2. The CompanyAgenda
Internet of Things: introduzione
L’IoT per il Manufacturing: Industry 4.0 e Connected
Manufacturing
Soluzioni SIDI per il Connected Manufacturing
Esempi di realizzazioni (fonte SAP)
4. The CompanyInternet of Things connette informazioni, persone e cose
Internet dei
contenuti
Internet delle
persone
Internet delle
cose
Diffusione
Evoluzione
5. The CompanyL’IoT interesserà quasi ogni aspetto della vita
Connected policing
Connected healthcare Connected cities Connected home
Connected carConnected buildings Connected shopping
Connected work Connected travel Connected assistanceConnected family
Connected Government
6. The CompanyL’IoT sarà particolarmente strategico per le industrie
Opportunità generate dall’IoT per il
settore Manifatturiero 2013-2018:
18.8% tasso di crescita annuo
composto del fatturato
Macchine industriali
e componenti
Digital product memory
Performance-based contracting
Remote monitoring
Automotive
Predictive maintenance
Responsive manufacturing
Connected fueling
Connected car
Opportunità generate dall’IoT per il
settore Utilities 2013-2018:
18.6% tasso di crescita annuo
composto del fatturato
Oil & gas
Predictive maintenance
Remote monitoring
Connected fueling
Utilities
Smart grid
Demand forecasting
Predictive maintenance|
Remote monitoring
Opportunità generate dall’IoT per il
settore Retail 2013-2018:
19.8% tasso di crescita annuo
composto del fatturato
Trasporti e
Logistica
Responsive supply chains|
Predictive maintenance
Connected fleet
Retail
Personalized consumer
experience
Responsive supply chains
Intelligent replenishment
7. IoT per il Manufacturing:
Industry 4.0 e Connected Manufacturing
8. The CompanyIoT per il Manufacturing (Industry 4.0)
R&D Sales Manufacturing
Aftermarket
Service
Supply Chain
Smart Data
Track &
Trace
Adaptive
Logistics
Performance based
Contracting
Smart
Products
Digital
Product Memory
Resilient
Production
Energy
Management
Remote Service
Management
Predictive
Maintenance
Valore e nuovi modelli di Business generati da una più completa integrazione di
dati, processi e dall’utilizzo del Cloud
9. The CompanyIndustry 4.0: i 5 scenari di integrazione
1
ERP
MES
SCADA
Livello
Macchina
Azienda
Manifatturiera
2
2. Macchina a Macchina
Orchestrazione della produzione
1
1. Shop floor - Top floor
Integrazione verticale intra-company
E-Commerce/ collaborazione
Cliente
3
3. Integrazione eCommerce
Integrazione diretta con clienti e
distributori
3
Manutenzione esterna
Dati macchine in Cloud
5. Dati Macchine in Cloud
- Manutenzione predittiva
- Qualità predittiva
5
5
Operatore della Qualità
5
Fornitore
4. Produzione Collaborativa
- Visibilità
- Genealogia e tracciabilità
- Qualità
- Approvvigionamento diretto
Collaborazione con fornitori4
4
10. The CompanyIndustry 4.0 e Connected Manufacturing
SAP Connected Manufacturing realizza l’IoT per le aziende manifatturiere con
gli scenari di Industry 4.0
MES
SCADA/HMI
Livello
Macchina
ERP
Industry 4.0
Manufacturing
Industries
Convergenza OT-IT
Sistemi e devices
nello shop floor
Internet of Things
Tutte le Industrie
Tutti gli apparati
e device
Scenario Interno Scenario Esterno
12. The CompanyLe 8 soluzioni per il Connected Manufacturing
Efficienza complessiva
delle attrezzature
Integrazione
Shop – Top
Floor
Dati e
Performance in
Real Time
Qualità del
prodotto
Controllo consumi
energetici
Network del
Prodotto
Tracciabilità Manutenzione
Predittiva
Cloud Platform
1
