Advanced Analytics for SAP workloads on Microsoft Azure

By Rishi Arora, Cloud Solutions Architect

Today, many SAP workloads worldwide are being migrated to Microsoft Azure from on-premises environments for reasons that include costs, driving efficiencies, and rapid scaling needs. 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.

Customers may wonder what Azure Cloud can do for their workloads once their SAP data has been moved to Azure. I will explain the possibilities in this post.

There is a tremendous opportunity right now in generating actions and insights through Advanced Analytics and Machine Learning with SAP-sourced data. The problem is that oftentimes company employees who analyze SAP-sourced data aren't data scientists or data wranglers. Cortana Intelligence Suite, which is part of Azure, empowers employees by eliminating the need to code. Instead, data specialists can use an easy-to-learn component like Azure Machine Learning to create predictive models using SAP-sourced data.

What's even more compelling about using predictive models for SAP data is that most of the source data comes from standard tables and fields that are used by many companies in the same industry. In other words, once a partner or system integrator creates a predictive model for one customer using data from SAP standard tables, they can potentially re-use the model for another customer in the same vertical. This allows for faster solution deployment and delivery.

SAP also has applications and tools that can deliver Machine Learning experiments. But with SAP one needs to rely heavily on data scientists to program and deliver solutions that will potentially have long delivery cycles.

Azure delivers on the digital transformation promise while making life easier for customers. SAP Enterprise Resource Planning (ERP) data is separated by process or module: Sales and Distribution (SD), Supply Chain Management (SCM), Finance & Controlling (FICO), Customer Relationship Management (CRM), and Human Capital Management (HCM). Similarly, our partners 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 our partners can build for their SAP customers:

Human Capital Management (HCM)

  • Match job postings to candidate profiles
  • Identify and track potential bias in talent acquisition and management processes

Sales and Distribution (SD)

  • Analyze social media feedback and decide how to respond to customers
  • In product development, correctly assess the demand and net sales realization using cluster analysis

Supply Chain Management (SCM)

Demand analytics – How is my forecast tracking with actual sales?

  • Detailed demand forecasting at the point of sale (store level, retailer, distribution channel roll-up)
  • Deviation analysis of forecast versus actual at the SKU level
  • Integration with promotional events and holidays to fine tune the forecast

Impacts: Forecast accuracy, in-store availability, lost sales

Finished inventory optimization – What stock should I hold and where should I invest?

  • Inventory budget optimization
  • Safety stock level recommendations
  • Segment inventory for tailored and customized fulfillment strategies by customer type

Impacts: Inventory cost, customer service levels

Replenishment planning analytics – What, when, and where should I ship?

  • Integrated planning at the retailer, distributor, and channel level
  • Optimize fulfillment logistics to account for handling, storage or warehouse constraints

Impacts: In-store availability, customer service levels

Network planning and optimization – Do I have the right network of manufacturing and warehousing facilities?

  • Number of physical plants
  • Optimized flow paths to fulfill different segments of customer demand at the lowest total cost

Impacts: Fixed and variable costs of operations

Transportation Analytics – Optimizing transportation routes and loads, including contract compliance

  • Optimizing routes including backhaul
  • Optimizing shipment schedules
  • Maintaining compliance with transportation contracts

Impacts: Freight costs, equipment utilization, contract compliance

Procurement Analytics – How to achieve the lowest landed cost and secure long-term high-quality supplier partners?

  • Scoring models for vendor quality, cost, and stability.

There is a huge opportunity for our partners to address SAP workloads opportunities with Predictive Analytics. Partners with SAP expertise are developing capabilities around Azure Machine Learning and other advanced analytical components in Azure. Be one of the first to deploy repeatable models and publish your vertical solutions in AppSource.

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