This document provides a comparison of popular simulation software packages. It summarizes their key capabilities and features like typical applications, compatible operating systems, modeling approaches, output analysis support, and pricing. The document is based on a survey from the October 2017 issue of OR/MS Today magazine and aims to help readers analyze, compare and select the best simulation software to solve their challenges.
The last mile: The next battleground for businesses adapting to changing cons...Deloitte Canada
The document discusses how companies must adapt their last mile delivery strategies to meet evolving consumer expectations around delivery and pick-up options. It notes that online retail is growing significantly and consumers increasingly demand flexibility in how and where they receive purchases. Companies that offer customers choice and seamless integration across channels through innovative last mile solutions, like SmartCentres' Penguin Pick-Up delivery locations, will be better positioned competitively to serve consumers how they want. The last mile presents both opportunities and threats, so companies must ask the right questions to efficiently manage their supply chains and distribution as consumer demands change.
Augmented Reality in supply chain managementOnkarChopade1
Augmented reality has many applications in supply chain management and logistics, including optimizing warehouse operations, transport, assembly/repair, customer service, and last-mile delivery. AR-enabled devices can guide workers, provide real-time inventory and location data, enable remote troubleshooting and repairs, and improve tracking of shipments and deliveries. The future of AR in supply chains may include customers using AR to identify product issues for faster repairs without visiting stores.
Just a few years back, artificial intelligence meant adaptions like Jarvis. Who would have thought that AI would soon become an application of our daily lives?
Artificial intelligence has the potential to streamline several business processes, analyze data for insights, and help in building fruitful business strategies. Hence, globally, it is being used to remediate old processes, invent new methods, and improve productivity.
How Last Mile Delivery Affects Supply Chain, E-tailing & Order FulfillmentAngela Carver
Perfecting last mile delivery has had a significant impact on the supply chain, how retailers develop their e-commerce sites and how order fulfillment is completed. Essentially, every part of the consumer experience has been adapted to meet changing expectations due to omni-channel retailing. Let’s explore how each aspect has been affected.
The manual processes of yesterday have been replaced with the automated data collection technology of tomorrow. Field service staff are now equipped with mobile ADC devices that allow them to view changes to deliveries and routes and update order status’. Replacing paper-based processes has improved visibility within the supply chain and improved the overall customer experience during the last mile. These ADC devices are also being used in fulfillment centers to increase the efficiency of order processing. Supply chain operators have realized that if orders are processed faster they can reach the end customer faster.
Retailers have also put a new focus on the user-friendliness of their e-commerce websites. Studies show that easy-to-navigate shopping carts lead to larger and more frequent online purchases. Optimizing these sites can lead to revenue growth. Online sale increases will force transportation providers to adapt to handle last mile delivery volume influx.
To handle these increases and make last mile delivery more effective, supply chain operators are expanding distribution networks by adding smaller scale e-fulfillment DCs on the edge of urban areas. This encourages swift delivery of online and in-store orders. Another way retailers are doing this is with the addition of new courier services. In some of these densely populated areas small parcel delivery is done by bicycle or personal vehicle delivery to avoid traffic congestion.
Increases in technology adoption can also be attributed to last mile logistics requirements. Retailers and their supply chain partners are implementing distributed order management solutions to streamline order fulfillment operations. This is done through custom workflow rules used to automate everyday processes. Supply chain service providers are also utilizing delivery window planning solutions to reduce costs associated to redelivery. And to make the most of their transportation dollars shippers are implementing route optimization and transportation scheduling solutions.
To learn more about how last mile logistics has affected supply chain operations speak to a Datex expert today at marketing@datexcorp.com or www.datexcorp.com .
This document discusses digital transformation in logistics. Technologies like big data, analytics, AI, IoT, and robotics promise to transform business operations by raising quality, increasing flexibility, and boosting productivity. However, digital transformation is challenging for large, established companies not born in the digital age. The document proposes using design thinking to understand problems and leverage team capabilities to become more consumer-driven, digital, and willing to break from the status quo. It also recommends a simple framework for digital transformation in logistics focusing on foundations, integration, and collaboration. Finally, it discusses deploying global analytics for logistics by starting with pilot markets and key metrics before global rollout.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Last mile delivery is defined as the movement of goods from a transportation hub to the final delivery destination. The final delivery destination is typically a personal residence. The focus of last mile logistics is to deliver items to the end user as fast as possible. Last mile logistics has become a popular area of interest for retailers due to the growing demand for fully integrated omni-channel retailing. Evolving omni-channel needs have forced retailers to evaluate current transportation network capabilities and make adjustments accordingly.
Focus has been placed on last mile logistics because, in many cases, this is a key differentiator for retailers. Because consumers can easily shop for product alternatives retailers and their supply chain partners must provide exceptional service to gain market share and build brand loyalty.
Last mile delivery is becoming more important than ever due to the surge of online orders. E-commerce sales are expected to reach $1.35 billion by 2018, an increase of 28.8% from 2013. These expected increases span across a variety of product types including apparel, entertainment, food, health & beauty, electronics and more.
Retailers must begin to prepare their transportation networks for traffic fluctuations caused by the expected growth in online sales. Traditional transportation methods such as UPS, FedEx and USPS are not successful in all regions and retailers are beginning to search for alternatives to satisfy their needs. In order to accommodate faster shipping times, changing regulation and infrastructure limitations retailers and their transportation partners have started to research delivery alternatives including click-to-collect locations, local regional carriers, drones and much more.
By focusing on last mile delivery alternatives retailers are able to provide and guarantee exceptional service levels to their customers and adapt to the constantly changing omni-channel retail environment. To learn more about last mile delivery and omni-channel retailing contact Datex experts today at marketing@datexcorp.com or www.datexcorp.com .
The last mile: The next battleground for businesses adapting to changing cons...Deloitte Canada
The document discusses how companies must adapt their last mile delivery strategies to meet evolving consumer expectations around delivery and pick-up options. It notes that online retail is growing significantly and consumers increasingly demand flexibility in how and where they receive purchases. Companies that offer customers choice and seamless integration across channels through innovative last mile solutions, like SmartCentres' Penguin Pick-Up delivery locations, will be better positioned competitively to serve consumers how they want. The last mile presents both opportunities and threats, so companies must ask the right questions to efficiently manage their supply chains and distribution as consumer demands change.