13. The CompanyIntegrazione Shop Floor – Top Floor
Visualization
Quality Engine
KPIs / Metrics / Alerts
Data Services
Manufacturing Integration & Intelligence
(MII)
Composition
Environment
PLAN MAKE DELIVER
ERP SCM PLM
INTEGRAZIONE
Distributori / ClientiFornitori/ Terzisti
Performance Management Interfaccia utente specifica per
operatori
Plant Connectivity (Pco)
Analisi produzione in tempo reale
Semplificazione dei processi ERP
Business Logic Services
Plant Historian
SCADA/HMI
Wireless integration
Plant Data Collection
Environmental Building
Management
Plant DB
LIMS/ Inspection /
Equipment Testing
MES
DCS / PLC via OPC
14. The CompanyIntegrazione Shop Floor – Value Proposition
2. Energia
4. Pianificazione della
Produzione
6. Disponibilità ed
efficienza degli asset
1. Materie prime
Value Drivers
Miglioramento Costi
8. Qualità
Miglioramento
efficienza dei mezzi
di produzione
Qualità e Sicurezza
Obiettivi strategici Aree di miglioramento
7. Compliance
Aumento
dell’efficienza
operativa
3. Materiali/manodopera di
Manutenzione
5. Esecuzione della
Produzione
Gestione della variabilità delle materie prime in linea con le aspettative dei clienti
Riduzione costi d’urgenze in produzione e spedizione
Riduzione degli sprechi e delle perdite energetiche
Massimizzazione dell’uso di tutti i tipi di energie utilizzati
Riduzione dei costi di manodopera per manutenzione per miglior schedulazione.
Miglior gestione skills, competenze ed efficacia del training
Riduzione giacenze parti di ricambio
Aumento della customer satisfaction e retention
Diminuzione delle giacenze e aumento OEE
Diminuzione dei tempi di reazione alle modifiche ordini
Aumentata visibilità dei processi permette una facile identificazione dei problemi
Maggiore efficienza nell’allocazione e tracking delle risorse
Identificazione delle eccezioni minimizzando rilavorazioni e straordinari
Aumentato l’OEE grazie alla migliore manutenzione, planning and scheduling
Miglioramento del ciclo di vita degli assets produttivi
Aumentata efficienza delle tecniche di manutenzione. preventiva e predittiva
Riduzione degli incidenti EH&S e dei costi associati
Diminuzione costi della conformità normativa
Miglior gestione delle merci pericolose, scarti, rifiuti ed emissioni
Minore variabilità del prodotto, aumentata qualità e customer satisfaction
Identificazione proattiva di problemi, varianze e gestione dei problemi qualitativi
Miglioramento continuo della qualità
15. The CompanyEquipment Effectiveness (OEE)
“Disponibilità”
Fermi non pianificati
“Qualità”
Scarti
“Performance”
Perdite di Efficienza
OEE
Sviluppa la strategia
Definisce gli obiettivi
Configura i KPI’s
Assegna I Targets
Connessione con il Sistema fabbrica
Monitora la Produzione
Raccoglie I dati di Performance
Associa il contesto
Analizza le Performance
Invia allarmi
Compara gli Assets
Compara i Plant
Identifica i problemi
Facilita la Team Collaboration
Da il via alle azioni correttive
Programmi di miglioramento
Conferma il miglioramento
“Lessons Learned”
Rivaluta la Strategia
Resetta gli obiettivi
Valuta
Pianifica
IntegraAnalizza
Migliora
16. The CompanyOEE Value Proposition
Facilita il ciclo del Miglioramento Continuo:
Combina misurazioni quantitative delle performance con appropriati obiettivi
Combina le performance di produzione con il contesto di business
Facilita azioni correttive a livello di fabbrica e su più stabilimenti
Consente miglioramenti in tutta l'azienda, tra cui supply chain, sustainability e financial
