Upcoming Webinar: Optimize Inventory Safety Stock - A Fundamental Principle and Tool

Many factors involved with an inventory affect the bottom line of a business including the purchase price, selling price, bank interest rate, warehouse space cost, utility cost, cost of the item being damaged, stolen and becoming obsolete, etc. It is a consensus among planners that balancing these factors and setting safety stock levels to improve the overall profitability is by no means an easy task.

In the webinar, Dr.Jay Zhou will introduce a much-need guiding principle that pinpoints the optimal safety stock level to maximize the profitability. It is a powerful and quantitative principle yet a straightforward one that can be easily understood by everybody. The benefits of the approach are many-faceted: fully measurable financial outcomes, improved planning productivity, etc.

Dr. Zhou will show how it actually works by using PeakProfit, a software tool from Friesian Analytics that implements the principle, and finding the optimal safety stocks for stock items convincingly and elegantly in a live demo.

The webinar is designed for all inventory professionals and managers regardless of if they have 2 months or 20 years of working experience. Please register for the webinar scheduled on Thursday, January 13th 2022 - 11:00 AM (EST) here

Upcoming Webinar - Optimize Inventory Safety Stock - A Fundamental Principle and Tool

Without a clear and quantified guiding principle, planners find themselves having a hard time determining inventory safety stock levels. Often, it becomes an “art”, which involves manual judgement and spreadsheets, rather than a science. It is not uncommon to see that a company’s safety stock levels stay the same for years despite the business conditions have changed drastically. As a result, a company’s bottomline is negatively impacted.

In the webinar, Dr.Jay Zhou will introduce a much-need guiding principle that pinpoints the optimal safety stock level to maximize the profitability. It is a powerful and quantitative principle yet a straightforward one that can be easily understood by everybody. The benefits of the approach are many-faceted: fully measurable financial outcomes, improved planning productivity, etc.

Dr. Zhou will show how it actually works by using PeakProfit, a software tool from Friesian Analytics that implements the principle, and finding the optimal safety stocks for stock items convincingly and elegantly in a live demo.

The webinar is designed for all inventory professionals and managers regardless of if they have 2 months or 20 years of working experience. Please register for the webinar scheduled on Thursday, January 13th 2022 - 11:00 AM (EST) here

Upcoming Webinar: Optimize Inventory Safety Stock - A Fundamental Principle and Tool

Many planners take a trial-and-error approach based on experience or guesswork to set inventory safety stock levels. In the webinar, Dr.Jay Zhou will introduce a fundamental principle that pinpoints the optimal safety stock level to maximize the profitability. It is a powerful and quantitative principle yet a straightforward one that can be easily understood by everybody. The benefits of the approach are many-faceted: fully measurable financial outcomes, improved planning productivity, etc. Following the introduction of the principle, Dr. Zhou will show how it actually works by using PeakProfit, a software tool from Friesian Analytics that implements the principle.

The webinar is designed for all inventory professionals and managers regardless of if they have 2 months or 20 years of working experience. Please register for the webinar scheduled on Thursday, January 13th 2022 - 11:00 AM (EST) here .

We are hiring data engineers.

We are looking for multiple contractors for a six-month project, Data Engineers, who may work remotely. The role will help us build and maintain a Snowflake data warehouse that captures historical and ongoing business data. The data in the warehouse need to be cleaned, transformed, and merged to generate a holistic view of the business, which will serve as the foundation for downstream value-added in-depth analytics and machine learning.

Responsibilities
Build Snowflake data pipeline/ETL jobs to ingest data from text files, relational and non-SQL databases.
Perform ongoing maintenance and administration of the Snowflake warehouse.
Design and implement Snowflake schemas including tables, views, and materialized views.
Understand the data from a business perspective and write SQL scripts based on business logic.
Manage data sharing and data access for business users.
Optimize data storage and warehouse query performance.

Qualifications and Experience
Experience with Snowflake warehouse, data pipeline, Apache Airflow, and ETL tools in an enterprise environment.
Strong experience working with large data sets from multiple sources. Performed tasks including data cleansing, data merge, and aggregation.
Strong SQL and Python programming skills. Knowledge in R programming is a plus.
Experience MySQL, MongoDB, Amazon AWS services (S3, SQS, Lamba, etc.).
Experience with business intelligence tool is a plus.
Experience with machine learning is a plus.


To apply for the position, please send your resume to us at jobs@sqlytics.com.

An Antique Neural Network for Image Classification

Quite accidentally I found the picture below showing my research work in 1994 when I trained a neural network to identify the land use types from a satellite image. The neural network had a structure of 3 input neurons, corresponding to the blue, green, and red bands of a Landsat TM image, 14 hidden neurons, and 7 output neurons, representing seven land use types including the cornfields, wheat fields, water bodies, impervious surfaces, etc. The number of training data points is 65.

I coded everything from scratch including the algorithm and the user interface using C++. There were no prebuilt packages to use, no internet to search the information, and no such thing as asking questions on social media. The computer used had a 486 CPU, a 32-megabyte memory, and a hard drive with less than 500-megabyte storage.

