Kevin Schmoldt’s Post

🤔 Why would a supermarket manager clean up chicken fridge daily? He didn't just do it once, it was a daily ritual. Seriously, everyone thought he was somewhat of a weird one. 1. He didn't clean anything else in the store 2. He didn't stock any other shelves 3. There were others who could do that Yet he was on it everyday: 🧹 clean several times a day 📦 restock several times a day What happened then was a bit of shocker. 📈 Week after week chicken sales started growing. There are ON and OFF weeks and chicken didn't care, it was growing. So what the hell happened? Three things: 1. 🎯 focused on one metric. (chicken was a separate metric in the sales pie) 2. 📊 identified leading metrics which influence the target one 3. ✅ executed daily Clean and stocked fridge did not enable sales. It ensured that whenever anyone wants it — chicken is there. ⏰ Doing it several times a day ensured that it's clean and stocked most of the time. Looking at it in retrospect it's easy to say, well he simply applied 4DX and got the results. 💡 How the hell did he figure it out some 20 years ago?

In this dashboard, we can see the sum of sales by the manager,branch, pizza type, quantity of pizza and also by the city-wise.

Hello Everyone, 🚀 🚀 I am really excited while saying that i have completed my power bi project plato's pizza sales analysis. Plato Pizza is a fictional Greek-inspired pizza place in New Jersey, United states of America. Things are going OK here at Plato’s, but there’s room for improvement. We’ve been collecting transaction data for the past year, but really haven’t been able to put it to good use, Mario Maven (Manager, Plato’s Pizza) want the following business questions to be answered from the insight derived from their dataset. 🍕 💻 🤔 1)What days do we tend to be busiest both for number of orders and income? 2)How many pizzas are we making during peak days, days of the week and months? 3)What are our best and worst selling pizzas? What’s our average order value? 4)How well are we utilizing our seating capacity? (we have 15 tables and 60 seats) 🗃 About the dataset This dataset contains 4 tables in CSV format The Orders table contains the date & time that all table orders were placed The Order Details table contains the different pizzas served with each order in the Orders table, and their quantities The Pizzas table contains the size and price for each distinct pizza in the Order Details table, as well as its broader pizza type The Pizza Types table contains details on the pizza types in the Pizzas table, including their name as it appears on the menu, the category it falls under, and its list of ingredients. 💡 insights:- 1) There is a total number of 21,350 Orders, the total Revenue generated is $817,860, the average order value is $38.31 and the total number of pizzas sold is 49,574. 2) Plato Pizza has more Large(L), Medium(M) and Small(S) size pizza orders than the Xtra-large(XL) and Xtra-xtra-large(XXL) size order. 3)A total of 23,817 pizzas are made during peak periods with 13,189 pizzas around 12 to 1pm and 10,628 pizzas around 5 to 6pm. 4) i. The top four best selling pizzas are the Classic deluxe pizza, Barbecue chicken pizza, Hawaiian pizza, Pepperoni pizza. ii. The top four worse selling pizzas are the Soppressata pizza, Spinach Supreme pizza, Calabrese pizza, Mediterranean pizza. 5) Most order are for 1-2 people sitting at a table so increase table for 2 instead of 4 people sitting . In this project i have created various measures, used many dax functions including related dax function ,used matrix to show the peak hour, used slicers ,charts, graphs, cards . overall i really enjoyed while making this project. 😁 🤖 do check out novypro:- https://lnkd.in/duZ__Kzg github:- https://lnkd.in/gzE3iDF3 #dataanalytics #dataanalysis #powerbidashboard #powerbi #project

Sharing a nice little article about Friends' Marketplace from the Shelby Report (a food and grocery industry magazine)!

**Pizza Place Sales Analysis** **Business Case:** A fictitious pizza place provided a dataset including attributes like size, type, category, quantity, date, and time. The goal was to conduct various analyses to derive meaningful insights, including revenue, sales, and ingredient analysis. **Analysis Goals:** 1. Determine daily customer count and peak hours. 2. Identify popular pizza orders and bestsellers. 3. Calculate yearly revenue and identify sales seasonality. 4. Analyze menu items for potential removal or promotion opportunities. 5. Determine average monthly and quarterly sales. 6. Evaluate sales trends across different seasons. **Key Insights:** - On average, the pizza place receives 60 daily orders, with peak hours during the late afternoon and evening. - Single pizza orders are predominant among customers. - The best-selling pizzas include The Big-Meat Pizza (S size), The Thai Chicken Pizza (L size), and The Five Cheese Pizza (L size). - Most sales occur during the spring and summer seasons. - The pizza place generates a total revenue of $818k annually, with the highest revenue in July. - Friday sees the highest number of orders, generating $136k in revenue. - Garlic, Tomatoes, Red Onions, and Red Peppers are the most used ingredients. **Project Link:** [Pizza Place Sales Analysis](https://lnkd.in/dNKUZcGP) This project provides valuable insights into the pizza place's sales dynamics, informing strategic decisions for inventory management, menu optimization, and revenue growth.

🤔 Ever wondered about the distinction between best-by and sell-by dates while navigating the grocery aisles? 🛒 Join us on another enlightening Q&A segment where we decode the mystery behind these dates📅. Have burning questions for our experts? Share them in the comments below!👇 #HowItsCoded #KeepCodingSimple #BestByCodes #SellByCodes #QandA #SNEEDCAST #CustomerFAQs #TraceabilityCodes #RetailRequirements #TIJ #ThermalInkjetPrinters

Keeping a close eye on how your promotion and LTO offers are doing in real time will help you increase sales and per person average. Check out our recent webinar where you will hear how Twin Peaks Restaurants uses promo reporting to get better results with their weekly promos. https://hubs.la/Q02Hs9bG0

As the Grab-And-Go industry continues to grow, this article from Nestle Professional is useful for businesses looking to enhance their grab-and-go offerings effectively! Some highlights are: - Strategies for optimizing grab-and-go food services - Emphasis on creating appealing displays - Using appropriate packaging, and labeling items clearly - The importance of cross-merchandising to boost sales, suggesting pairing meals with beverages and snacks https://lnkd.in/gtRPRuj5

Keeping a close eye on how your promotion and LTO offers are doing in real time will help you increase sales and per person average. Check out our recent webinar where you will hear how Twin Peaks Restaurants uses promo reporting to get better results with their weekly promos. https://hubs.la/Q02Hs0ZD0

Latest Episode of Market Trends with Tracy: Finally Some Good Chicken News #marketnews #beefnews #porknews #poultrynews #grainnews