Le performance di produzione migliorate incidono direttamente sui costi
La visibilità in tempo reale delle performances di produzione e la capacità di compararle tra diversi stabilimenti e diverse risorse di
produzione portano in tempi brevi ad una riduzione dei costi di produzione
Miglioramento ROI su macchine e impianti
Miglioramento del COGS
Miglioramento sulla utilizzazione delle
risorse
Miglioramento della qualità
del prodotto
Riduzione dei rifiuti/scarti
Riduzione dei fermi macchina
17. The CompanyManutenzione Predittiva
Analizza e monitora i dati degli
equipment e li correla con le
informazioni di business per predire
futuri malfunzionamenti
Rileva da remoto i dati operativi
dagli equipment
Ottimizza le operazioni e i processi di
manutenzione per rende possibili
nuovi modelli di business grazie alla
ottimizzazione globale degli
equipment
18. The CompanyManutenzione Predittiva: Value Proposition
Aumenta la redditività dell’assistenza post-vendita grazie ai
minori costi di servizio e a nuovi flussi di entrate
Risoluzione di un più alto numero di chiamate
Più alta percentuale di soluzioni alla prima visita
Aumentata Customer Satisfaction e Retention
Più alta percentuale di rinnovi dei contratti di assistenza
Nuovi modelli di business
Produttore di
Macchine e Impianti
Utilizzatore di
Macchine e Impianti
OEE più alto
(disponibilità dell’asset, performance e qualità)
Aumenta l’efficienza della manutenzione
Diminuisce i costi di manutenzione
Reazione più rapida ad allarmi e guasti
Più alto tempo medio tra guasti
Più basso tempo medio di riparazione
Grande valore per costruttori ed utilizzatori di macchinari e impianti
20. The CompanyEnergy Control: Value Proposition
Production
Plant
MaintenanceVP Facilities/ Maintenance
Chief Sustainability Officer
EHS
Historians/
Control Systems
Facilities/
BCS
SCM/SRM
Transport
FinanceCOO
CTRM
VP Manufacturing
Energy
Costs
Margins
Protezione dei Margini grazie
ad un controllo rigido dei costi
energetici
Visione integrata dell’energia
come elemento fondamentale della
produzione
Approccio company-wide
ai piani di riduzione dei consumi e
delle emissioni
Più rapido ritorno
degli investimenti
Il Controllo energetico
offre importanti informazioni sulle
strategie di gestione di
immobili/machine/impianti
In-house
22. The CompanyTracciabilità: Value Proposition
L‘identificazione dell‘attuale locazione o degli attuali utilizzatori di un prodotto, la cui genealogia è oggetto di analisi a causa di diproblemi,
consente un rapido ed efficiente blocco del prodotto e limita al minimo i richiami e i ritiri
Impatto positivo sulla salute dei consumatori e/o sull‘efficienza dei prodotti e quindi sui costi aziendali associati. Minimizza i rischi di
esposizione finanziaria dovuti a problemi dei prodotti
Effetti positivi sul Brand aziendale
Rispetto dei tempi limite per i reporting normativi in caso di problemi
KPI per una soluzione: Il tempo di analisi
Oggi nella maggior parte dei casi la sola tracciabilità dei componenti all‘interno di uno stabilimento non è
più sufficiente.
La tracciabilità deve essere mantenuta attraverso tutta la catena logistica, dai fornitori dei fornitori,
attraverso le aziende e i siti produttivi del gruppo e via via fino ai distributori, grossisti e clienti finali.
Inoltre le indagini devono essere rapide e precise ed infine tutti i report richiesti dalle diverse normative
devono essere prodotti correttamente.
