Manufacturing Sector

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The SAP HANA technology platform has become one of the fundamental pillars of innovation and generation of "data value" in more than 23,000 companies.

Explore the different cases and contact us for more information or a specific session for your business and areas of interest

Demand Signal Management & Forecasting

Business Challenge

Consumer-buying patterns are increasingly volatile
- Constant need to optimize how to approach various markets
Scattered, unstructured, and fast-growing demand and market data
- High manual effort for data harmonization
- Narrow view of the data (by market, region, distribution center)
Long manufacturing cycle out of sync with customer purchasing dynamics
How to have the right product at the right volume at the right location to meet demand

Solution

Automated process to capture incoming demand signals and data with rules to check data quality & completeness
Align external data with internal business data to align wafer start plans, product in the manufacturing flow, modeled test outs, product in the distribution network, and location to actual demand
Improve the usability of incoming data with personalized Standard UIs and adhoc reporting capabilities

Benefit

Increase profitability, revenue and market success
Improve market insights by using holistic information of the market
Reduce out-of-stock, transit, and warehousing costs by having the right product in the right location to meet the demand

Automated Sales Forecasting

Business Challenge

SLA contracts are in place with penalties in case of late deliveries
Cost of extra stock or extra freight capacity to meet SLAs and demand can be very high
Ad-hoc increases to capacity (production volume, weight and volume freight reservation) can significantly increase costs; advance planning is required
Market dynamics are different per geographical region and require separate planning

Solution

Automate historical data collection and consolidation in one platform using HANA
Automate the short-mid term model creation across all required product/ market/ route combinations using Predictive Analytics Modeler scripts
Evaluate model accuracy using Predictive Analytics
Analyze forecasts and past performance using SAP Lumira

Benefit

Enable fast, agile and accurate automatic forecasting across a large number of combinations
Facilitate planning of optimum capacities (production volume, weight and volume freight reservation)
Improve production and stock holding costs
Provide insights for better price negotiations on popular routes
Adjust response dynamically per market/ product

Increase Operational Efficiency with AIN

Business Challenge

No standardized or shared asset information between supplier and enterprise
No common asset definition or description, leading to inefficient and inconsistent collaboration
No systematic and enterprise-wide collaboration on asset management processes with business partners
Lack of complete and consumable asset information when specifying, purchasing, or using equipment, undermining smart decision making

Solution

A Global Registry of industrial assets and equipment built and shared between multiple parties across the value chain
Enable new business models by simplifying the exchange of asset information to all parties
Enable a platform for OEM and service providers

Benefit

Lower Asset Lifecycle costs
Higher Asset Availability
Reduce master data maintenance effort
All critical providers have access to the same data

Predictive Maintenance & Services

Business Challenge

Reduce the cost and lost productivity of planned maintenance and the risk associated with unplanned equipment maintenance and service
Realize the efficiency gains from a maintenance platform that recommends preventive maintenance based on actual performance data rather than fixed calendar schedules
Identify best maintenance practices across multiple sites

Solution

Combine equipment IoT and SAP business data to provide new insights into cause-andeffect, performance, cost, and reliability
Overcome poor data quality and operate at scale
Uncover new insights using predictive algorithms
Demonstrate useful results for EM maintenance analysts to drive new better practices

Benefit

Identify better maintenance practices that will reduce unplanned downtime
Anticipate and predict faults and issues before actual unplanned tool or factory down
Drive insight to action, moving to data-driven outlier detection
Better positioning of service assets
Improved engineer productivity

SAP Master Data Governance, EAM Extension by Utopia

Business Challenge

Improve master data quality through central governance
Gain consistent insight into procurement, merchandising, and sales volumes
Accelerate access to up-to-date master data across the extended enterprise
Track changes with full who-what-why transparency

Solution

Predefined and accurate master data model for enterprise assets leading to increased productivity
Make data maintenance straightforward and efficient. Set up the appropriate authorizations to enforce security across your business
Achieve accurate, time-based reporting. Manage your assets more effectively, based on accurate, up-to-date information
Control access and track changes. Configure the change authorization process to suit your needs
Manage enterprise asset data proactively

Benefit

Increase productivity and improve safety
Ensure that assets run in a productive manner
Ensure that assets run in a productive manner
Improved visibility of maintenance requirements helps to ensure nothing is missed

Overall Equipment Effectiveness (OEE)g

Business Challenge

In a typical manufacturing process, much of the processing time is lost to tasks such as loading, waiting, or repairs, and only a small amount is actually spent upon creating the end product (value operating time). Understanding and minimizing these losses is key when trying to optimize the process
Accurate logging of loading time, net production time, net operating time and value operating time is needed
Granular level of detail is needed to identify inefficient production steps