Augmented Reality in supply chain managementOnkarChopade1
Augmented reality has many applications in supply chain management and logistics, including optimizing warehouse operations, transport, assembly/repair, customer service, and last-mile delivery. AR-enabled devices can guide workers, provide real-time inventory and location data, enable remote troubleshooting and repairs, and improve tracking of shipments and deliveries. The future of AR in supply chains may include customers using AR to identify product issues for faster repairs without visiting stores.
Just a few years back, artificial intelligence meant adaptions like Jarvis. Who would have thought that AI would soon become an application of our daily lives?
Artificial intelligence has the potential to streamline several business processes, analyze data for insights, and help in building fruitful business strategies. Hence, globally, it is being used to remediate old processes, invent new methods, and improve productivity.
How Last Mile Delivery Affects Supply Chain, E-tailing & Order FulfillmentAngela Carver
Perfecting last mile delivery has had a significant impact on the supply chain, how retailers develop their e-commerce sites and how order fulfillment is completed. Essentially, every part of the consumer experience has been adapted to meet changing expectations due to omni-channel retailing. Let’s explore how each aspect has been affected.
The manual processes of yesterday have been replaced with the automated data collection technology of tomorrow. Field service staff are now equipped with mobile ADC devices that allow them to view changes to deliveries and routes and update order status’. Replacing paper-based processes has improved visibility within the supply chain and improved the overall customer experience during the last mile. These ADC devices are also being used in fulfillment centers to increase the efficiency of order processing. Supply chain operators have realized that if orders are processed faster they can reach the end customer faster.
Retailers have also put a new focus on the user-friendliness of their e-commerce websites. Studies show that easy-to-navigate shopping carts lead to larger and more frequent online purchases. Optimizing these sites can lead to revenue growth. Online sale increases will force transportation providers to adapt to handle last mile delivery volume influx.
To handle these increases and make last mile delivery more effective, supply chain operators are expanding distribution networks by adding smaller scale e-fulfillment DCs on the edge of urban areas. This encourages swift delivery of online and in-store orders. Another way retailers are doing this is with the addition of new courier services. In some of these densely populated areas small parcel delivery is done by bicycle or personal vehicle delivery to avoid traffic congestion.
Increases in technology adoption can also be attributed to last mile logistics requirements. Retailers and their supply chain partners are implementing distributed order management solutions to streamline order fulfillment operations. This is done through custom workflow rules used to automate everyday processes. Supply chain service providers are also utilizing delivery window planning solutions to reduce costs associated to redelivery. And to make the most of their transportation dollars shippers are implementing route optimization and transportation scheduling solutions.
To learn more about how last mile logistics has affected supply chain operations speak to a Datex expert today at marketing@datexcorp.com or www.datexcorp.com .
This document discusses digital transformation in logistics. Technologies like big data, analytics, AI, IoT, and robotics promise to transform business operations by raising quality, increasing flexibility, and boosting productivity. However, digital transformation is challenging for large, established companies not born in the digital age. The document proposes using design thinking to understand problems and leverage team capabilities to become more consumer-driven, digital, and willing to break from the status quo. It also recommends a simple framework for digital transformation in logistics focusing on foundations, integration, and collaboration. Finally, it discusses deploying global analytics for logistics by starting with pilot markets and key metrics before global rollout.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Last mile delivery is defined as the movement of goods from a transportation hub to the final delivery destination. The final delivery destination is typically a personal residence. The focus of last mile logistics is to deliver items to the end user as fast as possible. Last mile logistics has become a popular area of interest for retailers due to the growing demand for fully integrated omni-channel retailing. Evolving omni-channel needs have forced retailers to evaluate current transportation network capabilities and make adjustments accordingly.
Focus has been placed on last mile logistics because, in many cases, this is a key differentiator for retailers. Because consumers can easily shop for product alternatives retailers and their supply chain partners must provide exceptional service to gain market share and build brand loyalty.
Last mile delivery is becoming more important than ever due to the surge of online orders. E-commerce sales are expected to reach $1.35 billion by 2018, an increase of 28.8% from 2013. These expected increases span across a variety of product types including apparel, entertainment, food, health & beauty, electronics and more.
Retailers must begin to prepare their transportation networks for traffic fluctuations caused by the expected growth in online sales. Traditional transportation methods such as UPS, FedEx and USPS are not successful in all regions and retailers are beginning to search for alternatives to satisfy their needs. In order to accommodate faster shipping times, changing regulation and infrastructure limitations retailers and their transportation partners have started to research delivery alternatives including click-to-collect locations, local regional carriers, drones and much more.
By focusing on last mile delivery alternatives retailers are able to provide and guarantee exceptional service levels to their customers and adapt to the constantly changing omni-channel retail environment. To learn more about last mile delivery and omni-channel retailing contact Datex experts today at marketing@datexcorp.com or www.datexcorp.com .
1) AI and automation technologies like RPA, machine learning, and computer vision can address uncertainties and inefficiencies in supply chains by optimizing tasks like demand forecasting, procurement, inventory management, and predictive maintenance.
2) Increased transparency through real-time tracking and monitoring enabled by technologies improves visibility across supply chains and drives efficiency.
3) Machine learning and behavioral analytics can make logistics operations safer by monitoring driver behavior and predicting accidents through advanced driver assistance systems.
Tracxn - Top Business Models - Artificial intelligence Industry Application...Tracxn
Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Artificial Intelligence - Industry Applications rebrand.ly/u3hvir3
Get our free reports on #PracticeArea or #sector of your interest to your mailbox regularly https://rb.gy/cx2upn
Almost everything in your warehouse will eventually be connected to the Internet of Things (IoT), the online network that connects and exchanges data between devices, vehicles, inventory and even buildings. The Internet of Things is already starting the digital transformation of supply chains by improving efficiency, accuracy, and reducing costs. It is anticipated that many more benefits and opportunities will emerge in the future. Every supply chain business, from manufacturing to logistics, should be taking a close look at the Internet of Things now. Early adopters of IoT technologies will be better able to take a giant leap forward in their business and gain a competitive edge from this technology.