23. The CompanyDati e Performance della produzione in Real Time
Analisi dei KPI correlati alle performance in ogni
momento e ad ogni livello organizzativo
Benchmarking interno ed esterno delle performance
Gestione per eccezione con limiti di accettabilità e
allarmi
Browser di Reporting indifferentemente su PC o
mobile devices
24. The Company… e oltre il Manufacturing
Logistics Container
Asset investment
management
Infrastructure
Telematics offerings
Geo- and Traffic monitoring &
control
In the terminal
loading & unloading
OnDemand
Services & Offerings
Sea routes or canals
Monitoring
Connected Logistics
35. SIDI S.p.a. WWW.SIDIGROUP.IT
Società Italiana di Informatica sidispa@sidigroup.it
Tel. +39.02.37742.1
Sede di Imola
Via Lasie, 10/L
40026 Imola - BO
Sede di Roma
Viale Cesare Pavese, 305/313
00144 Roma
Sede di Milano
Via Maffucci, 10
20158 Milano - MI
Sede di Padova
Via Regia, 88
35010 Perarolo di Vigonza - PD
Notes de l'éditeur
This use case is applicable for the following industries: all
This use case is applicable for the following LoBs: Manufacturing, Supply Chain
Key message: Harley Davidson optimizes manufacturing with SAP Connected Manufacturing
<click>
The company and products: Harley-Davidson is the legendary American motorcycle manufacturer.
<click>
The situation / business needs: The factory was completely rebuilt between 2009 and 2011. 5 fabs (one per model) to just one fab for model mix. In the past HD sold just standard bikes – customization was done by the dealers. Now they wanted to offer complete customization options.
<click>
The process innovation through IoT:
Now you do not find 2 bikes in sequence that are the same – Each model has more than 1.300 config options.
All machinery and logistics devices are equipped with sensors and location awareness.
One Motorcycle off the line every 89 seconds.
<click>
The solutions that enable this innovation:
The complete SAP manufacturing solution, including SAP Connected Manufacturing with SAP MII and SAP ME. SAP Connected Manufacturing integrates the shop floor equipment into the manufacturing processes.
<click>
The benefits:
Manufacturing costs reduced.
Delivery time down from 21days to 6h. Come to the factory in the morning – configure your bike and ride home in the afternoon.
Agility and predictiveness.
Hugh improvement in quality because process parameter are measured, compared and store for later tracking
More business
Stock from 10$ (2009) to 70$ (today)
This use case is applicable for the following industries: all
This use case is applicable for the following LoBs: Service, Maintenance
Key message: GEA drives new service excellence and customer satisfaction by establishing an innovative remote service process while using SAP Predictive Maintenance & Services (cloud edition).
<click>
The company and products: GEA is the leading manufacturer of separators and decanters for industrial usage. Separators are machine that separates particles from liquids (e.g. separating fat in milk). GEA is a German based company with global presence. Customers of GEA are Nestle, Heineken, or Müller-Milch.
The value chain in which separators operate is very straight forward: The cow delivers milk that is collected and shipped to dairy companies; fat is separated, and bottled in packages or bottles.
<click>
The situation / business needs: GEA’s objectives were to establish a new service process by monitoring the equipment during the operations in their customers’ supply chains to increase the machine update while reducing the cost of services.
<click>
The process innovation through IoT: Therefore, GEA wanted to implement a solution to monitor the equipment (like separators) in real-time and predict by when a key component of the machine might cause an unplanned machine downtime.
Supervising of the remote equipment is done by a remote service engineer. Once, the remote service engineer identifies critical machine conditions than he plans the service execution based on the service contracts that are captured in SAP CRM. The service engineer will receive a service order and collects –if required- the spare parts and executes the service order.
<click>
The solutions that enable this innovation:
The solution is based on SAP’s Predictive Maintenance and Services Cloud Edition solution which is developed on the SAP HANA Cloud Platform.
The sensors are connected by an IoT adaptor that was development by ifm. Ifm is also the manufacturer of the sensors which are used in GEA’s equipments. The ifm adaptor will convert the various sensor formats in standard format that can be processed by SAP’s IoT connectors. Both the ifm adaptor and the SAP connector run on an Industry PC next to the separators. The sensor data (e.g. 1 sensor value per second) is streamed into the SAP HANA Cloud Platform. The SAP Predictive Maintenance & Service solution is the application that is developed on the SAP HANA Cloud Platform. The platform also manages the so-called hot-store vs. cold-store (which data need to be actually stored in the in-memory data base to drive real-time analysis).
