Supply Chain

Introduction

Maintainer: regardt_jacobs@bmc.com

Control-M is a versatile tool that can significantly enhance supply chain operations through its effective workflow automation capabilities. By leveraging Control-M, supply chain managers can streamline operations, reduce lead times, and improve overall supply chain efficiency, ultimately leading to higher customer satisfaction.

Use Case Overview

In the BFSI industry, an efficient supply chain is crucial for timely and accurate data delivery which supports decision-making processes. This includes the movement and management of sensitive financial data, compliance with regulatory standards, and ensuring data integrity through every step of the pipeline. Control-M can orchestrate end-to-end data workflow automation, ensuring data is collected, processed, and delivered accurately and efficiently.

The Consensus Recording for this use case can be found here: Watch this video!

Use Case Technical Explanation

Data Source Retrieval

Control-M schedules and automates the extraction and aggregation of stock data from multiple data sources, like:

SAP ERP
CRM systems (e.g. Oracle CRM On Demand, Microsoft Dynamics 365)
Stock File Sources for Automated Storage Retrieval Systems and Store goods

In the zzz-supply-chain use case, there are 3 sub folders relevant to this Data Source Retrieval step; Arriving-Goods which includes a job designed to demonstrate connecting to the SAP Application Server or SAP Message Server, to export arriving goods for further processing, Retrieval_Sytem_Inventory which includes a job designed to demonstrate the process of recieving goods at the warehouse through a file transfer job that pulls order details or customer requests, and Store_Goods which includes a job designed to demonstrate storing the inspected goods in a designated location within the warehouse by recieving the inventory stock from stores through an embedded script with build in logic to retrieve barcode information, store information on local registers, old vs new stock and where empty warehouse slots are located.

Data Collection, Data Processing, and Data Storage

In the zzz-supply-chain use case, there is 1 subfolder, Supply_Chain_Consolidation_Processing, relevant to this step of Data Collection, Data Processing, and Data Storage to demonstrate the consolidation and processing of supply chain data from multiple sources and delivering this data for further use through 3 simultaneous workflow streams. These workflow streams exemplify the triggering functions in the Inventory Management System to ensure up to date stock inventory and applying algorithmns to trigger warehouse management processes from the Order Management System to kick off picking stock for distribution to relevant stakeholders and send notification to auomated guided vehicles to start the process and transport of stock items.

Stream 1) includes jobs designed to demonstrate the triggering and monitoring of data consoliation through an SSIS Package execution on a MSSQL database to clean the data, processing this clean data via a AWS Lambda trigger, and loading this processed data into a Inventory Management System via an SAP R3 execution to update and place new stock inventory orders.
Stream 2) includes creating an EC2 instance for additional infrasturature required for data processing via an AWS EC2 job.
Stream 3) includes jobs designed to demonstrate the triggering and monitoring of data management and integration tasks and plans from Talend via a Talend Data Management job, loading the prepared data from Talend into a Snowflake data warehouse via a Snowflake job, and replenishing packing stations via a AWS Step Functions job. for the Inventory Management System.

Data Consumption

In the zzz-supply-chain use case, the zzz-stockorder-refresh job demonstrates the delivering insights step of refreshing stock data, via AWS QuickSight, after an order has been placed to be prepare for the next order within the Order Mangement System.

Audit Compliance and SLA Reporting

Service Level Agreement (SLA) management in the supply chain is crucial to ensure that all stakeholders meet specified performance standards, improving customer satisfaction and operational efficiency. By using Control-M for SLA management in the supply chain, organizations can ensure timely and efficient operations, maintain high levels of customer satisfaction, and continuously improve their performance against established SLAs.

To view the demo flow code-base, and all artifacts, please navigate to the Supply Chain Git Repository

Job Types Included

Control-M SAP BW
Control-M SAP R3
Control-M Managed File Transfer
Control-M OS
Control-M Databases
Control-M for AWS EC2
Control-M for Talend Data Management
Control-M for AWS Lambda
Control-M for AWS Step Functions
Control-M AWS Quicksight
Control-M for Snowflake
Control-M SLA Management

Demo Environment Information

Environment	Status	Folder
Helix Production	Available	zzz-supply-chain
Helix Pre-Production	Available	zzz-supply-chain
VSE CTM PROD	Available	zzz-supply-chain
VSE CTM QA	Available	zzz-supply-chain