Tuesday, July 10, 2018

Magic Software becomes a Microsoft One Commercial Partner

Magic has earned Co-Sell Ready Status through the Microsoft One Commercial Partner (OCP) Program for its Magic xpi integration platform and Magic xpc cloud-based integration platform as a service (iPaaS). 

Magic Software representatives will collaborate with Microsoft field sales teams and Microsoft partners worldwide on targeted customer integration and application development opportunities.

Magic offers a robust variety of integration solutions for Microsoft

Using Magic xpi integration platform and the Magic xpc cloud-based 
integration platform as a service, companies can connect a wide range of business ecosystems by implementing out-of-the-box certified and optimized connectors to extend the capabilities of leading ERP, CRM, finance, and other enterprise systems.

"Magic brings great value to our customers and partner eco-system that can deliver integration faster, at low risk and natively in the cloud." 
Idit Gazit Berger, MEA ISV Lead at Microsoft.

“We are eager to partner with Microsoft and leverage our market leadership, vendor certified connectors and API-driven high productivity environment.”
Stephan Romeder, VP Global Business Development at Magic Software.

The Microsoft Co-Sell Program aligns Microsoft’s large, global salesforce behind partners like Magic Software to drive top-notch solutions for customers. To be eligible, businesses must submit customer references that demonstrate successful projects, meet a performance commitment, and pass technology and sales assessments, all of which Magic Software was able to quickly demonstrate.

Magic Software is demonstrating Magic xpi and Magic xpc and how Microsoft partners can drive new additional business at the upcoming Microsoft Inspire conference, July 15-19 in Las Vegas, at booth #1803.




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Thursday, June 21, 2018

Using system integration to optimize the manufacturing process

Core systems within the manufacturing cycle need to be connected to optimize productivity and performance levels.

More manufacturers are becoming smart factories by leveraging the latest innovations such as the Internet of Things (IoT) to optimize production. All of these innovations require the ability to collect, share, and process reams of data.

However, if all the separate systems used to manage each phase of manufacturing are siloed, management cannot gather the insights they need. Allowing the free flow of information through each stage of the manufacturing cycle is necessary to pave the way to the factory of the future.

An integrated enterprise resource management (ERP), manufacturing execution system (MES), and product lifecycle management (PLM) platform empowers Industrie 4.0 by connecting core systems and provide manufacturers with all the necessary data to gain the needed insights to achieve higher levels of quality and productivity.


The building blocks of a product lifecycle

ERP manages the business of manufacturing products; MES controls the production process itself; and PLM tracks the design of the products being built.

The three systems have distinct purposes, but each of them holds data essential for understanding each stage of the product lifecycle to maximize manufacturing efficiency and quality.

Companies that manufacture products in-house typically use an ERP system to manage information that is shared between their finance, sales, and manufacturing departments. Companies use ERP systems to track orders throughout the manufacturing process, from receipt and production through delivery, in order to get a better understanding about ideal inventory levels and delivery lead times. MES is designed to help track and manage manufacturing information in real time, giving managers greater visibility into the shop floor to help improve quality, productivity, and production time. MES works either minute by minute, or over 10 or 20-minute increments collecting and processing data in real time to control and coordinate manufacturing processes for traceability and performance improvement.

PLM is a business system designed to control the product record across all of the development stages—from concept to design to production. Using a PLM system to manage product data, manufacturers have continuous access to the single and correct version of their product record at any time and can implement an efficient and streamlined change process.

With a PLM system, a company can manage all types of product data including bill of materials (BOMs) and product files. A PLM system also enables a company to communicate changes to the product designs to every supplier in the supply chain.




Process optimization with system integration

ERP to MES integration is becoming standard for syncing customers, orders, and inventory data with the shop floor to meet actual production requirements, and for reconciling material consumption for better planning.

Typically, MES populates the ERP system with the quantities manufactured and scrapped as well as performance levels. The real-time business management information it provides can be used to fine-tune production schedules.

