’ hands.
2. Try implementing FIFO (first in first out) policy or LIFO (last in first out) policy.
This practice can enable us to minimize the overall age of our inventory at any given time.
3. Look at ways to optimize your supply chain.
This can be done by working with multiple suppliers instead of just one or two so that our product flow isn’t dependent on any one supplier going down or having problems with their delivery schedule.
 
To conclude FISH is an important concept for businesses to understand because it can have a massive impact not only on the inventory management strategy but also the overall success of the business

Lean Six Sigma experts usually suggest to take a target for improvement in mean as compared to median because of the following reasons: -
·        Population Data is used for analysis
·        Approach is decision based
·        Focus is on achieving on project goals
·        The mean has a direct relationship with the total (total = mean x N), but the median does not that is why it difficult to move the needle with median
·        Outliers present in the data can be taken care of by understanding the reason and if it is result of measurement error we can exclude it from analysis so that there is no impact on Mean
 
Let’s consider the following examples involving skewed data
 
Example 1: Lotteries
Which one of the below two games would you choose to play?
Game A:
·         A 1/3 chance of winning $1
·         A 1/3 chance of winning $2
·         A 1/3 chance of winning $3
Game B:
·         A 1/3 chance of winning $1
·         A 1/3 chance of winning $1.90
·         A 1/3 chance of winning $1,000,000
It was found that most people preferred Game B even with lower median ($1.90 vs. $2.00).
After looking at the data set although the distribution is skewed we should prefer using mean over median since for Game B  $1,000,000 is an extreme value and is part of the distribution and relevant for decision-making.
 
One shouldn’t take the decision based on the 1 in 3 chance of becoming a millionaire with Game B.
 
Example 2: A Company wants to increase its Headcount resource
 
A company is planning to hire 300 new employees and to estimate the total cost of the initiative.
 
Salaries data distribution would tend to be skewed and we can expect that the mean salary of new jobs would be larger than the median.
 
Considering the data distribution of salaries to be skewed the manager of the company decides to use Median and to estimate the total cost of hiring the new employees he multiplies median with 300.
 
The actual cost turned out to be much higher than their estimate since the Manager had used Median and not mean.
 
In this example Company was more interested in the total cost of hiring the new employee and Manager should have used Mean since it has a direct relationship with the (total = mean x N), but the median does not.
 
In this case, the company was interested in a total. The mean has a direct relationship with the total (total = mean x N), but the median does not.
 
So we can conclude that mean should be preferred over median as a better metric whenever the analysis is driven by goals and business decision’s depends on a total (total revenue or total sales) since it has direct relationship with the total. Also Means are sensitive to large values and care should be taken to ensure that outliers if any are taken care of.