Solution

SAP MII and SAP EAM, and IoT
Provides a comprehensive loss analysis tool for the manufacturing shop floor
Connects shop-floor systems with business operations and delivers actionable intelligence to production personnel
Calculates the efficiency of key success drivers, and allows drill-down at multiple levels up to single pieces of equipment
Real-time visibility for measuring and controlling performance across all plants and the enterprise, which directly improves the bottom line
Operational improvement from the bottom up by minimizing and reducing adverse events contributing to plant performance issues

Benefit

Reduced manufacturing cost through loss reduction
Maximize return on capital investment by improving utilization
Increased effectiveness due to higher equipment availability, speed of production and better quality

Digital Twin for Manufacturing Operations

Business Challenge

Some of the greatest efficiency losses in the mill industry, often related to energy usage, are due to equipment failure and breakdown
Learning about problems and equipment failures after they happen can be very costly

Solution

A ‘Digital Twin’ - the live digital representation or software model of a connected physical object
Network of sensors that monitor the efficiency of each piece of equipment, allowing to identify parts that are wearing out or are failing by using just a tablet or laptop
A digital twin shows the efficiency of a machine as it works in real time and compares that information to the machine’s past performance

Benefit

Presents decision-makers with real time data from performance monitoring sensors which can detect, store and communicate a wide variety of conditional observations, from temperature to vibration or pressure
The digital twin can also increase efficiency of data-usage by removing non-critical information and processing basic information into a format that can be consumed by the shop floor personnel
The digital twin’s profile can be turned into a benchmark standard (baseline), which is used to identify anomalies. Know when a machine is on its way to failure - early enough in the process to be able ot order spare parts and schedule maintenance

Connected Manufacturing

Business Challenge

Real-time enterprise-wide connectivity – Live response in production planning and scheduling for high flexibility with last minute demand changes
- Manufacturing execution & orchestration
Seamless tool-to-tool communication across factories, partners and suppliers
eCommerce integration: Sales order entry with configuration and personalization for individualized products
Machine cloud integration, direct replenishment, outsourcing

Solution

Connected manufacturing connects the worlds of machines and business via, cloud, open APIs and specialized connectors with numerous MES vendors
Enable real-time tracking of product quality per production step, paperless manufacturing, process stability, OEE Analysis, 3D Printing, network collaboration…

Benefit

Improved order fulfillment
Improved throughput with less scrap
Improved flexibility/bottleneck planning
Reduced complexity, fewer constraints

Predictive Quality Management

Business Challenge

Being unable to adjust or respond to production problems (both material and operational) results in inconsistent product quality
Increased expenses to rework the product, or in some cases disposal of the product
Reduce time-to-quality on product switchover

Solution

Predictive quality management means looking at historical data of a product and then developing a predictive model based on the data
Monitor and guarantee quality standards continuously instead of through one lab measurement at the end
Calculate in real-time what the quality of the product that is currently n production will be
Replace physical quality test by forecasts, thereby reducing QM costs

Benefit

Detect quality defects in products before going in full-scale production allows for in-process adjustments to be made to bring the product back into specification
This increases first pass yields, reduces necessary rework, and decreases waste products
Being able to predict the quality of the product based on production line decisions enhances the scheduling process

Quality Complaints & Feedback Analysis

Business Challenge

The continuous pursuit to improve product quality to deliver even greater and more consistent value to customers
Performance across manufacturing and supply chain operations can impact product quality (complaints / feedback / recalls) and there is generally little understanding of the relationships between these factors
Relevant data is often stored in siloed data sources with poor quality and little consistency

Solution

An agile Quality Data Warehouse to explore the relationships, cause and effects of complaints and feedback to manufacturing deviations. Consideration was also given to manufacturing changes and product recalls
Complaints and deviations were ingested, modelled in net-new ways and predictive models developed to explore and identify the root causes of complaints and their relationship to deviations
New insights including problematic internal factors locations and product pack types were highlighted and predictive models were developed to determine the impact of deviations

Benefit

Identified relationships between quality outcomes (e.g. complaints) and internal factors (e.g. deviations) are used to focus efforts for learning and improvement
Combining data in novel new ways derived significantly extra business value and actionable insight

Connected Worker Safetyn

Business Challenge

Being unable to gather worker safety incidents, issues, or near misses quickly and completely allows potential safety problems to remain unaddressed
Workers often do not report near misses due to the additional workload involved (triggering investigations, writing reports, etc.)