MES to PLM integration, is becoming more popular as manufacturers are looking for ways to accelerate product ramp-up times and create a feedback loop between the various elements of the product cycle, including design and production, to better manage quality.

PLM feeds the MES with bill of materials information, which can speed up a production cycle by eliminating incomplete or inaccurate information sharing between product designers and the shop floor. With MES and PLM integration, manufacturers also have the flexibility to customize production for a particular country or plant, which can eliminate a lot of manual labor to fine tune manufacturing processes.

Integrating ERP and PLM provides a clear and comprehensive view on the status of engineering change orders including the corresponding work orders, inventory changes, and supplier communications.

Sharing information between these systems closes the gap between the stage of product development and delivery and replaces error prone labor intensive manual processes required to consolidate information.

Although there are a lot of benefits to integrating these core enterprise systems, the process is still relatively new. Engineering, information technology (IT), and operations technology (OT) have treated their systems as their own assets that are used to meet their individual goals. Closed systems together with communication protocols and proprietary networks, have created complicated technical hurdles hindering data from flowing easily between systems.

When systems are integrated together, there's traditionally been a lot of reliance on hand coding, which can break when there are system modifications to either one system or the other, requiring constant maintenance and rework. Multipoint integrations using middleware can provide a standard method for managing and reconciling data that accelerates and simplifies the integration process.

ERP, MES, and PLM systems each have distinct functionalities that give an organization better control over its manufacturing processes and can pave the way toward Industrie 4.0 when they are used together. Together, they can accelerate production and improve quality by becoming the backbone for innovation to keep improving the manufacturing process.

Written by Javier Jiménez,  the president of Magic Software Enterprises Americas. 




Contact Magic Software to help you assess the best practices and your connectivity needs



Originally published at Control Engineering

Wednesday, June 20, 2018

Build a smarter supply chain with IoT

Last week, when I took a sip of my first cup of coffee in the morning, I discovered the milk was sour. I wanted to know everyone and anyone who could be held responsible — the store manager, the delivery truck driver, the quality control supervisor of the dairy farm. You don’t want to mess with my morning coffee.

Until recently, it may have seemed too much to ask, but it’s now possible, with today’s technology, to track every step of the supply chain for the contents in your refrigerator. Today manufacturers are using IoT to track products through their lifecycle to achieve higher levels of efficiency. The combination of smart sensors, cloud technology and analytics are making supply chains smarter and more efficient, with the ability to track each product through every stage of its lifecycle.

Here are some examples of how IoT can be used to better manage the supply chain:


Inventory control
IoT can be used to provide a real-time window into inventory levels by measuring product quantities and, when necessary, automatically sending an order to a supplier for depleted stock. Product availability can be displayed on a screen to respond to customer inquiries. Eskimo Cold Storage uses RFID tags to track the location of its 32,000 pallets in its 10.9 million cubic foot cold storage, eliminating $208,000 in costs associated with locating lost inventory and $25,000 in annual chargebacks due to lost merchandise.

Shipping
When shipping products, companies are using IoT to monitor the product condition during the entire trip from beginning to end, instead of relying on testing at the end of the journey. Maersk uses IoT to monitor 300,000 refrigerated containers containing fresh produce that needs to be shipped in a tight range of temperature and humidity. In addition to protecting perishable merchandise, Maersk only needs to visually inspect 60% of its containers since data from sensors provides certainty that the goods in these containers have been kept in good condition during the shipping process.

Warehouse management
Using IoT, every single part can be tracked from when it’s first manufactured to when it’s assembled and shipped to an end customer. Walmart cut taking physical inventory from one month to just 24 hours by using sophisticated drones that fly through the warehouse, scan products and check for misplaced items. BMW uses sensors to follow a part from the point it was manufactured to when the vehicle is sold from all of its 31 assembly facilities located in over 15 countries, ensuring everything gets to the right place while utilizing the minimum amount of resources. 