Nurturing Digital Twins: How to Build Virtual Instances of Physical Assets to...Cognizant
To embark on the digital twin jounrey, assess your readiness, define and communicate a vision, set common data management rules and build in flexibility for intelligence.
Most of the attention around Analytics goes to the results of the Analytics activity - a better customer profile, a new target market, more efficient product design. What about the process and infrastructure that is needed to get the data to the point in which it is useful to the Analytics community? This presentation addresses the less glamorous, but critically important side of Analytics: the people, process and technology infrastructure that enable an analytics-driven organization.
The presentation will cover these questions:
• How do you align your information assets to your Analytics goals? What data do you need and where do you find it?
• What are the organizational constructs that need to be considered to integrate Data Governance and Analytics?
• What organizational change can be anticipated and how should it be addressed?
• How do you design your data management and data governance programs to support Analytics? How is this different than an operational use case?
This slide deck is drawn from a tutorial presented jointly with Samra Sulaiman of ConsultData at Enterprise Dataversity 2015.
A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.
This document provides a reference architecture for building a digital banking environment using VMware Cloud on AWS and AWS native services. It outlines key components like SDDCs for core banking and retail banking workloads, networking components like Transit VPC and Transit Connect, and how various AWS services can be integrated for capabilities like analytics, machine learning, security, and operations. The architecture shows account structures, network topology and flows, and examples of workloads and services across the environment.
Master Data Management (MDM) is a systematic approach to cleaning up customer data so businesses can manage it efficiently and grow effectively. MDM helps businesses achieve a single version of truth about customers. It deals with strategies, architectures, and technologies for managing customer data, known as Customer Data Integration (CDI). Implementing MDM requires gaining commitment from senior management, understanding business drivers and resource requirements, and providing estimates of benefits like reduced costs and increased sales. A pilot project should be proposed before a full implementation to demonstrate value and gather feedback.
7 Amazing Examples of Digital Twin Technology In PracticeBernard Marr
Digital twins are a virtual simulation of real-world objects. By using Internet of Things sensors that feed data from the physical object to computers, digital twins provide the exact same situation to study and test without the consequences of doing the test in the real world. The uses for the technology are nearly limitless.
This document discusses how smart manufacturing and artificial intelligence of things (AIoT) can help drive digital transformation. It provides examples of how IoT solutions have helped various companies reduce costs and improve operations. It then discusses key concepts in smart manufacturing like the intelligent edge, cloud computing, and different waves of innovation with IoT, edge, and AI. The document outlines Microsoft's IoT portfolio and reference architecture for smart manufacturing. It also describes various Azure IoT capabilities and solutions like IoT Hub, IoT Edge, Time Series Insights, and preconfigured solutions for predictive maintenance, remote monitoring and connected factories. Finally, it discusses how machine learning can address supply chain optimization, predictive maintenance, anomaly detection, production scheduling and demand
I gave a talk at North California Business Marketing Association on Customer Data Platform with examples ranging from Uber Grayballing to Zoom's customer retention email and "dogs or muffins".
This document discusses the digital transformation of high-tech industries. It notes that profit and market value are migrating away from hardware and components towards internet platforms. It identifies trends like artificial intelligence, internet of things, cloud computing and edge processing driving changes. Few product companies have fully transformed, with internet platform companies outpacing spending on research and development. The document outlines a framework for companies to transform their core business while growing new business models in areas like connected products, living products and services, and ecosystem platforms. It emphasizes the need for digital talent and factories to drive transformation.
The presentation discusses master data management and reference data. It covers defining key data, assessing the impact of MDM, creating a common data quality vision, and the importance of an enterprise data model. Specific topics include the data architecture, mapping vendor data to standard definitions, how MDM provides a single customer view, the role of the customer master index, and how MDM supports both CRM and BI applications.
Local Dynamos – emerging-market companies focused largely on their home markets - are beating both local state-owned companies and multinational corporations, thanks to savvy digital strategies and an ability to meet rising consumer expectations. MNCs need to understand how the Dynamos are rewriting the rules in emerging markets.
This document discusses key performance indicators (KPIs) and how to develop them. It provides information on different types of KPIs, including process, input, output, leading, and lagging KPIs. The document also outlines steps for creating KPIs, such as defining objectives, identifying key result areas and tasks, and determining how to measure results. Additionally, it discusses common mistakes to avoid when developing KPIs, such as creating too many KPIs or not linking them to organizational strategy.
This document discusses digital twin technology. It begins with an introduction and history, explaining that digital twins were first introduced in the 1960s and the term was coined in 2002. It then describes the types and components of digital twins, how they work by connecting a physical object to a virtual model, and their applications in manufacturing, healthcare, smart cities, and more. Key advantages are improved decision-making and predictive maintenance, while disadvantages include data and security issues. The future of digital twins is discussed as becoming more integrated into IoT and playing an important role in industries.
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
The document discusses creating a data-driven culture and organization. It provides advice on building a data-driven culture, developing the right team and skills, adopting an agile approach, efficiently operationalizing insights, and implementing proper data governance. Specific recommendations include establishing executive sponsorship, advocating for data use, developing data science, engineering, and analytics teams, prioritizing work using agile methodologies, and communicating a business roadmap to operationalize insights.
What are some of the trends that driving the manufacturing industries in this digital economy era? What do these mean for the manufacturing businesses and how can they run and sell smarter? Download this presentation deck and learn more.
You can also watch the On-Demand Webinar here: http://tinyurl.com/bcrmAPJ216
AspectCTRM is the only Web-based trade, risk and operations management solution.
Fuel Marketers can now benefit from this leading professional system with an efficient
and cost-effective way to manage streams of trading and transport activity. Traders, risk
managers, schedulers, procurement and back-office personnel rely on this comprehensive,
affordable solution.