How things have changed in 27 years! With a deep learning neural network, such as a convolutional neural network, many layers of neurons serving different purposes can be stacked together to form a complex structure and collectively perform recognitive tasks that were unthinkable in the past. And all these can be done with a few lines of Python scripts and the execution is often done in a cloud computing environment with virtually unlimited computation and storage resources. The progress is astonishing.

Online Course: Oracle SQL for Random Sampling

Since many companies store their critical business data in Oracle databases, it is advantageous to perform random sampling within the same environment using SQL without data movement. For example, it is time-consuming to pull a large data set out of a database and do random sampling using Python on a laptop computer. In addition, the data are prone to various security issues once they are not protected by the database.

A Competition-winning data scientist and long-time Oracle SQL practitioner Dr. Jay Zhou creates an online course and shares his expertise in performing random sampling using Oracle SQL. Students will learn practical skills that can be applied immediately in their work. There were hundreds of people from 85 countries who took the course.

The course begins with a description of scenarios where random sampling is necessary. A number of useful Oracle SQL random functions are introduced. The course uses examples and presents SQL scripts to perform the following common tasks.

• How to quickly view random samples of the data. There are multiple ways to do this task.
• How to select a precise number of samples randomly.
• How to split data randomly. This is a necessary task when we build a machine learning model and need to produce three data sets, i.e., training, testing, and validation sets.
• How to select random samples by groups. For example, we want to randomly select 100 students, 50 of them female and 50 male, from a school.

Are there any course requirements or prerequisites?

Very basic Oracle SQL knowledge

Who this course is for?

SQL developers, data analysts, data scientists, statisticians

Please take the course here.

Taking Operational Efficiency to the Next Level: Leverage the 95-5 Rule of Automation

Through an odyssey of over two decades helping clients in various industries solve hard problems, I have gained a deep appreciation of a pattern that can be leveraged to dramatically improve the quality and efficiency of the work and, ultimately, the return on investment of businesses.

Whether it is inventory planning, or financial fraudulent transaction detection, or finding costly insurance claims, it generally holds true that 95% of the work can be resolved by automated algorithms. The remaining 5% needs to be done by domain experts using their expertise, intuition, and creativity. I call it the 95-5 rule of automation.

The 95-5 rule is not simply a division of the labor between machines and human experts flatly in that proportion. There is a structural and temporal implication in it. Algorithms are first applied to a raw problem, which involves a large number of cases and big data and is hard or inefficient to solve manually. This step produces as an output a simpler problem where the work is greatly reduced, by 95% generally. Human experts then work on this reduced problem and make their judgment calls to reach the final decision.

Take as an example our solution to a worker compensation insurance claim problem. A company receives about 200 worker injury claims daily. Our algorithm highlights 10 (5% of the total) of them as potentially costly using a machine learning model based on factors including age, cause of injury, and injury body parts. Using these 10 cases as a starting point, analysts review them carefully and take proper action. The solution has resulted in a 40% reduction in claim loss.

To recap, in the real world the 95-5 rule of automation works this way: applying algorithms to a raw problem to reduce the work by 95% and subsequently having human experts take on the reduced problem.

Here are the benefits as reported by our clients that have adopted solutions based on the 95-5 rule of automation.

• Improved outcomes. For example, a bank sees its fraud loss reduced by 70%. Another bank finds the bad debt rate dropping by 50%.
• Increased efficiency. In a K12 education company, content tagging is 100 times more efficient than a manual process.
• More jobs. A group in a bank hires more analysts because the operation there drives a good return on investment.
• Improved employees' morale. This is because they work on the reduced problem where the same amount of effort generates more fruitful outcomes. ( I did not realize this point until I saw a report produced by an independent department from a client company.)

When the rule is applied to inventory planning, our advanced optimization algorithm generates a set of recommended safety stocks for all items which serves as the foundation for planners to make further improvements.

One lesson that we have learned is that, unless it is an exceptionally simple circumstance, domain experts should not work with the raw problem directly. Unfortunately, the violation of this principle is happening every day resulting in ineffective, inefficient, and unscalable operations and a stressful workforce. The whole situation is avoidable.

The 95-5 rule of automation has worked for us remarkably. I hope you make the most of it in your organization and take operational efficiency to the next level.

Webinar Video: Holistic Safety Stock Optimization - Putting the Horse Before the Cart

The recorded video of the webinar "Holistic Safety Stock Optimization - Putting the Horse Before the Cart" delivered by Dr. Jay Zhou on August 12.



A significant amount of money can be saved without impacting the overall demand satisfaction by optimizing the inventory holistically. With the right approach, we can comfortably construct an inventory with less money while providing higher overall demand satisfaction.

Webinar Invitation: Holistic Inventory Optimization

If you are responsible for a large inventory, I would like to invite you to attend my webinar "Holistic Safety Stock Optimization - Putting the Horse Before the Cart" on Thursday at 2:30 pm, August 12, 2021. Please register here..

I have written an article ) describing the challenge facing companies with large inventory. Tens or hundreds of millions of dollars or more, in addition to their stock prices or market valuation, are at stake depending on the company’s inventory sizes. C-level executives and senior managers want answers to the following critical questions which are both sides of the same coin.