Delivery
The last mile is essential. Estimated time of arrival synchronization can help trucking companies place the right trucks in the right areas at the right times to avoid backups in the loading areas and ensure that other resources, like fuel and hourly employee time, are not wasted. Grocery retailer Ocado equips delivery vans with a range of IoT sensors to log valuable information, such as the vehicle’s location, wheel speed, engine revs, braking, fuel consumption and cornering speed, to select the best route for the most efficient delivery

Supplier management
The data obtained through asset tracking is also important because it allows companies to tweak their own production schedules, as well as recognize subpar vendor relationships that may be costing them money. According to IBM, up to 65% of the value of a company’s products or services is derived from its suppliers, which provides companies with a huge incentive to manage the relationship more efficiently. North West Redwater built a new bitumen refinery across six square kilometers that included over 100 contracting companies. An IoT system was used in order to simplify the contractor onboarding process, decrease operating costs and control the project schedule.


There are certain pieces of the puzzle that need to be put in place to make a working IoT system, including a flexible infrastructure for complying with regulations, scalability to manage the data tsunami from thousands of sensors, as well as comprehensive and seamless data integration. Several different systems in the back office as well as the shop floor need to be integrated including product lifecycle management, enterprise resource planning and customer relationship management systems to achieve end-to-end process optimization.

However, investing in IoT to streamline the supply chain is well worth the effort. With all the potential improvements in customer service and efficiency, it’s only a matter of time before IoT is an expected and necessary part of managing the supply chain.




Contact Magic Software to help you assess the best practices and your connectivity needs



First published in TechTarget's IoT Agenda


Written by Javier Jiménez, president of Magic Software Enterprises Americas. 

Monday, June 11, 2018

Smart Manufacturing with AI Inside

Already deployed in autonomous trucks, chatbots providing customer service, and drone trains, artificial intelligence (AI) is also making huge boosts to manufacturers’ productivity. Creating insights to enable manufacturers to produce higher quality products faster and more efficiently, AI solutions also provide critical information to help managers make more informed business decisions.

There are several ways that AI can optimize each stage of manufacturing from the shop floor to the final product delivery. 

Here are 5 ways manufacturing functions can raise their level of productivity by using AI.


Streamlined supply chain
Every step along the supply chain can be optimized by using smart sensors to track the location of components combined with analytics and machine learning. McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively.
BMW has already implemented an AI system that follows a part from the initial point it was first manufactured, all the way through to when the vehicle is sold for 31 assembly facilities, spread over 15 countries. This system makes sure everything gets to the right place at the right time while utilizing the minimum amount of resources.

Better inventory control 
AI and machine learning can forecast demand by testing hundreds of models and possibilities while also being more precise by adjusting its calculations for the introduction of new suppliers, product and materials. By having more accurate demand forecasts, companies can avoid overproduction and the costs of keeping excess inventory on shelves. An excess of raw materials or finished goods ties up cash that can be put to better use elsewhere. Insufficient inventory or stock outs can be just as detrimental by causing product delays, which can reduce customer satisfaction and tarnish a company’s reputation for reliability. 

Predictive maintenance 
There is a huge incentive to invest in predictive maintenance solutions because of their strong ROI and quick payback. By utilizing sensors to monitor operational conditions, technicians can be alerted in advance of potential equipment problems and service machines based on actual wear and tear instead of scheduled service visits based on general manufacturers’ recommendations. When predictive maintenance systems are connected to ERP systems, machines can even evaluate their own performance, order their own replacement parts and schedule a field technician when necessary.
Siemens installed smart boxes containing sensors and a communications interface on older motors, transmissions of wind turbines, and other equipment to assess a machine’s condition and detect irregularities in order to determine when a service call is required. A similar predictive maintenance solution has been implemented at the German railway company Deutsche Bahn to monitor the condition of engines of high speed trains.

Customized manufacturing 
Advances in AI and software intelligence are enabling companies to make products and services that are highly personalized. Twenty percent of consumers said they would be willing to pay a 20% premium for products or services personalized for them. And brands who can highly personalize products are also able to build greater loyalty and trust with their customers. Daimler utilizes real-time data of parts and their availability to efficiently respond and adjust for vehicle customizations even providing an app where customers can track the progress of their car’s assembly called Joyful Anticipation.