LLamasoft provides the technology and expertise to help companies visualize, optimize and simulate the supply chain for major improvements in cost, service, sustainability and risk mitigation. Design alternatives and explore the trade-offs associated with change. Test results under real-world conditions using simulation. With LLamasoft you can do it all in a single, integrated software platform.
1) AI and automation technologies like RPA, machine learning, and computer vision can address uncertainties and inefficiencies in supply chains by optimizing tasks like demand forecasting, procurement, inventory management, and predictive maintenance.
2) Increased transparency through real-time tracking and monitoring enabled by technologies improves visibility across supply chains and drives efficiency.
3) Machine learning and behavioral analytics can make logistics operations safer by monitoring driver behavior and predicting accidents through advanced driver assistance systems.
Tracxn - Top Business Models - Artificial intelligence Industry Application...Tracxn
Tracxn's proprietary #taxonomy brings to you top #BusinessModels in Artificial Intelligence - Industry Applications rebrand.ly/u3hvir3
Get our free reports on #PracticeArea or #sector of your interest to your mailbox regularly https://rb.gy/cx2upn
Almost everything in your warehouse will eventually be connected to the Internet of Things (IoT), the online network that connects and exchanges data between devices, vehicles, inventory and even buildings. The Internet of Things is already starting the digital transformation of supply chains by improving efficiency, accuracy, and reducing costs. It is anticipated that many more benefits and opportunities will emerge in the future. Every supply chain business, from manufacturing to logistics, should be taking a close look at the Internet of Things now. Early adopters of IoT technologies will be better able to take a giant leap forward in their business and gain a competitive edge from this technology.
Nurturing Digital Twins: How to Build Virtual Instances of Physical Assets to...Cognizant
To embark on the digital twin jounrey, assess your readiness, define and communicate a vision, set common data management rules and build in flexibility for intelligence.
Most of the attention around Analytics goes to the results of the Analytics activity - a better customer profile, a new target market, more efficient product design. What about the process and infrastructure that is needed to get the data to the point in which it is useful to the Analytics community? This presentation addresses the less glamorous, but critically important side of Analytics: the people, process and technology infrastructure that enable an analytics-driven organization.
The presentation will cover these questions:
• How do you align your information assets to your Analytics goals? What data do you need and where do you find it?
• What are the organizational constructs that need to be considered to integrate Data Governance and Analytics?
• What organizational change can be anticipated and how should it be addressed?
• How do you design your data management and data governance programs to support Analytics? How is this different than an operational use case?
This slide deck is drawn from a tutorial presented jointly with Samra Sulaiman of ConsultData at Enterprise Dataversity 2015.
A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.
This document provides a reference architecture for building a digital banking environment using VMware Cloud on AWS and AWS native services. It outlines key components like SDDCs for core banking and retail banking workloads, networking components like Transit VPC and Transit Connect, and how various AWS services can be integrated for capabilities like analytics, machine learning, security, and operations. The architecture shows account structures, network topology and flows, and examples of workloads and services across the environment.
Master Data Management (MDM) is a systematic approach to cleaning up customer data so businesses can manage it efficiently and grow effectively. MDM helps businesses achieve a single version of truth about customers. It deals with strategies, architectures, and technologies for managing customer data, known as Customer Data Integration (CDI). Implementing MDM requires gaining commitment from senior management, understanding business drivers and resource requirements, and providing estimates of benefits like reduced costs and increased sales. A pilot project should be proposed before a full implementation to demonstrate value and gather feedback.
7 Amazing Examples of Digital Twin Technology In PracticeBernard Marr
Digital twins are a virtual simulation of real-world objects. By using Internet of Things sensors that feed data from the physical object to computers, digital twins provide the exact same situation to study and test without the consequences of doing the test in the real world. The uses for the technology are nearly limitless.
This document discusses how smart manufacturing and artificial intelligence of things (AIoT) can help drive digital transformation. It provides examples of how IoT solutions have helped various companies reduce costs and improve operations. It then discusses key concepts in smart manufacturing like the intelligent edge, cloud computing, and different waves of innovation with IoT, edge, and AI. The document outlines Microsoft's IoT portfolio and reference architecture for smart manufacturing. It also describes various Azure IoT capabilities and solutions like IoT Hub, IoT Edge, Time Series Insights, and preconfigured solutions for predictive maintenance, remote monitoring and connected factories. Finally, it discusses how machine learning can address supply chain optimization, predictive maintenance, anomaly detection, production scheduling and demand
I gave a talk at North California Business Marketing Association on Customer Data Platform with examples ranging from Uber Grayballing to Zoom's customer retention email and "dogs or muffins".
This document discusses the digital transformation of high-tech industries. It notes that profit and market value are migrating away from hardware and components towards internet platforms. It identifies trends like artificial intelligence, internet of things, cloud computing and edge processing driving changes. Few product companies have fully transformed, with internet platform companies outpacing spending on research and development. The document outlines a framework for companies to transform their core business while growing new business models in areas like connected products, living products and services, and ecosystem platforms. It emphasizes the need for digital talent and factories to drive transformation.
The presentation discusses master data management and reference data. It covers defining key data, assessing the impact of MDM, creating a common data quality vision, and the importance of an enterprise data model. Specific topics include the data architecture, mapping vendor data to standard definitions, how MDM provides a single customer view, the role of the customer master index, and how MDM supports both CRM and BI applications.
Local Dynamos – emerging-market companies focused largely on their home markets - are beating both local state-owned companies and multinational corporations, thanks to savvy digital strategies and an ability to meet rising consumer expectations. MNCs need to understand how the Dynamos are rewriting the rules in emerging markets.
This document discusses key performance indicators (KPIs) and how to develop them. It provides information on different types of KPIs, including process, input, output, leading, and lagging KPIs. The document also outlines steps for creating KPIs, such as defining objectives, identifying key result areas and tasks, and determining how to measure results. Additionally, it discusses common mistakes to avoid when developing KPIs, such as creating too many KPIs or not linking them to organizational strategy.
