Big Data Providers
Big Data Providers, it has been shown that more and more organizations are adopting data analytics to respond to supply chain disruptions and to enhance supply chain management (SCM), with several major disruptions currently impacting the supply chain. The following share big data suppliers, take a look.
Big Data Vendors1Key vendors in the global big data market include Microsoft (US), Teradata (US), IBM (US), Oracle (US), SAS Institute (US), Google (US), Adobe (US), Talend (US), Qlik (US), TIBCO Software (U.S.), Alteryx (U.S.), Sisense (U.S.), Informatica (U.S.), Cloudera (U.S.), Splunk (U.S.), Palantir Technologies (U.S.)
1010data (U.S.), Hitachi Vantara (U.S.), Fusionex (Malaysia), Information Builders (U.S.), AWS (U.S.), SAP (Germany), Salesforce (U.S.), Micro Focus (U.K.), HPE (U.S.), MicroStrategy (U.S.), ThoughtSpot (US), and Yellowfin Tuna (Australia).
These vendors have adopted various organic and inorganic growth strategies such as new product launches, partnerships and collaborations, and mergers and acquisitions to expand their presence in the global big data market.
AWS (US) provides cloud computing services in the form of Web services. The company offers a wide range of products and services to customers in 190 countries. Amazon's portfolio includes segments for compute, storage, database, migration, web and content delivery, developer tools, management tools, media services, machine learning, and analytics. In addition, the Solutions segment offers web sites and web applications, mobile services, backup, storage and archiving, financial services and digital media.
It caters to various verticals such as media and entertainment, automotive, education, BFSI, gaming technology, government, healthcare and life sciences, manufacturing, retail, telecom, oil and gas, and power utilities. In the Big Data market, its products include Amazon QuickSight, Amazon S3, Amazon Glacier, AWS Glue
Big Data Vendor 2A comprehensive analysis of the benefits of Big Data for the supply chain
Nowadays, Big Data has completely crossed the conceptual hype. But in the field of supply chain management, the application of big data technology industry development is in its infancy, but I believe that along with the rapid development of big data in other industries, big data in supply chain management will also quickly follow up, then people will inevitably ask what benefits big data can bring to the supply chain in the end, the following please follow Qian Yuan Kun and I with the The benefits of big data to the supply chain to understand.
Big Data and Supply Chain
1, inventory optimization. For example, SAS's unique and powerful Inventory Optimization Model can be used to minimize supply costs and improve supply chain responsiveness while maintaining high levels of customer satisfaction.
Its inventory costs can drop by 15% to 30% in the first year, and its accuracy in predicting the future rises by 20%, resulting in a 7% to 10% rise in its overall revenue. Of course there are other potential benefits such as increased market share. In addition, with SAS, product quality is significantly improved, and the defect rate is reduced by 10% to 20% as a result.
2, creating operational benefits, from the supply chain channel, and the production site of the instrument or sensor network collected a large amount of data. Using big data to more closely integrate and analyze these databases can help improve inventory management, the efficiency of sales and distribution processes, and the continuous monitoring of equipment. For manufacturing to thrive, companies must understand the cost benefits that big data can produce. Predictive maintenance of equipment is in a position to adopt big data technology now. Manufacturing will be a major source of Big Data operating revenue.
3. B2B e-commerce supply chain integration. Strong e-commerce will lead the upstream downstream production plan - downstream sales docking, this docking trend is the upstream manufacturing outsourcing supply chain management Supply-Chain, focusing only on production Manufacturing, ProductionChain (R& D).
Logistics outsourcing up to supply chain outsourcing is a huge leap, reflecting the strong competitiveness of e-commerce and integration capabilities, massive data support and cross-platform, cross-company docking has become possible. B-B supply chain integration has a strong market space, can improve China's industrial layout, industry chain optimization, optimize the distribution of production capacity, reduce inventory, reduce supply chain costs, improve supply chain efficiency.
4, logistics platform scale development, B-C business model integration has become a reality, but the construction of the logistics execution platform is dragging the bottleneck. The integration of the sales supply chain of multiple products has great technical difficulties, such as supply cycle, inventory cycle, distribution time, logistics operation requirements, such a logistics center is very difficult.
Big data platform construction will drive the overall sales supply chain integration; China's also the reality of cross-regional logistics and distribution, urban-rural differences, etc., the government's control is a major difficulty/troubleshooting, big data platform to help the government to adjust its functions in place.
5, product co-design, in the past we are most concerned about product design. But now, in the product design and development process, the relevant personnel collaborate with each other, the factory and manufacturing capabilities are also synchronized in the design and development. The current pressure is to deliver to the market more competitive, higher configuration, lower price, higher quality . . products, and meeting all of these requirements simultaneously is the next big value proposition for manufacturing and engineering organizations. That's where Big Data comes in.