Autonomous optimization
AI systems can monitor quantities used, cycle times, temperatures, lead times, errors, and down time to continuously optimize production runs. AI will enable us to transform data into intelligence in a vendor agnostic environment where all machines speak the same language, increasing production efficiency from machine to machine across the shop floor. Siemens already equips devices with AI capabilities to improve the reliability of power grids, with the ability to classify and localize disruptions in the grid, and perform the necessary calculations remotely to self-heal electrical systems.

There is resistance to the implementation of AI systems. Many companies are reluctant to share vast production and process data due to its sensitive nature. Others include concerns about data security, lack of standards (although those are rapidly progressing), and concerns about the impact on people who fear they will be replaced by machines or resist working side by side with AI systems.

There is also the challenge of integrating data between different types of equipment and back office systems with a high level of reliability and low latency so systems can benefit from real-time insights. A data management platform can provide scalability with the capabilities needed to collect, integrate, process and share huge volumes of data with a high level of performance and security.

Since AI can introduce so many different types of efficiency boosters throughout the manufacturing organization, it’s certain that in time technology, people and processes will adjust to make machine learning an indispensable part of companies improving product quality and customer service.

Learn more about how ASSA ABLOY implemented a smart manufacturing project powered by Magic xpi Integration Platform:






Contact Magic Software to help you assess the best practices and your connectivity needs



First published in MHW Magazine



Written by Stephan Romeder, VP Global Business Development at Magic Software

Tuesday, May 29, 2018

Predictive Maintenance KPIs Are Unbeatable

Field service is one of the most successful applications of IoT and it is already generating valuable results for companies. By utilizing sensors to monitor operational conditions, storing historical and real-time data in the cloud and performing analytics, predictive maintenance makes it possible to service equipment based on actual wear and tear instead of scheduled preventive maintenance. The end result is increased productivity, profitability and improved employee safety.

Both government and consulting firms concur that predictive maintenance can unleash substantial equipment and field technician productivity in field service. McKinsey estimates a 10 percent reduction in annual maintenance costs and a 20 percent reduction in downtime with a 25 percent reduction in inspection costs for AI-driven predictive maintenance models.

UPS claims it has already saved millions of dollar by implementing a predictive maintenance to reduce breakdowns and extend the equipment life for their fleet of trucks. Managing over 100,000 vehicles globally they combine over 16 petabytes of data from engines to analyse the performance and condition of their vehicles.

Siemens performs predictive maintenance for NASA’s cooling systems at the Edwards US Air Force base in California by monitoring the performance of fans, pumps, air handlers, and cooling towers. Every time there is a significant status change for a piece of equipment, automatic notifications are sent to NASA and an analyst for review and action.

Deutsche Bahn (DB), Germany’s railway company, and Siemens have launched a pilot application for the predictive servicing and maintenance of the high-speed trains. Siemens has opened a dedicated Mobility Data Services Center in Munich to perform data analysis to predict potential equipment failures.


Predictive Maintenance as a Service

Field service solutions are a much-needed action layer for modern equipment that can self-monitor and diagnose. Once an anomaly is detected, an alert from the IoT cloud can move into a field service management system where it can be scheduled and assigned to a technician. KPIs like Mean Time to Diagnose (MTTD), Mean Time to Repair (MTTR), First Time Fix (FTF), and Technician Productivity are all positively impacted when equipment can self-diagnose. It can instantly provide these insights so that technicians know exactly the type of service that is needed and can come prepared with the right tools and inventory. In addition, KPIs like Uptime and Customer Satisfaction are positively impacted. Proactively addressing problems with predictive maintenance programs helps ensure longer equipment lifetime and higher customer satisfaction with uninterrupted performance.

