Supply chain analytics is a building block for operations-heavy company. The supply chain function is responsible for managing the purchase of raw material, inventory, vendor management, negotiations, among other things. Hence, it becomes important to learn about supply chain analytics, and we believe there is no better way to learn from real-life supply chain analytics case study.

Rajesh is a manager of a retail store in Timbaktu. Rajesh has been facing a problem of low service level for the past few weeks. He has been given a target from his boss to improve the service level to 95% or above from current levels of 80%. Since Rajesh’s appraisal rating depends on the service level that he is maintaining, he took this target very seriously and started in-house analysis to identify the problem.

He collected the sales and procurement data for analyses. During his analyses, he wanted to see if something can be improved to increase the service level to 95%. While doing the analyses, he noticed that while their sales forecast are quite in line with their demands, the problem exists with their reorder level. They are not able to order the right quantities from the vendors.

Some of the data points that Rajesh collected are as follows:

Stock is delivered from the vendor to the retail store in 4 weeks after placing the order i.e. lead time is 4 weeks. Demand is normally distributed with a mean of 100 units a week and a standard deviation of 10 units.

Also, Rajesh is also interested in knowing the reorder level if he wants to increase the service level to 98 per cent.

Could you help Rajesh identify the right reorder level?

If you have any other way of solving this case study, feel free to share your solutions with us at info@aimonks.com. If we like your solution, we will publish it on our website.

You may also like Retail Analytics Case Study: Helping Fashionista, Inc. Improve Business

Disclaimer: Please note that this case study has been written with a sole purpose to impart information. Any resemblance with a true business scenario is purely coincidental and AI Monks should not be held responsible for it.

The post Supply Chain Analytics Case Study: Improving Procurement for a Retail Store appeared first on AI MONKS.

Over the past few years, retail analytics has played an important role in driving business performance for retail organizations. Be it reducing costs, improving top line or optimizing process, retail analytics has played an important role in all facets of an organization. In order to learn the benefits that analytics can provide to retail organizations, case study is one of the best methods. This post – Retail Analytics Case Study: Helping Fashionista, Inc. Improve Business – has been written with an objective to help you experience actual business problems that companies face.

Fashionista, Inc. is a brick-and-mortar fashion retailer with 100+ stores across the USA. The retailer has been doing a good job for the past 10 years in acquiring customers and has maintained pace with the new and latest trends over the years. The company has focused a great deal on tying up and partnering with popular brands and innovative labels over the years. Though the company has been doing good and has an EBITDA margin of 15%, which is one the highest in the industry, two quarters back, the company decided to introduce automation and digitization in all its stores.

As a part of its digitization strategy, the retailer decided to do a pilot project for three months at 5 of its stores. The project aimed at capturing the number of walk-ins (potential customers) at each of the stores at any point in time across all days of the week for the time the store was opened (10:00 AM to 10:00 PM). The company later, aggregated the data at an hourly level for each of the stores to see the number of walk-ins at each of the stores in an hour.

On further analysis of this data,  the company came up with surprising insights that the average conversion ratio (the number of transactions made during an hour vs the number of people entering the store during the same hour) is way below the industry standard. The client initially thought that the people coming in a group of 2 or more usually end up buying in a single transaction; however, the same was denied by multiple store managers. The reason put forth by store managers was that Fashionista runs a very lucrative loyalty program for its customers and hence, multiple customers in one group usually tend to buy separately so as to take benefit of the loyalty program. Moreover, this is also encouraged by store managers to increase the customer base in their CRMs.

When the CEO got to know about this, he decided to do a detailed study on this, and identify the reasons and potential opportunities to improve. He wanted to answer one question in particular – do we have adequate staff in stores to cater to customers who walk-in?

If yes, then there are other reasons for low conversion rate. He also thought that if we are adequately or over-staffed, can we reduce in-store staff and save some cost?

If no, what is the optimal number of in-store staff at each of the five stores? Can we also hire staff on contractual basis for few hours on certain days to manage the under-staffed situation? OR hiring full-time staff makes more sense?

Another question he wanted to seek an answer was should he keep the store up and running during certain times when conversation rates are abysmally low?

The CEO thought of reaching out to management consultants who can help him provide clarity on his thoughts and decide the strategy ahead. Can you help the CEO in solving the problem at hand and provide him potential savings or revenue improvement estimates?

Additional information:

The stores currently deploy staff depending on the area of the store. For every 200 sq ft, the company employs one full-time employee. The salary of staff is USD 3,000 per month for a shift of 8 hours, six days a week. For every additional hour that the staff works, the company has to pay, USD 10 per hour extra. Additionally, each of the stores has one store manager with a monthly salary of USD 4,500 for a shift of 8 hours, six days a week. For every additional hour that the store manager works, company pays him USD 24 per hour. Store manager has weekly off on every Monday, hence, there is no store manager on Monday. On an hourly basis, contractual staff can be employed at USD 12 per hour.

The rental and fixed cost of the store is decided on monthly basis and can’t be reduced further.

Below are details of the five where pilot was carried out.

Following datasets have been provided separately.

• Transaction details, aggregated at hourly level, at each of the five stores
• Walk-ins, aggregated at hourly level, at each of the five stores

If you want us to review at your solution and publish on our website, send your solutions at info@aimonks.com.

Here’s another case study for you to try: Supply Chain Case Study: Improving Procurement for a Retail Store

Disclaimer: Please note that this case study has been written with a sole purpose to impart information. Any resemblance with a true business scenario is purely coincidental and AI Monks should not be held responsible for it.

The post Retail Analytics Case Study: Helping Fashionista, Inc. Improve Business appeared first on AI MONKS.