Qlik Case Studies Chef Works Meets Qlik AutoML

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	Chef Works Meets Qlik AutoML Qlik

Chef Works Meets Qlik AutoML

Qlik

Technology Category	Analytics & Modeling - Predictive Analytics Analytics & Modeling - Machine Learning
Applicable Industries	Consumer Goods Food & Beverage
Applicable Functions	Sales & Marketing Business Operation
Use Cases	Predictive Quality Analytics Predictive Replenishment Demand Planning & Forecasting
Services	Data Science Services
Challenge	Chef Works, a supplier for hospitality businesses, was impacted by the economic climate driven by the global Covid-19 pandemic. The company experienced a reduction in bandwidth and a growing need to effectively practice data science with a lower time commitment. The pandemic left many businesses in the hospitality industry on uncertain ground, and Chef Works needed to make the best use of their time and resources to not only sustain themselves through these changes, but to continue to innovate in ways their customers have come to expect at a price point that would be agreeable to once-lucrative businesses now strained for cash. Chef Works understood the grim reality that, while many of their customers would see the other side of the pandemic with business in-tact, many would not. To better support those that would survive, and to understand which businesses those were most likely to be, Chef Works turned to the power of data science. Read More
About Customer	Chef Works is an industry leader in the design development of apparel for the hospitality industries. A leader in innovation, Chef Works aims to provide the modern products their customers need at a competitive price point. They offer a wide range of products and styles to suit chefs, bartenders, servers, and other hospitality professionals with frequent new offerings that key in on industry trends. The company began their journey with Qlik® in June of 2020, during the global Covid-19 pandemic. Read More
Solution	With Qlik AutoML, Chef Works was able to explore their data with machine learning and iterate on machine learning models in a fraction of the time it took via previous labor processes. They were able to see how Covid-19 was mostly likely to impact their customers, taking into consideration a vast array of business information including state industry and predicted sales using Shapley values to guide their predictions. They explored topics and solutions in the realms of forecasting finished goods, bringing inventory under control, reducing lead times, making regional changes, efficiency and sales analysis, and regression analysis. They were first to market with industry innovations in no small part due to their data-centric approach. The transition to Qlik AutoML was a smooth and speedy one, even without a predictive analytics background. Qlik AutoML was the all-around winner in terms of time and cost savings for a robust and powerful ML platform that simply fit with Chef Work’s goals and expectations. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Analytics & Modeling - Machine Learning

Applicable Industries

Consumer Goods

Food & Beverage

Applicable Functions

Sales & Marketing

Business Operation

Use Cases

Predictive Quality Analytics

Predictive Replenishment

Demand Planning & Forecasting

Services

Data Science Services

Challenge

Chef Works, a supplier for hospitality businesses, was impacted by the economic climate driven by the global Covid-19 pandemic. The company experienced a reduction in bandwidth and a growing need to effectively practice data science with a lower time commitment. The pandemic left many businesses in the hospitality industry on uncertain ground, and Chef Works needed to make the best use of their time and resources to not only sustain themselves through these changes, but to continue to innovate in ways their customers have come to expect at a price point that would be agreeable to once-lucrative businesses now strained for cash. Chef Works understood the grim reality that, while many of their customers would see the other side of the pandemic with business in-tact, many would not. To better support those that would survive, and to understand which businesses those were most likely to be, Chef Works turned to the power of data science.

About Customer

Chef Works is an industry leader in the design development of apparel for the hospitality industries. A leader in innovation, Chef Works aims to provide the modern products their customers need at a competitive price point. They offer a wide range of products and styles to suit chefs, bartenders, servers, and other hospitality professionals with frequent new offerings that key in on industry trends. The company began their journey with Qlik® in June of 2020, during the global Covid-19 pandemic.

Solution

With Qlik AutoML, Chef Works was able to explore their data with machine learning and iterate on machine learning models in a fraction of the time it took via previous labor processes. They were able to see how Covid-19 was mostly likely to impact their customers, taking into consideration a vast array of business information including state industry and predicted sales using Shapley values to guide their predictions. They explored topics and solutions in the realms of forecasting finished goods, bringing inventory under control, reducing lead times, making regional changes, efficiency and sales analysis, and regression analysis. They were first to market with industry innovations in no small part due to their data-centric approach. The transition to Qlik AutoML was a smooth and speedy one, even without a predictive analytics background. Qlik AutoML was the all-around winner in terms of time and cost savings for a robust and powerful ML platform that simply fit with Chef Work’s goals and expectations.

Impact #1	Chef Works was able to discover not only which types of businesses had the highest rate of probable survivability but also more granular information like which products were most likely to be purchased together.
Impact #2	They achieved this with unprecedented speed thanks to Qlik AutoML’s intuitive approach that supports quickly and efficiently changing models, easily pulling in new features, and filtering.
Impact #3	Without the need to code and tune their models, Chef Works was able to use machine learning in a small fraction of the time it took to complete similar tasks manually using R, sometimes in as little as 30 minutes.

Impact #1

Chef Works was able to discover not only which types of businesses had the highest rate of probable survivability but also more granular information like which products were most likely to be purchased together.

Impact #2

They achieved this with unprecedented speed thanks to Qlik AutoML’s intuitive approach that supports quickly and efficiently changing models, easily pulling in new features, and filtering.

Impact #3

Without the need to code and tune their models, Chef Works was able to use machine learning in a small fraction of the time it took to complete similar tasks manually using R, sometimes in as little as 30 minutes.

Benefit #1	Qlik AutoML saves Chef Works as much as $10K annually in time commitment alone.

Benefit #1

Qlik AutoML saves Chef Works as much as $10K annually in time commitment alone.

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Overview

Chef Works Meets Qlik AutoML

Operational Impact

Quantitative Benefit