There are a number of vendors that advertise the ability for their components to initiate their own service calls, including Cummins Power Generation, a global firm that makes generators and other power generation equipment. The company’s equipment automatically alerts homeowners and technicians about potential equipment problems or service requirements via a mobile app.

While the amount of data coming out of connected equipment may seem daunting at first, once companies understand the different streams of data that are needed to deliver predictive maintenance and are able to correctly analyze it, then they gain an incredible advantage over their competition. For one, they are able to provide superior service by getting ahead of equipment breaks to deliver just the right amount of maintenance when it is needed.

Manufacturers might even prefer to sell outcomes, such as uninterrupted hours of operation, rather than equipment.


Infrastructure and Security Requirements of Predictive Maintenance

In order for predictive maintenance to become a viable solution, machines, devices, sensors and people need to connect and communicate with one another seamlessly. There needs to be a virtual copy of the physical world in order to make sense of all that data to conceptualize the information. Technologies utilizing artificial intelligence need to be deployed to support decision making and problem solving so digital systems can work, whenever possible, without human intervention.

There is also the challenge of integrating data between different types of equipment and back office systems with a low level of latency. In addition, data needs to be filtered so that manufacturers’ proprietary information and customers’ financial data won’t be hacked. A data management platform can provide the capabilities needed to collect, integrate, process and share huge volumes of data with a high level of performance, security and reliability.

By monitoring machine health and having a way to act based on IoT analytics, manufacturers are able to prevent downtime and extend equipment life, resulting in huge productivity and customer satisfaction gains. The KPIs are so superior that, despite the challenges, predictive maintenance will become the heart and brains of every field service organization.





Written by Tsipora Cohen, Global Head of Marketing for Magic Software

Sunday, January 28, 2018

Aerzener Maschinenfabrik Opts for Salesforce and Magic xpi platform

German Company, Aerzener Maschinenfabrik, one of the world's leading manufacturers of two-shaft rotary pistons, harnesses the power of Magic xpi in its integration to connect Salesforce to internal enterprise solutions.

Magic xpi native Salesforce adapter allows Aerzener Maschinenfabrik to receive multiple benefits including: 360-degree real-time views of customer data, easily integrate Salesforce CRM with internal enterprise systems, and replace complex programming of interfaces.

Aerzener-Maschinefabrik

360-degree real-time view of customer information

Providing sales with a 360-degree customer view including credit status and social media comments, Magic xpi gives real-time data of accurate information for Aezener Maschinenfabrik. Now the company is able to achieve a high level of customer service from the integration. 


Salesforce CRM easily integrates with internal enterprise systems using Magic xpi Integration Platform

Magic xpi integration significantly expands the speed and scope of enterprise data connectivity with an easy communication of business processes. The backend systems of industrial high-performance machines were easily integrated to the Salesforce platform. Magic ensures synchronized data flows back and forth making all information available in the internal systems and Salesforce. Aezener Maschinenfabrik benefits from the flexible connectivity to different systems, file types and data formats connecting between Salesforce platform to internal enterprise solutions



Aerzener-Maschinefabrik factory


Built-in Salesforce connector will replace complex programming of interfaces

For the Salesforce integration, Magic xpi provides a standard connector, eradicating the need for complex programming of interfaces. Magic xpi Studio makes is simple with code-free, drag and drop environment and comprehensive visual data mapper. If required, Aerzener Maschinenfabrik GmbH can link any of its other cloud and on-premise systems with Magic xpi. Magic xpi acts as a hub and enables clear and comprehensive solutions that can be easily maintained. 

Ewald Hillebrand, head of IT at Aerzener Maschinenfabrik, explained how it all comes together with Magic xpi: “By integrating our systems with Magic xpi, we get a 360-degree real-time view of our customer information." He continues, "Automating our workflows and business processes lead to increased productivity and reduced human errors." It also enabled the company to connect the Salesforce Platform with their backend systems in an efficient way, providing another tremendous benefit.





Contact Magic Software to help you assess the best practices and your connectivity needs



*Image Credit: Aerzener-Maschinefabrik