The remote service engineer can access the data via the SAP Predictive Maintenance and Service application. Integration is provided to SAP ERP and CRM to drive the execution of the service processes. Further outlook is that the end-customer will also get access to the information and further processes like procurement or engineering will be also taking advantage of the real-time machine monitoring.
Screenshot explanation: The remote service engineer has the possibility to verify the machine condition with a real-time machine monitoring dashboard on which the values of the monitored sensors are depicted.
The remote service engineer gets multiple insights, e.g. whether the machine is in operation, whether sensor values surpass the upper and lower limits, or what the trend of the sensor values are. This information is provided in real-time per equipment to get immediate insights about the condition of the machine.
<click>
The benefits:
The key benefit is that GEA can now monitor the equipment in real-time and trigger services processes based on the actual contracts that are captured per customer in the SAP CRM system. This vertical integration (insights into the machines) and horizontal integration (enabling business processes) are the key innovation drivers in the IoT age.
Additional Benefits:
Increased machine uptime (reduction of unplanned equipment down-time)
Higher service productivity (Service performed during regular service window, reduced firefighting)
Summary:
Leveraging real-time machine insight will drive competitive advantage resulting in higher revenue and reduced operational costs while increasing the customer loyalty.
This use case is applicable for the following industries: A&D, IM&C, Automotive
This use case is applicable for the following LoBs: Service, Maintenance
<click>
This is the result of a co-innovation project with an aircraft manufacturer.
Overview
Predictive airplane health monitoring framework
Performance benchmarking
Identify alternative maintenance schedules
<click>
Situation / business need: The manufacturer wanted to better leverage engine data of its fleet to optimize R&D and service processes.
<click>
Big Data platform enablers
Prediction of unscheduled maintenance based on engine health data patterns
Detection of outliers and anomalies in the data with machine learning (supervised and unsupervised)
Classification of scheduled and unscheduled maintenance events using text analysis
Analyze flight data at fleet level to provide actionable insight to R&D
<click>
Solutions
SAP HANA Big Data Platform
SAP Predictive Analytics
Lumira for visualization
<click>
Business Goals / Benefits
Better customer support
Proposal of alternative maintenance schedules which may help avoid unplanned downtime and reduce cost
Increased aircraft availability (e.g., operational level of OEMs’ customer airplanes, which in turn increases their customer satisfaction)
Increased service and maintenance revenues
Improved R&D and engineering capabilities to design better airplanes
This use case is applicable for the following industries: all
This use case is applicable for the following LoBs: Service, Sales
Key message: Kaeser enables new business models through IoT-based Service.
<click>
The company and products: Kaeser Compressors is one of the largest providers of compressed air systems and compressed air consulting services. And a long standing customer of SAP.
<click>
The situation / business needs: Already in 2011 they have changed their business model to usage based billing. They have a service called “Sigma Air Utility” by which they sell cubic meters of compressed air rather than the compressors. Benefit for the customers is an OPEX based cost structure instead of CAPEX investments.
They provide high-availability compressed air station for mission critical processes and operate & maintain stations at customer sites as solution provider.
Since the machinery is now actually under the operational responsibility of Kaeser (and not the end customer), Kaeser has an even higher interest to minimize downtime and recognize errors as much as possible.
In order to achieve this, they are working with SAP on a predictive maintenance solution for the deployed equipment.
<click>
The process innovation through IoT:
In order to be able to do this, Kaeser has their installed compressors equipped with sensors which allow metering the amount as well as the quality (pressure, humidity). This data is constantly communicated back to Kaeser’s Service Center.
Data points like temperature, pressure, load, speeds and many more are picked up from multiple part of the equipment.
Intelligent routines detect only parameters that are changing to minimize the amount of data that needs to be transmitted to the backend.
In the backend statistical methods, prediction routines and of course a lot of subject matter expertise is used to project trends into the future and predict potential failures to the equipment.
Based on this, Kaeser is able to adapt the operation of the machine and to schedule any time of maintenance or repair well in advance of the fault actually happening.