This document discusses digital twin technology. It begins with an introduction and history, explaining that digital twins were first introduced in the 1960s and the term was coined in 2002. It then describes the types and components of digital twins, how they work by connecting a physical object to a virtual model, and their applications in manufacturing, healthcare, smart cities, and more. Key advantages are improved decision-making and predictive maintenance, while disadvantages include data and security issues. The future of digital twins is discussed as becoming more integrated into IoT and playing an important role in industries.
Creating your Center of Excellence (CoE) for data driven use casesFrank Vullers
The document discusses creating a data-driven culture and organization. It provides advice on building a data-driven culture, developing the right team and skills, adopting an agile approach, efficiently operationalizing insights, and implementing proper data governance. Specific recommendations include establishing executive sponsorship, advocating for data use, developing data science, engineering, and analytics teams, prioritizing work using agile methodologies, and communicating a business roadmap to operationalize insights.
What are some of the trends that driving the manufacturing industries in this digital economy era? What do these mean for the manufacturing businesses and how can they run and sell smarter? Download this presentation deck and learn more.
You can also watch the On-Demand Webinar here: http://tinyurl.com/bcrmAPJ216
AspectCTRM is the only Web-based trade, risk and operations management solution.
Fuel Marketers can now benefit from this leading professional system with an efficient
and cost-effective way to manage streams of trading and transport activity. Traders, risk
managers, schedulers, procurement and back-office personnel rely on this comprehensive,
affordable solution.
LLamasoft provides the technology and expertise to help companies visualize, optimize and simulate the supply chain for major improvements in cost, service, sustainability and risk mitigation. Design alternatives and explore the trade-offs associated with change. Test results under real-world conditions using simulation. With LLamasoft you can do it all in a single, integrated software platform.
This document describes TSO Logic, a company that provides metrics and automation software for data centers and cloud computing. It offers three main products - TSO Metrics for ongoing IT insights, TSO Motion for automated provisioning and workload movement, and TSO Match for algorithmic brokering. The platform uses patented algorithms to analyze data from data centers and clouds, optimize resource utilization, reduce costs, and enable automated decision-making and workload placement. It integrates with other tools and is used by enterprises to optimize their on-premise and cloud computing environments.
In medicine - an MRI can quickly reveal a hidden ailment and actionable insight to get better. For IT and business leaders whose key concern with the mainframe is the platform costs and lean operations - the CA Mainframe Resource Intelligene reveals multiple sources of hidden mainframe costs and operational inefficiencies along with actionable recommendations.View this slideshare to understand how this new SaaS offering from CA brings together automation, speed, analytics and mainframe expertise of 40+ years. CA Mainframe Resource Intelligence reports answer your CIO’s toughest questions about mainframe optimization and potential for digital transformation.
For more information, please contact your account director or mainframe specialist at:
http://ow.ly/PALG50htHgF
The document discusses four components of an effective supply chain modeling platform: 1) A unified optimization and simulation engine, 2) Automated model building for simplified data analytics and documentation, 3) Cloud-based model solving and collaboration, and 4) A shared service center/center of excellence for supply chain design. The platform allows businesses to build a "living, digital model" of their end-to-end supply chain to enable continuous improvement, answer "what-if" questions, and rapidly adapt to changing market conditions.
An Integrated Simulation Tool Framework for Process Data ManagementCognizant
Digital simulations play an increasing role in product lifecycle management (PLM) processes and simulation data management (SDM) based on the PLM XML protocol, which is a key interface with computer-aided engineering (CAE) applications. We offer a framework for aligning SDM with the overall product development process to shorten lead times and optimize output.
ZDLC (Zero Deviation Life Cycle) is a set of engineering tools used in the end-to-end lifecycle of systems to drive down costs and accelerate delivery through automation and improved quality. It embraces agile iterative development while using executable models to reduce gaps between requirements and the built system. Key components of ZDLC include Smart Process Discovery (SPD) which enables extraction and modeling of existing systems, and User Activity Profiler (UAP) which intelligently captures user actions to document and validate business functions. ZDLC provides precise documentation of systems that is continuously updated, accelerates remediation, reduces testing time, and assessments impact of changes.
This presentation talks about Software Defined Vehicles, Automotive Standards including Cyber Security and Safety, Agile Methods like SAFe/Less , Continuous Delivery best practices.
This document describes Siemens' Manufacturing Execution System (MES) solution called SIMATIC IT. Some key points:
- SIMATIC IT establishes transparency in manufacturing plants and fully integrates information flow.
- It offers a range of components to optimize planning, execution, documentation and processes, enabling seamless integration.
- Using SIMATIC IT can help companies achieve greater efficiency, quality, delivery reliability, shorter cycle times and full traceability.
ImpaktApps provides an enterprise low-code platform to build applications using drag-and-drop tools that allow users to quickly model complex business rules and processes. The platform includes modules for rules management, business process management, API management, and user interface design. It offers a best-in-class payout automation solution and an all-in-one digital underwriting solution. The rules engine allows users to author and maintain rules using a non-technical language and visualize decision flows. The platform is cloud-native and microservices-based.
The document provides an overview of the services offerings of Amdox including security, application management, business intelligence and analytics, testing and QA services, integration services, application development services. It then details their competency in various Microsoft technologies and tools. Finally, it outlines their engagement approach including agile competency, engagement models and services.
The document discusses innovation through platforms and modeling at the "edge." It describes how platforms can create and capture value by enabling new applications and business outcomes. Specifically, it outlines Merck's plans to develop a Scientific Modeling Platform that would integrate data and predictive models across research, development, and medical domains. This would allow modeling to be used more holistically and help advance the most promising drug candidates. The platform aims to drive innovation by supporting new collaborations, capabilities, and business models.
Pistoia Alliance US Conference 2015 - 1.1.2 Innovation in Pharma - Chris WallerPistoia Alliance
The document discusses innovation through platforms and modeling at the "edge." It describes how platforms can create and capture value by enabling new applications and business outcomes. Specifically, it outlines Merck's plans to develop a Scientific Modeling Platform to integrate data and predictive models across research, development, and medical domains. This platform would support collaborative modeling efforts and drive innovation by providing predictive insights earlier in the drug development process. Ultimately, the platform aims to transform drug discovery and development at Merck through increased use of analytics, data-driven decision making, and more successful projects.
