Recently many SAP workloads throughout the world are being migrated to Microsoft Azure from on-premise environments because of low expenditure, high efficiency and rapid scaling requirements. Lift and shift deployments of SAP Business Suite 4 SAP HANA (SAP S/4 HANA), SAP Business Warehouse (BW) on HANA, and SAP HANA Sidecar onto Azure take place every day. There is a tremendous opportunity right now in generating actions and insights through Advanced Analytics and Machine Learning with SAP-sourced data on Azure. First lets start by understanding the basics.
What exactly is Advanced Analytics?
Advanced Analytics uses mathematical, probabilistic and statistical modeling techniques and enables predictive processing and automated decision making. It goes beyond the historical reporting and data aggregation of traditional business intelligence (BI). Advanced analytics solutions usually involve the following steps:
- Interactive data exploration and visualization
- Machine Learning model training
- Real-time or batch predictive processing
Most advanced analytics architectures include some or all of the following components:
- Data storage
- Batch processing
- Stream processing
- Analytical data store
- Analysis and reporting
- Real-time message ingestion
What are the features and benefits of Microsoft Azure?
- SAP sourced with Advanced Analytics and Machine Learning – It has created many opportunities in generating actions and insights but does not make the process of analyzing easier for employees who are not data scientists. Azure has Cortana Intelligence suite which eliminates the need to code subsequently helping the employees. Instead, data specialists can use an easy-to-learn component like Azure Machine Learning to create predictive models using SAP-sourced data.
- Predictive models for SAP data which come from standard tables and fields are used by many companies in the same industry. Thus, the predictive model used for one customer using data from standard SAP tables can be used for another customer in the same vertical. This allows faster solution deployment and delivery.
- Extract more intelligence from the SAP data – Unlike SAP, Microsoft has AI and advanced analytics that can extract more intelligence from the SAP solution environments. When SAP integrates with tools like Office 365, Power BI and Cortana Intelligence, productivity and analytics reporting increases. Moreover, enterprises can find additional apps and support to help them succeed with their SAP workloads as they can access a deep Microsoft ecosystem.
- Transform Digitally – With SAP one needs to rely heavily on data scientists to program and deliver solutions that will potentially have long delivery cycles despite having applications and tools that can deliver Machine Learning experiments. Azure, on the other hand, delivers on the digital transformation promise while making life easier for customers.
SOME REAL LIFE SCENARIOS FOR YOU TO UNDERSTAND BETTER
Industries can build repeatable predictive models either with data for a given SAP module, cross module, or cross application, including data sourced outside of SAP.
Here are some industry examples of what they can build for their SAP customers:
Human Capital Management (HCM)
- It matches job postings to candidate profiles
- It identifies and tracks potential bias in talent acquisition and management processes
Sales and Distribution (SD)
- It analyzes social media feedback and decides how to respond to customers
- Using cluster analysis, it correctly assess the demand and net sales realization in product development.
Supply Chain Management (SCM)
- It gives detailed demand forecasting at the point of sale (store level, retailer, distribution channel roll-up)
- It provides deviation analysis of forecast versus actual at the SKU level
- It integrates with promotional events and holidays to fine tune the forecast
It helps with forecast accuracy, in-store availability and lost sales.
Finished Inventory Optimization
- It provides inventory budget optimization
- Gives safety stock level recommendations
- Segment inventory for tailored and customized fulfillment strategies by customer type
It has an impact on Inventory cost and customer service levels.
Replenishment Planning Analytics
- Sees to it that planning is integrated at the retailer, distributor, and channel level
- It optimizes fulfillment logistics to account for handling, storage or warehouse constraints
In short, it influences In-store availability and customer service levels.
Network Planning and Optimization
- Number of physical plants
- Optimizes flow paths to fulfill different segments of customer demand at the lowest total cost
It has an impact on fixed and variable costs of operations
- It optimizes routes including backhaul
- It Optimizes shipment schedules
- It maintains compliance with transportation contracts
It has an effect on freight costs, equipment utilization and contract compliance
- It scores models for vendor quality, cost, and stability.
There is a huge opportunity for customers to address SAP workload opportunities with Predictive Analytics. Customers with SAP expertise are developing capabilities around Azure Machine Learning and other advanced analytical components in Azure.
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