Solution

SAP Predictive Analytics, SAP EHS&M, SAP Data Hub, SAP Visual Enterprise

Benefit

Automatic gathering of safety incidents, worker health, and location via embedded sensors on protective equipment (PPE) and environmental systems, along with monitoring capabilities allows a company to quickly respond to incidents, and to ensure that all incidents are fully captured
Utilizing Predictive Analytics to identify where, when and under what circumstance incidents are more likely to occur allows companies to be proactive in resolving and preventing incidents in the first place
Reduction in the number, cost, and severity of incidents

Intelligent Enterprise – Invoice Matching

Business Challenge

Efficient handling of a large number of incoming invoices for transportation orders, equipment and spare parts, or raw materials
Automated matching of invoices and payments would significantly reduce the effort involved in the process
Hard-coded matching rules are hard to maintain, as they quickly become outdated, and due to differences in the various global regions

Solution

SAP Cash Application system running on SAP Leonardo Machine Learning
Continually learns from accountant behavior using SAP Leonardo Machine Learning and applies it to future payments
Automates and improves accuracy of the labor-intensive and repetitive clearing process
Learn from historical payment patterns and automatically clear payments
Machine learning drives automation in accounts receivable, by matching payments to open invoices
Can be scheduled as single run or a recurrent job

Benefit

Machine Learning can improve the invoice matching to a >90% automation level
Enabling accountants to focus on complicated cases that can't be cleared automatically
Faster processing of incoming payments reduces DSO and improves customer service

Operational Data Analytics with OSIsoft PI

Business Challenge

Being unable gather operational data in a timely fashion slows down decision making and can result in decision based on a lack data
Not being able to perform timely comparisons inhibits timely problem identification and resolution
Unable to identify corporate best practices due to siloed information

Solution

SAP Predictive Analytics, SAP MII, OSISoft PI Server, OSISoft S/4HANA Connector, SAP Data Hub, SAP Machine Learning, SAP AIN

Benefit

Automatic gathering of operational data throughout the corporation enables companies to quickly identify problems
‘Real-time’ reporting, compare operations across the company to identify ‘best in class’, ability to compare assets to identify bad performers (equipment classes, equipment models, suppliers)
By resolving problems before they become critical, savings are generated by reducing maintenance costs, improving product throughput, improving product quality, and reducing production costs and waste

Digital Operations Room

Business Challenge

Factory information from emails and notifications can be out-of-date to what is available via phone – and in effect businesses either react to aged data or override information systems to meet business demands
Need to be able to run business simulations, where the adverse effects of various options are played out – on current information

Solution

Consolidated 360º views of critical business, sales, revenue, demand, manufacturing, and customer KPIs
Real-time with live access to data sources without requiring moving or manual data manipulation
Visualizations leverage three very large interlinked touch screens, to enable decisionready insights

Benefit

Total transparency and view into the business for decision makers
Instant data-driven insights drawn from live data
More efficient and action driven meetings

Process Mining

Business Challenge

Gain a transparent view of actual as-is business processes i.e. how the business is running
Identify process deviations and weaknesses to ensure compliant operations which is an imperative for organisations operating in regulated and validated environments
Measure and control improvement business process initiatives and remedy cost-intensive or inefficient processes

Solution

SAP Process Mining by Celonis helps organizations to improve their bottom line and increase the efficiency of core business processes by up to 30%
Leverages the power of SAP HANA, its highly efficient algorithms can analyse vast amounts of transactional data in real-time facilitated by easy to use end-user visualizations

Benefit

Complete transparency and visualization of actual as-is processes and their multiple variants
Identification of previously unknown process weaknesses and areas for improvement
Higher process quality and reliability for improved compliance
Accelerated SAP S/4 HANA process migrations and consolidation / standardisation initiativesr

Business Process Mining

Business Challenge

Existing business process re-engineering approaches are slow, costly, and yield diminishing results
No real capability to consistently track sub-process lead-times and any variance – especially those that cross multiple systems and geographies
Todays high rate of organizational change make continuous business process improvements more difficult

Solution

Automatically extract transactional business process data and visualize how processes are executed in reality (system agnostic)
Uncovers all hidden bottlenecks, deviations, and critical fraud patterns
Instantly analyzes throughput times, critical vendors, automation rates, material flows, product flows etc.
Intuitive UI and navigation allows for zooming in and out of processes and custom KPIs
Automatically monitor adherence to corporate process policy
Advanced machine learning enabled automated process insights and transparency

Benefit

Up to 30% improvement in business process cost by eliminating bottlenecks
Benchmarks the most efficient process paths and variants
Identifies critical fraud patterns improves efficiency and compliance
Optimize, harmonize, and standardize business processes