Build end-to-end solutions with BlueMix, Avi Vizel & Ziv Dai, IBMCodemotion Tel Aviv
The document discusses IBM's cloud platform Bluemix. It provides an overview of Bluemix, describing it as an open platform for developing and hosting applications that simplifies tasks associated with managing infrastructure at internet scale. Bluemix is built on IBM's Cloud Operating Environment architecture using Cloud Foundry as an open source PaaS. It enables developers to rapidly build, deploy, and manage cloud applications while tapping into available services and runtimes provided by IBM and other ecosystem partners. The document outlines some key Bluemix concepts and components such as applications, services, organizations/spaces, and buildpacks.
Zeller Edm Summit Agile Deployment Of Predictive AnalyticsRonald.Ramos
The document discusses leveraging predictive analytics and cloud computing. It outlines using the R programming language for predictive model development, PMML for model integration and deployment, and Amazon EC2 for scalable predictive model execution in the cloud. The overall process involves developing predictive models with R, exporting them to PMML, deploying them on EC2 using a platform like ADAPA for real-time predictive analytics in a scalable, standards-based way.
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2. SIMULATION
SOFTWARE COMPARISON
Simulation sets standards in business performance
optimization. Representing complete digital footprints of
the existing systems, simulation models allow managers
to experience the consequences and benefits of their
decisionsinarisk-freeenvironment.Oncecreated,amodel
can help detect various issues sooner, solve them faster,
predict aftermaths to a much higher degree of accuracy,
design and build better products, and, ultimately, better
serve customers.
Creating a bridge between the physical and digital
worlds, simulation models are applied in most business
areas, including manufacturing and material handling,
transportation,warehouseoperations,assetmanagement,
and business processes. When simulating complex
business systems, a multi-purpose simulation tool is
needed. It helps incorporate various business processes in
one model, better represents their interconnections, and
optimizes them in a transparent manner.
This paper provides a comparison of simulation
software, as well as a general gauge of the products’
capabilities, special features, and usage. It is based
on the biennial survey, from the October 2017 issue of
OR/MS Today magazine. The magazine is published in
conjunction withThe Institute for Operations Research and
the Management Sciences (INFORMS), which also holds
INFORMS Analytics and WinterSim conferences, as well as
providescertification,career,andnetworkingopportunities.
We selected the most popular tools and most
relevant parameters, and then compiled the information
in this whitepaper. Use this comparison to analyze and
contrast simulation software, and then choose the
product that will solve your challenge and drive your
business results.
FOR SIMULATING
COMPLEX BUSINESS
SYSTEMS, A MULTI-
PURPOSE SIMULATION
TOOL IS NEEDED
3. Imagine That Inc
FlexSim Software
Products, Inc.
Manufacturing, logistics,
and material handling
simulation
Pedestrian Dynamics —
a crowd simulation software
application, designed for the
creation and execution of
large pedestrian simulation
models in complex
infrastructures.
Professional level tool for
modeling and analyzing
complex discrete rate,
continuous, agent-based,
and hybrid systems.
Simulation and modeling
of any process, with the
purpose of analyzing,
understanding, and
optimizing that process.
• Warehouses
• Distribution
centers
• Airports and
harbors
• Healthcare and
pharmaceuticals
• FMCG
• Consumer
products
• Healthcare
• Energy
• Petro-chem
• Pulp/Paper
• Transportation
• Pharmaceuticals
• Semiconductors
• Military and
Government
• Mining
• Manufacturing
• Packaging
• Warehousing
• Material handling
• Supply chain
• Logistics
• Healthcare
• Factory
• Aerospace
• Mining
ExtendSim DE — entry-level
general-purpose, discrete
event and continuous
simulation tool.
FlexSim Healthcare —
simulation and modeling
to analyze, optimize, and
better understand healthcare
systems.
EXTENDSIM
PRO
FLEXSIM
01
ANYLOGIC AnyLogic North
America
Rockwell Automation
INCONTROL
Simulation Solutions
Multimethod general-
purpose simulation tool.
Discrete event, agent-
based, and system dynamics
modeling.
Used for simulating and
analyzing existing and
proposed systems as well as
operational analysis.
• Supply Chains
• Transportation
• Warehouse
operations
• Rail logistics
• Mining
• Oil and gas
• Road traffic
• Passenger flows
• Manufacturing
and material handling
• Healthcare
• Business processes
• Asset management
• Marketing
• Social processes
• Defense
• Manufacturing
• Supply chains
• Government
• Healthcare
• Logistics
• Food and
Beverage
• Packaging
• Mining
• Call Centers
anyLogistix — supply
chain simulation and
optimization software.
AnyLogic Cloud – web
service that allows
AnyLogic users run and
access models from a web
browser on any device,
compare results, create
custom dashboards,
and perform various
experiments
ARENA
ENTERPRISE
DYNAMICS
VENDOR
AND
MARKETS
N/A
VENDOR TYPICAL
APPLICATIONS
PRIMARY
MARKETS
VENDOR’S OTHER
SOFTWARE
4. 01
VENDOR
AND
MARKETS
Simio LLC
Siemens Product Lifecycle
Management Software Inc.
Lanner
Ideal product for professional
modelers and researchers.
Powerful object-oriented
modeling and integrated 3D
animation for rapid model
Discrete-event simulation,
visualization, analysis and
optimization of production
throughput, material flow, and
logistics
Fast, productive predictive
simulation desktop
software for professional
modelling and application
development.
• Business planning
• Process optimization
• Decision making
• Academic
• Aerospace defense
• Airports
• Healthcare
• Manufacturing
• Mining
• Military
• Oil and gas
• Supply chains
• Transportation
• Automotive OEM
and supplier
• Aerospace
and defense
• Consumer products
• Logistics
• Electronics
• Machinery
• Healthcare
• Consulting
SIMIO
ENTERPRISE
EDITION
PLANT
SIMULATION
WITNESS
PROMODEL
OPTIMIZATION
SUITE
SAS
SIMUL8 Corporation
Discrete-event simulation:
supply chains, resource
management, capacity
planning, workflow analysis,
and cost analysis.