<click>
The solutions that enable this innovation:
The connected compressors.
SAP Predictive Maintenance and Service (foundation): Perform online monitoring of KAESER stations, alerts in case of relevant occurrences and based on patterns, inspect future planned events or forecasts. This HANA based solution significantly accelerates analytics & predictive functionality and supports real-time streaming of machine data. The Machine Health Fact Sheet provides an overview of the current and future condition of the machine.
SAP HANA: This type of scenarios really require the advanced capabilities of the HANA platform as a foundation for new and innovative applications such as remote monitoring and predictive maintenance.
SAP CRM Service on HANA for service management processing and the management of customer, machine and contract data. This is part of Suite on HANA which creates the data driven horizontal integration that is needed as the basis to implement new business models.
Screenshot explanation: Machine health control center
<click>
The benefits:
For Kaeser: More attractive business model and differentiation / reduced maintenance cost
For the industrial customer: OPEX cost model, continuous supply at high quality
This use case is applicable for the following industries: All
This use case is applicable for the following LoBs: Supply chain
Key message: The port of Hamburg utilizes SAP Connected Logistics to manage the ever-growing container throughput.
Note: this is an example from a logistics service provider. SAP Connected Logistics can be used by manufacturing companies for plant logistics as well. The solution is the result from a joint project between Hamburg Port Authority, SAP and Deutsche Telekom / T-Systems.
<click>
The company and products: The Hamburg Port Authority is managing the Hamburg port. This solution has been built for the container terminal.
<click>
The situation / business needs: The harbor has an annual throughput of 139 mio. tons, growing at 6.2%. This is an equivalent of 4 mio. containers or 5.500 fully loaded trucks every day. The space in the container terminal is limited, there is no option for spatial growth. Traffic around the port and in Hamburg is an issue.
<click>
The process innovation through IoT:
Use of location sensors of trucks and trailers (on-board units or Smart Phone of Driver).
Track temperature for cool-chain sensitive goods.
Combine that with real time information on e.g. traffic situation and freight orders.
SAP Connected Logistics takes all that information, simulate all possible routes for maximum throughput and adherence to schedules and then advise the drivers accordingly.
HPA has built the smartPORT logistics (SPL) solution for their customers and partners. SPL combines the different components from SAP and T-Systems.
<click>
The solutions that enable this innovation:
Explained before
<click>
The benefits:
Benefits Harbor: More efficient operations – higher Throughput
Optimized unloading and re-loading from air to ground to sea
Better utilization of infrastructure (roads, rails, parking spaces)
Benefits Truck Drivers: Less idle times at terminals or in traffic jams, optimized parking
Benefits Hamburg citizens: Less traffic jams
This use case is applicable for the following industries: IM&C, High Tech
This use case is applicable for the following LoBs: Product development
<click>
The company and products: ifm electronic, an industrial automation technology manufacturer and producer of sensors:
Pressure, Temperature, flow, even digital imaging
They are positioned in the higher value segment
They are building these sensors which they supply to machine and equipment manufacturers who integrate them in their products. And supply these to end customers to integrate into their factories.
<click>
The situation / business needs: As discussed earlier, many factories are now vertically integrated and connected to the enterprise business processes. Which in many cases are run on SAP. So end-customers (companies with factories) want to connect their shop floors to their existing SAP systems and they are asking for connectivity to their machine and equipment supplier, who in turn ask the same from the suppliers of sensors. IFM is looking at this as an opportunity and is building a competitive advantage by making their sensors “SAP ready”. Which in turn renders the machine to be “SAP ready”. And in fact the whole factory – which is what the end customer wanted.
<click>
The process innovation through IoT:
IFM already today has a software stack, that connects the sensors to a limited set of analytics applications.
This are dashboards, that you would typically find on the shop floor above the machine or work center giving information about the status of the machine, through put and other statistics.
This is software, that IFM has developed on their own and that they sell together with their sensors.
