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Koy Clothing: Streamlining order fulfilment

Introduction

This case study revolves around the collaboration between Koy Clothing, a prominent fashion brand, and John Lewis, a major retailer in the UK. The case study focuses on two projects; undertaken to streamline the order fulfilment process between Koy Clothing and John Lewis. The primary objective was to optimize the integration of orders from John Lewis into Koy Clothing's Shopify system, ensuring seamless stock management and timely customer satisfaction. These projects aimed to improve the order fulfilment process, fully automate order acknowledgements, and integrate the delivery note creation and dispatch workflow.

In the first project, our goal was to automate and standardize the order fulfilment process using integration technology. We utilized SFTP for document exchange, with John Lewis sending CSV files through their platform: ‘The Edge’. Leveraging the CC Message Broker Service (our primary EDI/integration offering) we built a process which extracts and translates the data into JSON, which the Shopify API is able to digest. In the majority of cases, orders will be automatically acknowledged, by the automation, through a method of comparing stock levels to order quantities.

Building upon the success of the first project, the second project focused on further enhancing order processing. We introduced a new automation where the system generates PDF delivery notes as an order is acknowledged. We transmit these delivery notes to Koy Clothing's third-party logistics (3PL) system, ready to be printed.

Project 1: Order Fulfilment for John Lewis

In the first project, our focus was on optimizing the order fulfilment process for John Lewis, a key retail partner of Koy Clothing. The objective was to automate and streamline the integration of orders from John Lewis into Koy Clothing's Shopify store, ensuring efficient stock management and timely customer satisfaction.

To achieve this, we implemented a robust, automated process that involved several crucial steps. The process begins with the receipt of order documents from John Lewis, which were sent in CSV format through the Edge platform, using SFTP. Leveraging our ‘CC Message Broker Service’, we established a seamless connection to retrieve these documents automatically.

Once the documents were collected, we utilized data extraction techniques to retrieve the necessary information from the CSV files. This data extraction process was designed to accurately capture order details, such as product names, quantities, and customer information. By extracting this data, we ensured that the subsequent steps in the fulfilment process could be carried out smoothly and accurately.

Next, we translate the extracted data into JSON format, ready to be digested by the Shopify REST API. The integration of API technology ensured that the order information could be seamlessly integrated into Koy Clothing’s Shopify store.

We implemented logic that will automatically acknowledge an order, based on stock levels. To handle cases where stock availability is insufficient to automatically fulfill an order, we developed a notification system to alert Koy Clothing and suggest options to either source additional inventory or cancel the order. With the new process in place, when an order is received from John Lewis and imported into Koy Clothing's Shopify system, an automated order acknowledgement is generated. This order acknowledgement serves as a confirmation to John Lewis that the order has been received and is being processed for fulfilment.

We implemented logic that will automatically acknowledge an order, based on stock levels. To handle cases where stock availability is insufficient to automatically fulfill an order, we developed a notification system to alert Koy Clothing and suggest options to either source additional inventory or cancel the order. With the new process in place, when an order is received from John Lewis and imported into Koy Clothing's Shopify system, an automated order acknowledgement is generated. This order acknowledgement serves as a confirmation to John Lewis that the order has been received and is being processed for fulfilment.

By the end of the first project, Koy Clothing experienced improved order processing efficiency, and drastically reduced manual effort. The integration of the order fulfilment workflow resulted in a seamless and automated process, ensuring that orders from John Lewis could be efficiently managed and fulfilled in a timely manner. These improvements set the foundation for further enhancements in the second project; which aimed to enhance the order, automate the delivery note creation, and optimize the dispatch process.

Commerce-Connections have been fantastic and extremely flexible to our needs in order to create bespoke integration processes for Koy Clothing to seamlessly and automatically communicate with the John Lewis Edge Platform.”

Alastair Scott
Co-Founder, Finance & Operations Director, KOY Clothing Ltd

Project 2: PDF Delivery Note

In the second project, the objective was to further streamline the order fulfilment process by automating the creation of delivery notes.

John Lewis require a rigidly defined PDF delivery note to be sent with packages. Various data was required to be on the PDF, including rendering barcodes. To address these challenges, we implemented an enhanced process, that split off the order acknowledgement process, to create PDF delivery notes.

These delivery notes contained essential information, such as product details, quantities, and customer shipping addresses. The integration of these delivery notes into Koy Clothing's third-party logistics (3PL) system facilitated accurate dispatch details and streamlined the order fulfilment process.

Throughout the second project, our team closely monitored the implementation and collaborated with Koy Clothing to address any challenges or required modifications. The successful development of the PDF delivery notes marked a significant milestone in improving the order fulfilment process between Koy Clothing and John Lewis.