Assembly Line, Line
Balancing Strategic
planning, Operations,
Healthcare Systems, BPMN,
Lean, Shared Services,
Capacity Plan
Process optimization and
Improvement, Resource
utilization, System capacity
and throughput, Constraint
analysis, LSS
• DoD and Government
• Manufacturing
• Pharmaceutical
• Logistics
• Warehouse and DC
Enterprise Portfolio Simulator
— web-based simulation
analysis of multiple,
simultaneous project plans
FutureFlow Rx — ADT
Decisioning, Patient Flow and
Bed Management
MedModel — a dynamic,
animated computer simulation
of clinical environment
Process Simulator — flowcharts
and process diagrams
simulator
ProModel Corporation
• Manufacturing
• Banking
• Pharmaceuticals
and healthcare
• Energy
• Government
agencies
• Retail
• Education
• Transportation
• Manufacturing
• Healthcare
• Education
• Engineering
• Supply chains
• Logistics
• Government
• BPMN
• Lean
• Automotive
• Call centers
SAS SIMULATION
STUDIO
SIMUL8
PROFESSIONAL
N/A
N/A
N/A
N/A
N/A
VENDOR TYPICAL
APPLICATIONS
PRIMARY
MARKETS
VENDOR’S OTHER
SOFTWARE
5. Windows
WITNESS N/A N/A
02
TECHNICAL
COMPATIBILITY
Windows
Windows
• Microsoft Azure
• Wonderware
• OptQuest
• .Net Programs
(over 60 languages
supported)
• Excel, Access,
SQL Server, MySQL
• Excel
• Stat::Fit,
• OptQuest
• SQL Databases
• Matlab
• Excel
• SAP
• Simatic IT
• Teamcenter
• Autocad
• Microstation
• Wonderware
• OptQuest
• .Net Programs
(over 60 languages
supported)
Microsoft Excel and any
COM enabled IDE
• Parameterizing from
MS Excel
• Siemens PLCSIM Advanced
• OPC, OPC UA, ODBC
• MS Windows
• Oracle
SIMIO
ENTERPRISE
EDITION
SIMUL8
PROFESSIONAL
PLANT
SIMULATION
MULTIPROCESSOR
CPU SUPPORT
Windows, Linux
YES
SAS and JMP software, either
run externally or embedded
via SAS Program block.
Any program that
can launch a Java
application.
PROMODEL
OPTIMIZATION
SUITE
SAS
SIMULATION
STUDIO
SUPPORTED OPERATING
SYSTEMS
COMPATIBLE SOFTWARE
TO PERFORM SPECIALIZED
FUNCTIONS
BEING CONTROLLED OR
RUN BY AN EXTERNAL
PROGRAM
Windows, Mac, Linux
Windows
Windows
Windows, Mac
Windows
ANYLOGIC
• Excel, Access,
and any database
• OptQuest
• Stat::Fit
• Any Java / DLL
library e.g. for bayesian
or neural networks.
OptQuest
• Excel, Oracle,
Access, SQL Server,
MySQL
• Stat::Fit
• JMP
• Minitab
• any custom DLL
• Excel and other
database software
• C++ applications
Any Windows application
that can be configured as an
Automation controller, such as
Excel or Access, can control and
communicate with ExtendSim
as a COM Automation Server.
OLE and ActiveX
AnyLogic models can be
exported as standalone
Java applications that can
be run from/by any other
application. They could be
also run online via AnyLogic
Cloud web service.
Visual Studio for the purpose
of automation as well as VB
ARENA
ENTERPRISE
DYNAMICS
EXTENDSIM
PRO
FLEXSIM
OS
Windows • Excel and Access
• Stat:Fit
• MiniTab
• Excel and Access,
• C#
• VB and VBA
N/A N/A
Windows
6. 03
MODEL
BUILDING
CODE REUSE
FLEXSIM
PROMODEL
OPTIMIZATION
SUITE
SAS
SIMULATION
STUDIO
SIMIO
ENTERPRISE
EDITION
SIMUL8
PROFESSIONAL
PLANT
SIMULATION
WITNESS
16 statistical distributions
available. Integrated with
Stat::Fit.
Via JMP and SAS software
integration.
Custom options within the
software and Stat::Fit.
22 predefined distributions
Works with ExpertFit and
Stat::Fit. Supports table
driven input sampling.
• Output Viewer
• Minitab
• Excel
• Datafit
• Charts
• Sankey
• Bottleneck analyzer
• Energy Analyzer
• Neural networks
Output analysis via SAS
software products. Steady
state analysis included.
SMORE Plots for risk analysis,
sensitivity analysis, custom
dashboards, comprehensive
data in pivot tables, export
summary or details to
external packages
INPUT DISTRIBUTION
FITTING
GRAPHICAL MODEL
CONSTRUCTION
OUTPUT ANALYSIS
SUPPORT
ANYLOGIC
ARENA
ENTERPRISE
DYNAMICS
EXTENDSIM
PRO
31 predefined distributions
and custom distributions.
Stat::Fit, ExpertFit, and other
software for distribution
fitting.
35 predefined distributions.
Stat::Fit software for
distribution fitting.
Arena Input Analyzer to fit
distributions
Autofit — an internal
feature
Integrated with ExpertFit
Arena Output Analyzer and
Process Analyzer to review
results and users may use
external products as well
A full suite of charts and
graphs in the Dashboard, as
well as extensive Excel output
options.
• Reports
• Model execution logs
• Charts
• Output to the built-in
database or any external
data storage (databases,
spreadsheets, text files)
• Output to charts
reports
• Integrated Scenario Manager
with dialog or database
factors and responses,
sensitivity analysis,
confidence intervals, Gantt
charts, and quantile and
interval statistical analysis.
• Export to external
analysis applications
is also available.
