Case Studies Innovation with Style: How Zalando Enhances Procurement with ML-powered Suggestions

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	Innovation with Style: How Zalando Enhances Procurement with ML-powered Suggestions

Innovation with Style: How Zalando Enhances Procurement with ML-powered Suggestions

Technology Category	Analytics & Modeling - Machine Learning Application Infrastructure & Middleware - Data Exchange & Integration Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries	E-Commerce Retail
Applicable Functions	Business Operation Procurement
Use Cases	Predictive Replenishment Process Control & Optimization
Services	Software Design & Engineering Services System Integration
Challenge	Zalando faced challenges in optimizing their procurement processes, which included issues like maverick ordering and deviations from standard procedures. The company needed a solution to streamline these processes, reduce operational tasks, and improve overall efficiency. Additionally, they aimed to enhance supplier analytics to better understand supplier billing behavior and payment terms, ultimately improving working capital management. Read More
About Customer	Zalando is a leading European e-commerce company specializing in fashion and lifestyle products. With a strong focus on customer centricity and data-driven decision-making, Zalando aims to solve important challenges in the retail industry. The company operates across multiple countries in Europe and has a large workforce dedicated to providing a seamless shopping experience for its customers. Zalando's commitment to innovation and efficiency drives its continuous efforts to enhance various aspects of its operations, including procurement. Read More
Solution	To address these challenges, Zalando implemented Celonis for process mining and automation. This solution enabled the company to optimize procurement processes by identifying inefficiencies and automating tasks such as eInvoicing and eCatalogues. The Machine Learning Action Engine provided guided buying recommendations, reducing operational tasks and improving decision-making. Additionally, Celonis' supplier analytics capabilities allowed Zalando to gain insights into supplier billing behavior and payment terms, enhancing working capital management. The implementation of process governance measures helped mitigate maverick ordering and deviations from standard procedures, ensuring compliance and consistency. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Application Infrastructure & Middleware - Data Exchange & Integration

Functional Applications - Enterprise Resource Planning Systems (ERP)

Applicable Industries

E-Commerce

Retail

Applicable Functions

Business Operation

Procurement

Use Cases

Predictive Replenishment

Process Control & Optimization

Services

Software Design & Engineering Services

System Integration

Challenge

Zalando faced challenges in optimizing their procurement processes, which included issues like maverick ordering and deviations from standard procedures. The company needed a solution to streamline these processes, reduce operational tasks, and improve overall efficiency. Additionally, they aimed to enhance supplier analytics to better understand supplier billing behavior and payment terms, ultimately improving working capital management.

About Customer

Zalando is a leading European e-commerce company specializing in fashion and lifestyle products. With a strong focus on customer centricity and data-driven decision-making, Zalando aims to solve important challenges in the retail industry. The company operates across multiple countries in Europe and has a large workforce dedicated to providing a seamless shopping experience for its customers. Zalando's commitment to innovation and efficiency drives its continuous efforts to enhance various aspects of its operations, including procurement.

Solution

To address these challenges, Zalando implemented Celonis for process mining and automation. This solution enabled the company to optimize procurement processes by identifying inefficiencies and automating tasks such as eInvoicing and eCatalogues. The Machine Learning Action Engine provided guided buying recommendations, reducing operational tasks and improving decision-making. Additionally, Celonis' supplier analytics capabilities allowed Zalando to gain insights into supplier billing behavior and payment terms, enhancing working capital management. The implementation of process governance measures helped mitigate maverick ordering and deviations from standard procedures, ensuring compliance and consistency.

Impact #1	The implementation of Celonis led to significant improvements in procurement efficiency and speed, allowing Zalando to focus more on value creation.
Impact #2	The use of machine learning and automation reduced the burden of operational tasks, enabling employees to concentrate on strategic activities.
Impact #3	Enhanced supplier analytics provided valuable insights into supplier behavior, leading to better negotiation of payment terms and improved working capital management.

Impact #1

The implementation of Celonis led to significant improvements in procurement efficiency and speed, allowing Zalando to focus more on value creation.

Impact #2

The use of machine learning and automation reduced the burden of operational tasks, enabling employees to concentrate on strategic activities.

Impact #3

Enhanced supplier analytics provided valuable insights into supplier behavior, leading to better negotiation of payment terms and improved working capital management.

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Overview

Innovation with Style: How Zalando Enhances Procurement with ML-powered Suggestions

Operational Impact