• How to spend a fixed budget on safety stocks so that the average demand satisfaction for the whole inventory is the highest?
• How to achieve the desired average demand satisfaction for the whole inventory with the lowest budget of total safety stocks?

In the webinar, I will demonstrate a powerful solution developed by Friesian Analytics. The total inventory value can be reduced significantly without affecting the overall demand satisfaction. The horse (budget) should pull the cart (inventory), not the other way around as it is done by many companies. I look forward to seeing you at the webinar! Thank you.

Holistic Inventory Optimization

The following was first published by me as a LinkedIn article.

Tens of millions or hundreds of millions of dollars or more, in addition to stock prices or market valuation, are at stake depending on the company’s inventory sizes. C-level executives want answers to the following critical questions which are both sides of the same coin.

• How to spend a fixed budget on safety stocks so that the average demand satisfaction for the whole inventory is the highest?
• How to achieve the desired average demand satisfaction for the whole inventory with the lowest budget of total safety stocks?

To illustrate the point, consider the following simple inventory with 10 stock items. Factors determining the total value of safety stocks including lead time, unit price, demand standard deviation, and demand satisfaction (DS). Among these factors, DS for the individual stock items is the only one that we can adjust readily.

Figure 1. Common Approach - Setting the Same DS to Each Item

A common approach is to simply set the DS for all parts to a single number. Some managers adopt ABC analysis to divide stock items into several groups based on their values and assign the DS to each group. When we set DS to 91% for all items as shown in Figure 1, the average DS is 91% and the total value of safety stock is $129,985.

However, we can do much better. By setting the DS for each item more intelligently, we can keep the average DS of 91% and reduce the total safety stock by about 16% (Figure 2). Or by assigning a different set of DSs, we can maintain the same total safety stock budget and increase the average DS from 91% to 93% (Figure 3).

Figure 2. How to Keep the Average DS of 91% and Reduce the Total Safety Stock by About 16%?

Figure 3. How to Keep the Same Total Safety Stock Budget and Increase the Average DS From 91% to 93%?

Interested readers may do an exercise. Here is a downloadable spreadsheet file that embeds the formula to calculate DS. One simply fills in whatever DS she/he desires for each item. The average DS and total safety stock will be calculated for you.

If you can produce the results as shown in Figures 2 and 3 or better, congratulations! If not, I would invite you to attend my webinar "Holistic Safety Stock Optimization - Putting the Horse Before the Cart" on Thursday, August 12, 2021

Even if you are able to solve the 10-item problem by trial-and-error, in the real world many businesses have far more stock items than 10, I would still encourage you to attend the event.

Upcoming Webinar: Holistic Safety Stock Optimization - Putting the Horse Before the Cart

I will give a 45-minute webinar on Thursday, August 12, 2021, at 2:30 PM Eastern Time. If you are responsible for a large quantity of inventory, I would encourage you to attend the event. Please register here.

The following is a summary of the webinar.

The CFO of a company wants to know, given a fixed budget, how to determine the safety stocks of all inventory items to achieve the highest overall demand satisfaction. In the webinar, Dr. Jay Zhou will show that the above goal can only be achieved by optimizing safety stocks holistically. He will demonstrate a powerful solution developed by Friesian Analytics (https://friesiancorp.com). The horse (budget) should pull the cart (inventory), not the other way around as it is done by many companies.

Free Online Calculating Inventory Safety Stock Calculator

I made a youtube video at https://lnkd.in/d3hECWU to introduce a free online tool for calculating inventory safety stock. A spreadsheet file that implements the calculator is downloadable. Here is the link to the calculator. Enjoy!

Oracle Function Returns Two Values

There is a table in a schema that contains three columns, p, low and hi. In the table, p is the primary key. I want to develop a function to return low and hi based on an input variable p. First I create a type.

create or replace type t_low_hi as object ( low number, hi number);  

Then I create a function that finds low and hi based on p, constructs a type object and returns it.

create or replace function f_prob (p_p number)  
return t_low_hi is  
p_Low number;  
p_Hi number;  
Str_sql varchar2(2000);  
begin  
Str_sql := 'Select low, hi from t_lookup where p=:1';  
Execute immediate str_sql into p_low, p_hi using p_p;  
return t_low_hi(p_low, p_hi);  
end;  
/ 

I call the function and retrieve low and hi for p with a value of 0.99.

select x.v.low , x.v.hi from (select f_prob(0.99) v from dual) x; 

Updated Online Chinese Document Analytics Tool

We have updated our free online tool for analyzing Chinese documents: https://aistrike.us/text-analysis.html A user fills in a textbox with the content and click Submit button. The tool identifies words, displays a word cloud picture and calculates a sentiment index for each sentence. Enjoy!

Identifying words in a sentence is necessary. Chinese words in a sentence are next to each other without spaces separating them, e.g. Chinesewordsinasentencearenexttoeachotherwithoutspacesseparatingthem. And yes, the division of words could be ambiguous. For example, "结婚和尚未结婚的" could mean "married and unmarried" ("结婚 | 和 | 尚未结婚的") or "married, monk, unmarried" ("结婚 | 和尚 | 未结婚的") .