In order to achieve the desired connectivity to SAP, IFM is taking a relatively small piece of standard SAP Software – the “SAP Plant Connector” (Pco) and is integrating this into their connectivity layer
They have rebranded this new product and it is now called “Connectivity Port”
And it also now connects directly to the complete suite of SAP Software.
This includes:
Manufacturing Execution stack of ME, MII and EWM
ERP as a whole through NetWeaver
But also the HANA platform for collection of large amounts of data and applications like remote monitoring or predictive maintenance
Please note: This is the SAP System of the End Customer!
<click>
The solutions that enable this innovation:
See before
The way IFM uses SAP software is totally different to how SAP software has been used in the past. It is not used to support IFM’s business processes in some sort, but rather becomes a part of the IFM product. SAP is now part of the BOM.
This business model is called “OEM” and basically SAP will receive a certain amount of the revenue from each sensor sold, where this software is “built in”.
<click>
The benefits:
For IFM:
Add value to the technology offering
Expand the business through innovation
This use case is applicable for the following industries: All DI
This use case is applicable for the following LoBs: All
Key message: HCP powers the IoT backbone of Siemens.
What you see on this picture is a Simotics reluctance motor. These motors cover an output range from 5.5 to 30 kW, achieving high levels of efficiency in full and partial load range. They are deployed in industrial environments across the globe.
<click>
The company and products: The Siemens industry sector is the leading global producer of Industry Automation and Drive Technologies.
<click>
The situation / business needs: Siemens was looking for a way to stay connected to their equipment once deployed and offer value add services to their customers.
<click>
The technology and business innovation: Siemens will implement a so-called “Industry Cloud” based on the HANA Cloud Platform, that allows them to connect all types of machinery, monitor it remotely and offer value adding services like “Asset Analytics”, “Energy Analytics” and “Plant Cloud Services”.
<click>
The solutions that enable this innovation:
The Siemens Industrial Service Backbone (ISB) will be used to connect the devices, to transfer data from and to the devices and to access the devices remotely.
The HANA Cloud Platform will be the open cloud platform the complete solution will be based on.
Siemens and partners will provide IoT applications on top of that.
The business model from an SAP perspective is “outbound OEM”.
<click>
The benefits:
The solution combines the best technologies of both worlds – SAP and Siemens are market leaders in their respective space – and enables Siemens to provide a unique end-to-end IoT offering. This will be the market leading IoT Platform as a Service offering for industrial customers worldwide.
End customers will benefit when connecting their industrial assets to the platform. Accessing the Siemens Cloud for Industry, OEMs gain the missing link towards true data driven product life cycle support, from advanced usage and condition monitoring capabilities through energy management to usage based billing scenarios and all kinds of predictive scenarios. Owners and operators would connect their assets to SCI to obtain advanced insight into the condition, the utilization or the productivity benchmarking of their machines. Machine data is the basis for all predictive methods or other advanced dynamic modelling techniques. Leveraging the respective strengths of two market leading companies will deliver the value which the customers deserve - wherever they or their machines are!
This use case is applicable for the following industries: all
This use case is applicable for the following LoBs: Supply Chain
Key message:
This is the result of a co-innovation project between STILL GmbH, Sick AG, Fraunhofer Institute of Material Flow & Logistics and SAP, showing how adaptive logistics will look like if highly flexible and convertible hardware technologies like the cubeXX fork lifter meet its software counterpart so to speak – the SAP HANA Cloud Platform and its IoT capabilities. All components of the logistics process - humans, materials and load carriers - are interconnected in a grid that integrates them into a holistic system.
<click>
The company and products: Still is a leading supplier of forklift trucks, platform trucks and tractors plus the latest intra-logistics systems.
<click>
The situation / business needs: Still is evolving from a pure product to a services company.
<click>
The innovation through IoT:
The intelligent concept car STILL cubeXX has an on-board IoT connector that connects it to cyber physical systems and cloud apps. The warehouse worker uses a smart device to manage tasks between people and machines. A rotating sensor produces a 3D picture of the environment that is used for orientation and the truck positioning. This picture is displayed on a grid map on the warehouse worker’s iPad – so the intelligent truck is able to identify persons, warehouse lanes and palets digitally and in real time.
