Istanbul, 34445,
Turkey
Arçelik A.Ş. Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Arçelik A.Ş. and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 30000 Arçelik A.Ş. employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Arçelik A.Ş. has purchased the following applications: Microsoft Azure Machine Learning for ML and Data Science Platforms in 2016, Acquia Commerce Manager for eCommerce in 2018, Oracle Moat for Marketing Analytics in 2019 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Arçelik A.Ş. is running and its propensity to invest more and deepen its relationship with Microsoft , Acquia , Oracle or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing Arçelik A.Ş. revenues, which have grown to $1.94 billion in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Arçelik A.Ş. intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
AI Development
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Microsoft | Legacy | Microsoft Azure Machine Learning | ML and Data Science Platforms | AI Development | n/a | 2016 | 2017 |
In 2016, Arçelik A.Ş. implemented Microsoft Azure Machine Learning as part of a solution in the ML and Data Science Platforms category to automate and scale spare-parts demand forecasting. The initiative addressed an outdated forecasting environment that relied on ten disparate data collection systems and manual spreadsheets while supporting a spare-parts catalog of roughly 350,000 SKUs across the company’s global service footprint.
The deployment architecture centers on Azure SQL Database for historical and monthly demand data ingestion, Azure Machine Learning to experiment with and select forecasting algorithms, and Azure Data Factory to automate the testing and update pipelines. Microsoft Azure Machine Learning executes automated model selection by testing four candidate algorithms, applies time-series R code developed and packaged by BilgeAdam, and produces rolling 12-month forecasts which are refreshed as new monthly data is ingested.
The implementation integrates external signals such as weather and location into the forecasting data set to improve demand signal richness, and it extended coverage from roughly 100,000 SKUs to forecasts for all 350,000 spare-part SKUs. Operational ownership spans customer service, spare parts teams, purchasing, logistics, and IT, supporting service call scheduling and global parts availability across markets where Arçelik A.Ş. operates.
Governance and process changes include a monthly data upload cadence that feeds automated experimentation and model selection, plus a shift in forecast production frequency from biweekly or multiweek runs to weekly forecast publication. The project was delivered rapidly, going live in three months, and was implemented with BilgeAdam as the solution provider, leveraging Azure pay-as-you-go economics to avoid heavy upfront infrastructure and long development timelines.
Explicit outcomes documented after six months in production include forecast accuracy reaching up to 80 percent from about 60 percent, SKU coverage expanding to 350,000 parts, and an expectation that improved forecasting will increase inventory turnover by 10 percent by 2019. The faster, more reliable forecasts reduced time to produce forecasts from two or three weeks to weekly cycles, enabling earlier service call completion and contributing to improved customer satisfaction.
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eCommerce
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Acquia | Legacy | Acquia Commerce Manager | eCommerce | eCommerce | n/a | 2018 | 2018 |
In 2018, Arçelik A.Ş. implemented Acquia Commerce Manager as its eCommerce platform. The deployment established a centralized storefront and commerce capability to support the companys online merchandising and digital sales channels, aligning product catalog and customer facing content under a single commerce application.
Acquia Commerce Manager was configured to deliver core eCommerce functional modules including product catalog management, pricing and promotions configuration, checkout and payment orchestration, and order capture workflows. The implementation emphasized modular configuration and an API oriented architecture to enable content commerce convergence, supporting content driven product pages and managed merchandising workflows within the same application.
Operational responsibility for the Acquia Commerce Manager implementation was organized around commerce operations, marketing, and merchandising functions, with governance focused on centralized product data stewardship and staged release controls for online promotions and catalog updates. The narrative reflects an enterprise scale deployment at Arçelik A.Ş., where Acquia Commerce Manager provides the platform foundation for online commerce, merchandising, and digital customer experience management.
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CRM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Oracle | Legacy | Oracle Moat | Marketing Analytics | CRM | n/a | 2019 | 2019 |
In 2019, Arçelik A.Ş. deployed Oracle Moat for Marketing Analytics on its corporate website. The implementation used page-level site tagging to capture display and video ad impressions and to instrument third-party measurement across web properties.
Oracle Moat was configured to surface viewability and attention metrics along with ad verification signals, enabling standardized campaign measurement and consistent reporting. Configuration work focused on measurement tagging, pixel placement, and mapping Moat metrics into existing marketing analytics workflows to support media planning and campaign validation.
Integration was executed through website instrumentation and analytics tagging, with Oracle Moat data routed into marketing and media measurement processes. Arçelik A.Ş. Oracle Moat Marketing Analytics supports marketing and media measurement functions, and the tool was embedded in campaign reporting workflows to provide an independent measurement layer for advertising performance.
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IaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Digital Workspace | IaaS |
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2013 | 2013 |
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