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Arnold Motor Supply Data, Technology Stack, and Enterprise Applications
Analytics and BI
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Kerridge Commercial Systems Limited Legacy MAM Software Phocas BI Analytics and BI Analytics and BI n/a 2015 2018
In 2015 Arnold Motor Supply began evaluating Autopart MAM and integrated Phocas BI, deploying a MAM Software Phocas BI solution to address inventory visibility and financial analytics across its wholesale operations. The evaluation initiated in 2015 led to a staged implementation that reached full rollout by July 2018 across branches and distribution centers. The implementation combined Autopart MAM application workflows with Phocas BI reporting, leveraging the MAM Software Phocas BI configuration to surface inventory management metrics, warehouse operations data, and financial reporting. Functional capabilities implemented included inventory reporting and analytics, warehouse picking and fulfillment visibility, and manager self-service reporting consistent with an Analytics and BI deployment. Integration scope centered on feeding transactional and inventory data from Autopart MAM into Phocas for dimensional analysis and dashboarding, enabling branch and distribution center level reporting and consolidated views for finance and operations. The Phocas integration and deployment approach is documented in the Klipboard case study, which details the connection between MAM transaction feeds and BI visualization layers. Governance and rollout followed a multi-year program with phased branch rollouts, transitioning managers to self-service reporting and updating warehouse picking workflows to consume BI insights. Explicit outcomes reported at rollout included better picking accuracy, improved inventory turns, and broader self-service reporting for managers, with the MAM Software Phocas BI combination supporting inventory, warehouse, and financial business functions.
IaaS
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Amazon Web Services (AWS) Legacy Amazon EC2 Application Hosting and Computing Services IaaS n/a 2021 2021
Cloud Storage IaaS 2022 2022
Content Delivery Network IaaS 2021 2021
Content Delivery Network IaaS 2021 2021
Content Delivery Network IaaS 2022 2022
ERP Services and Operations
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Kerridge Commercial Systems Limited Legacy MAM Software Autopart Online Automotive ERP ERP Services and Operations n/a 2015 2015
In 2015, Arnold Motor Supply implemented MAM Software Autopart Online as its Automotive ERP to consolidate store operations, inventory control, warehouse processing and accounting. The decision followed a multi-year evaluation driven by limitations in a heavily customized point-of-sale environment and a separately managed accounting package, and Arnold Motor Supply selected the Kerridge Commercial Systems Limited solution for its SQL-based architecture and integrated feature set. MAM Software Autopart Online was configured to provide core Automotive ERP capabilities including order entry, purchasing, inventory management, accounts payable, accounts receivable and general ledger processing, with a native, real-time ledgers package. The deployment included an integrated Warehouse Management System to support receiving, picking, packing, cycle counting and performance reporting, and the Autocat eCatalog was adopted for point-of-sale catalog lookup and VIN-driven part identification. The implementation leveraged the Microsoft SQL foundation of Autopart to expose data via SQL views and enable custom reporting, and it was integrated with third-party solutions explicitly listed by the customer, including GCommerce VIC and Phocas Business Intelligence. By July 2018 the solution was live across 72 profit centers with over 400 active users, supporting store managers, purchasing teams, counter professionals and distribution center staff across Arnold Motor Supply’s branch and distribution footprint. Governance and rollout emphasized training and vendor support from Kerridge Commercial Systems Limited to manage conversion complexity and user adoption, and operational access was extended through the company portal and Excel-driven analysis for business users. Outcomes documented by the customer include improved access to system data and metrics, tighter integration between store operations and accounting, improved picking accuracy and enhanced inventory turn visibility, and faster, more accurate point-of-sale lookups via Autocat.
Collaboration
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Collaboration Collaboration 2014 2014
Collaboration Collaboration 2022 2022
CRM
Vendor
Previous System
Application
Category
Market
VAR/SI
When
Live
Insight
Marketing Automation CRM 2021 2021
Tag Management CRM 2021 2021
IT Decision Makers and Key Stakeholders at Arnold Motor Supply
First Name Last Name Title Function Department Email Phone
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Apps Being Evaluated by Arnold Motor Supply Executives
Date Company Status Vendor Product Category Market
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FAQ - APPS RUN THE WORLD Arnold Motor Supply Technographics

Arnold Motor Supply is a Automotive organization based in United States, with around 800 employees and annual revenues of $100.0 million.

Arnold Motor Supply operates a diverse technology stack with applications such as MAM Software Phocas BI, Amazon EC2 and MAM Software Autopart Online, covering areas like Analytics and BI, Application Hosting and Computing Services and Automotive ERP.

Arnold Motor Supply has invested in cloud applications and AI-driven platforms to optimize efficiency and growth, collaborating with vendors such as Kerridge Commercial Systems Limited and Amazon Web Services (AWS).

Arnold Motor Supply recently adopted applications including Amazon S3 in 2022, Amazon Elastic Load Balancing (ELB) in 2022 and Slack Connect in 2022, highlighting its ongoing modernization strategy.

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Our research team continuously updates Arnold Motor Supply’s profile with verified software purchases, vendor relationships, and digital initiatives identified from public and proprietary sources.

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