Thanks to new sensor and scanner technologies that support dynamic spatial perception, the cubeXX can be flexibly applied in automated applications also ones involving pedestrians.
The cubeXX can automatically complete transport tasks directly received from the SAP Extended Warehouse Management application (SAP EWM). The additional value the project partners have created with this solution is the development of IoT adapters for the cubeXX that enable the truck to communicate with the SAP HANA platform and the respective applications via standard protocols. This is done in such a way that the cubeXX receives transport job data and autonomously transforms into the necessary configuration needed for the transport evaluating information about type, location, quantity and weight or size of the materials to be moved.
Users administering their data with SAP HANA - including data of machinery and trucks - are even going to be able to monitor, optimize and plan maintenance for complete fleets of cubeXX with the support of the SAP HANA Cloud Platform if integrated on level with fleet management. This system will provide users with a full overview of all master and movement data, battery conditions, utilization levels, possible collisions as well as scheduled maintenance intervals. <click>
The solutions that enable this innovation:
STILLs cubeXX concept car is their new generation of a fork lifter which can be named a 6 in 1 material handling vehicle as it is able to run in 6 different modes such as (1) a horizontal order-picking truck (2) a high-lift pallettruck (3) a tugger train (4) a truck for double-stack use (5) a low-lift pallet truck and (6) a counterbalance forklift – so at the hardware site of future logistics scenarios customers will be able to really run smart fork lifter fleets which can be used for different purposes.
BUT NOT without having the respective software infrastructure connected to it realizing the Internet of Things enablement layer which is making sure that the cubeXX fork lifters for example understand what they should do and therefore automatically modify themselves based on the transportation orders that they get from an SAP Extended Warehouse Management system. Further it is also key to continuously collect telemetric data from the many sensor units of the cubeXX in order to get realtime insights into a particular fork lifter or a whole fleet.
HCP can also be used to administer the truck and will also provide means to administer cubeXX fleets in the future.
Screenshot explanation: mobile app for the warehouse worker
<click>
The benefits:
In the future - customers of STILL and others will not buy fork lifters anymore but will ask them for a solution to realize certain values of KPIs that represent the logistics efficiency.
Summary: With the cubeXX, STILL has created a resource-efficient concept car with an intelligent man-machine interface able to efficiently solve future tasks in logistics and production with its ever increasing degree of interlacing.
This use case is applicable for the following industries: all
This use case is applicable for the following LoBs: Maintenance, Service
Key message: Better asset availability through failure pattern detection
<click>
The company and products: This is the result of a common project with BASF, the leading producer of chemicals
<click>
The situation / business needs: In the manufacturing plants they run a huge amount of assets: Pumps, filters, catalysators, etc. They want to improve the asset availability and reduce downtimes. What they‘d like to do is to not only fix the equipment once it breaks down, but rather fix the problem before it occurs.
<click>
The process innovation through IoT:
These equipments have delivered operational data (temperatutres, pressures, chemical concentrations and such) over years. In the project we have taken 3 years worth of data from historians (structured data) and co-related it with the textual information from SAP Plant Maintenance. 2273 sensors over 3 years: 6x109 events.
Special challenges were:
There was not a specific failure to focus on
No production related data
No formal link between machine data and maintenance info except the timeline
<click>
The solutions that enable this innovation:
Sensor data is provided by data historians. BASF have their own maintenance department to make sure everything runs smootly. The maintenance activities are managed and tracked using SAP ERP Plant Maintenance. The Machine Analytics App is the single point of access to IT and OT data. It automatically analyses failure patterns and notified the user about potential upcoming errors.
Screenshot explanation: Machine Analytics app
<click>
The benefits:
Automatic analysis of failure patterns
Early detection of potential future breakdowns
Improved Overall Equipment Effectiveness (availability and performance)
Next steps:
Fusion of current and historical machine data
Further improvement of the analytical algorythms
Additional functionality