YES YES
N/A N/A
N/A
Experiment Wizard – an
internal feature
7. 03
MODEL
BUILDING
FLEXSIM
PROMODEL
OPTIMIZATION
SUITE
SAS
SIMULATION
STUDIO
SIMIO
ENTERPRISE
EDITION
SIMUL8
PROFESSIONAL
PLANT
SIMULATION
WITNESS
SimRunner
OptQuest
Cloud Deployment,
Experimentation,
Optimisation
Via data transfer to SAS/OR
software; can be embedded
in a simulation model via
SAS Program block.
OptQuest (option) takes full
advantage of all processors.
Featuring Multi-Objective and
Pattern Frontier optimization
Genetic Algorithm, Layout
Optimizer, Neural networks,
Hill Climbing, Dynamic
Programming, Branch and
Bound
Built-in Pack and Go
functionality
Requires Team Edition or
above to package model
SIMUL8 Studio and SIMUL8
Web Technology
OPTIMIZATION SUPPORT OF MODEL
PACKAGING
RUN TIME DEBUG
ANYLOGIC
ARENA
ENTERPRISE
DYNAMICS
EXTENDSIM
PRO
OptQuest is included,
additionally users can
employ any custom
optimization algorithms.
Models can be exported as
standalone Java applications
or shared online via
AnyLogic Cloud web service.
Evolutionary Optimizer is
included in all versions of
ExtendSim.
Trial version runs any model
built in ExtendSim. Analysis
RunTime version allows for
further model analysis.
OptQuest for Arena Arena Runtime
By providing support
for various third party
optimizers
By providing a free Viewer
License of the software
An optimization engine,
powered by OptQuest, is
available as an add-on.
The free trial version of
FlexSim is capable of running
any simulation model built
with FlexSim.
N/A
N/A
YES
FREE AND
PAID OPTIONS
AVAILABLE
NO
IS THIS FEATURE
FREE?
YES
YES
YES
FREE AND
PAID OPTIONS
AVAILABLE
N/A
N/A
YES
YES
8. SIMIO
ENTERPRISE
EDITION
SIMUL8
PROFESSIONAL
PLANT
SIMULATION
WITNESS
Run manual scenarios
with multiple replications.
Concurrent full use of all
processors. Built-in ranking
and selection
Multiple replications and
scenario management
Experiment Manager
supporting distributed
simulation
NO
YES YES
N/A
03
MODEL
BUILDING
MIXED DISCRETE/
CONTINUOUS MODELING
(LEVELS, FLOWS, ETC.)
FLEXSIM
PROMODEL
OPTIMIZATION
SUITE
SAS
SIMULATION
STUDIO
MODEL BUILDING USING
PROGRAMMING / ACCESS
TO PROGRAMMED
MODULES
BATCH RUN /
EXPERIMENTAL DESIGN
ANYLOGIC
YES YES
ARENA
ENTERPRISE
DYNAMICS
EXTENDSIM
PRO
Flexible user interface
to create the following
experiments: Parameter
Variation, Compare Runs,
Monte Carlo, Sensitivity
Analysis, Calibration, and
custom.
Process Analyzer to run a
series of different model runs
in a batch
By providing Experiment
Wizard and Scenario Manager
An experimentation engine is
built into the software.
Scenario Manager
Experimental design; manual
in the Simulation Studio
interface or automated (with
interactive modifications)
via JMP or SAS software
integration.
Users choose to store
run results in the internal
database or export to
an external application.
DOE includes manual, full
factorial, and two options
each for JMP custom design
and Minitab optimal design.
COST ALLOCATION/
COSTING
YES
N/A
11. SIMIO
ENTERPRISE
EDITION
SIMUL8
PROFESSIONAL
PLANT
SIMULATION
WITNESS
PROMODEL
OPTIMIZATION
SUITE
SAS
SIMULATION
STUDIO
ANYLOGIC
ARENA
ENTERPRISE
DYNAMICS
EXTENDSIM
PRO
FLEXSIM
06
STUDENT
VERSION
MAJOR NEW FEATURES
(SINCE 2015)
VENDOR
COMMENTS
Free AnyLogic Personal
Learning Edition
Free version available
Free version available
$25 download for ExtendSim Adopters; $50
for other students. Research grants are
available to use the full version of ExtendSim
in research projects for advanced degrees.
Free to $ 100
$ 30
Free and a $25-versions are available
Free version available
Free version available
$ 1995
Included with SAS/OR. Integrated with
SAS and JMP analytical capabilities.
Models can incorporate any SAS or JMP
code.
FlexSim is committed to help answer
questions relating to any process in the
most intuitive, easy-to-use interface
possible.
Unified modeling architecture with
powerful internal relational DB flexible
framework to represent widely different
systems
• App overhaul plus new UI,
charts reports
• Advanced Resource Mgmt
• Improved source editing environment
• New import/export capabilities.
• A new graphical tool for process
definition (Process Flow)
• Support for virtual reality
(Oculus Rift, HTC Vive)
• Resource distance traveled statistics
• Identify captured resource units
• In-process resource utilization statistics
• Programmatic export of statistics
• UI enhancements
• Linux support
• Enhanced controls on order
of execution for blocks and block ports
• Extended queueing controls.
• SIMUL8 Studio
• Power Free Conveyors
• Work Item tracking
• Create custom interfaces, Overtime
• Financial Input Summary
• Enhanced worker, robot, mixer, motion
paths and visualization
• New Simtalk, OPC UA and Siemens
PLCSIM Advanced connections
Patented innovations, designed by the Dr.
C. Dennis Pegden team, takes Flexibility
and Rapid Modeling to new levels.
• AnyLogic Cloud, a web service for
sharing models and running them
online on any device.
• The Road Traffic Library for detailed
modeling of vehicle movement on roads.
• The Material Handling Library for the
simulation of manufacturing systems and
operations
The only simulation tool that supports
combining Discrete Event, Agent-Based,
and System Dynamics simulations in one
model.
N/A
N/A
N/A N/A
N/A
N/A
N/A
N/A
N/A
OTHER
INFORMATION
• Improved Support for BIM, CAD
• Improved animation and debugger, etc.
N/A