How are organizations deploying Big Data?
Getting value out of data starts with handling big data, with the ability to ****enjoy, integrate, store, and search huge amounts of data from many sources. And in the case of the supply chain, that means being able to accept data from third-party systems and speed up feedback.
The overall impact is increased collaboration, faster decision-making, and greater transparency for all involved. Traditional supply chains are already using large amounts of structured data, and organizations are deploying advanced supply chain management systems that store resource data, transaction data, supplier data, quality data, and more to track supply chain execution efficiency, costs, and control product quality.
Benefits of Big Data for the Supply Chain
The current concept of Big Data goes beyond the traditional concepts of generating, acquiring, transforming, analyzing, and storing data, with the emergence of unstructured data and a diversity of data content, and the deployment of Big Data will face new challenges.
The challenge of simply processing the massive amounts of information generated, transmitted, and stored today. The volume of data is exploding, and with the adoption of M2M (machine-to-machine communication), this trend is set to continue.
But if these challenges can be addressed, a whole new world can be opened up? The core is in two areas:
1. Solve the problem of data generation, that is, how to use the Internet of Things technology M2M to obtain real-time process data to virtualize the supply chain process. By tapping into the potential of these new datasets and combining them with information from a wide range of sources, it is possible to gain entirely new insights. In this way, companies can develop entirely new processes that are directly linked to all aspects of the full product lifecycle. Integrated with this are reporting and analytics capabilities that provide feedback on the process, creating a virtuous cycle of reinforcement.
2. Solving the problem of data application, how to make the data generated by the various value conversion processes in the supply chain to generate business value, is fundamental to the revolutionary productivity of data deployment. The application of big data in the supply chain is no longer a simple transaction status visualization, support decision-making inventory level, the traditional ERP structure can not afford. Therefore, enterprises must re-do the top-level design of data application, and establish a powerful and comprehensive big data application analysis model, in order to cope with the complexity of the massive data how to play the value of the challenge.
The application of big data in the supply chain field has just begun, with the rapid development of the supply chain, big data analytics, data management, big data applications, big data storage in the supply chain field contains a huge potential for development, the investment in big data is also only with the supply chain combination, in order to produce sustainable, large-scale development of the industry
Big Data Vendor 3Supply chain
I. Types of Supply Chain Cases
A supply chain case can be a case of the entire supply chain from the supply of raw materials all the way to the final product delivered to the hands of the end-users, or it can also be a case that involves only one part of the supply chain or focuses only on a single logistics activity. Whichever case, it should be analyzed from the perspective of the supply chain as a whole, taking into account the impact of changes in a single link on other links in the supply chain.
The goals of supply chain case analysis
Improving customer service levels and reducing total operating costs are two of the main goals of supply chain management, which must be kept in mind when analyzing cases.
Third, the method of supply chain case study
Supply chain case study can be divided into such steps:
First, analyze the current situation of the supply chain.
First of all, analyze the structure of the supply chain, in the analysis can be drawn from the starting point of the supply of raw materials or spare parts, through the manufacturing chain and distribution and delivery chain, until the end user's hands of the flow of goods schematic diagram, the purpose of the schematic diagram is to describe the supply chain of the structure of the various fixed nodes (eg, factories, warehouses) and the flow of goods in the flow of the pattern between these nodes. . That is, the flow of goods.
The information flows and information systems that support the movement of goods are then analyzed, including order information processing, demand forecasting information, management information, and computer systems. Secondly, the current supply chain performance is analyzed, which is very effective in proposing improvement measures. The performance analysis can include the overall performance of the supply chain, the relative performance of the supply chain and the performance of individual logistics functions.
Secondly, problems are identified based on the analysis of the current situation.
This is often the most difficult and important step in case analysis. Because if you can't correctly identify the main problem, you can't make the right choice. It is important to distinguish between symptoms and causes, which are usually easier to identify when analyzing.
For example, the manager may think that the shortage of storage capacity is a problem, in fact, this may be only a symptom, the cause may be poor inventory management or production arrangements are not reasonable and make the inventory greatly exceeds the actual demand. So when analyzing, it is important to find the real cause of the problem.
Third, envision and propose solutions
The solution is closely linked to the current situation analysis, a good analysis of the current situation can be a clear identification of the main issues, so as to point out the correct solution or course of action. There are usually three levels to consider when proposing solutions: at the level of specific functional departments; at the company level, where cross-sectoral reforms are implemented; and at the supply chain level, where companies in the same supply chain collaborate with each other to carry out reforms.
Finally, the proposed program should be fully explained.
The above is to analyze the supply chain problem to provide a framework for thinking and analysis, which is not a universal method applied to all supply chain problems, but listed in the analysis of the problem can be taken into account when the factors, case study should be based on the actual problem to determine the relevant research factors.