Implementing Big Data Analytics In Supply Chain Management CRP CD

Slide 1: This slide introduces Implementing Big Data Analytics in Supply Chain Management. State Your Company Name and begin.
Slide 2: This slide is an Agenda slide. State your agendas here.
Slide 3: This slide shows a Table of Contents for the presentation.
Slide 4: This slide is an introductory slide.
Slide 5: This slide showcases various pain points faced by company in supply chain and logistics processes such as regarding poor compliance, delivery delays etc.
Slide 6: This slide shows analysis of company supply chain and logistics operational performance using KPI’s such as on time delivery, lead time, carrying cost etc.
Slide 7: This slide presents selection of most suitable solutions for effective supply chain management such as AI, blockchain, IoT and big data analytics.
Slide 8: This slide represents reasons to choosing big data analytics for product traceability, inventory management and real time order tracking etc.
Slide 9: This slide is an introductory slide.
Slide 10: This slide provides an overview of implementing big data analytics in business.
Slide 11: This slide entails how big data analytics in revolutionizing supply chain through assessing customer behaviors, supplier management etc.
Slide 12: This slide puts integration of big data with product and logistics data which includes planning, provisioning, performance, delivery and logistics.
Slide 13: This slide denotes characteristics of big data analytics in supply chain which includes elements such as volume, velocity , variety, value and veracity.
Slide 14: This slide demarcates steps involved in supply chain data analysis which includes data collection, storage, data mining, data analysis and analyzing results.
Slide 15: This slide demonstrates various challenges faced in implementing supply chain analytics such as data silos, poor data quality and ineffective data management.
Slide 16: This slide is an introductory slide.
Slide 17: This slide highlights global supply chain analytics market overview with annual growth rate, key market drivers and major business players.
Slide 18: This slide illustrates segmentation analysis of supply chain analytics market based on end users and region.
Slide 19: This slide contains various stats related to supply chain analytics solutions which includes stock inventory, logistic operations, optimal supply chains etc.
Slide 20: This slide constitutes various growth drivers of supply chain analytics market.
Slide 21: This slide caters to major software providers for supply chain analytics such as Accenture, aera technology, Birst Inc., Capgemini SA and Genpact limited
Slide 22: This slide puts recent technological development in business analytics market which are software intelligence platform, analytics suite etc.
Slide 23: This slide is an introductory slide.
Slide 24: This slide showcases emerging trend of rise in adoption of demand planning software with growth drivers such as supply chain issues, edge computing etc.
Slide 25: This slide shows emerging trend of high developed distributed channel which includes direct sales, indirect sales, online marketplaces, best practices etc.
Slide 26: This slide highlights growing trend for rise in supply chain management which includes logistics management, transportation planning etc.
Slide 27: This slide is an introductory slide.
Slide 28: This slide highlights various data analytics techniques used for supply chain management such as descriptive, predictive and prescriptive analytics.
Slide 29: This slide illustrates various applications of descriptive analytics in supply chain management such as demand analysis, evaluating vendor performance etc.
Slide 30: This slide presents use cases of predictive analytics in supply chain operations such as improving supply chain visibility, quality control and risk management.
Slide 31: This slide pertains to various applications of prescriptive analytics in supply chain processes such as inventory optimization, vendor sourcing, dynamic pricing etc.
Slide 32: This slide highlights comparison of descriptive predictive and prescriptive analytics techniques based on data used, technology, outputs, use case etc.
Slide 33: This slide is an introductory slide.
Slide 34: This slide puts various types of supply chain data which are collected such as stores, customers, products, sales, orders, shipping and delivery data.
Slide 35: This slide showcases classification of supply chain data sources based on operations such as EDI purchase orders, ERP &CRM transaction data etc.
Slide 36: This slide is an introductory slide.
Slide 37: This slide provides overview of supply chain analytics tools and techniques which includes elements such as distribution networks, supply chain planning etc.
Slide 38: This slide shows key elements of supply chain analytics software which includes visualization, analytics, data storage, integration, sources etc.
Slide 39: This slide elaborates major functions of supply chain analytics software which includes procurement, supplier, inventory analytics with KPI’s to be tracked.
Slide 40: This slide entails valuable insights gathered due to integration of supply chain analytics which includes prediction reports, trend analysis etc.
Slide 41: This slide showcases key features of effective supply chain software such as connected, collaborative, cyberware, cognitively enabled and comprehensive.
Slide 42: This slide shows cost estimation for supply chain analytics solution implementation based on factors such as, data complexity, data volume, reporting etc.
Slide 43: This slide highlights factors to be considered while choosing data analytics software such as data security, user friendly , scalability and pricing.
Slide 44: This slide provides overview of supply chain analytics tools which includes elements such as data preparation, supply chain data reports, data ingestion etc.
Slide 45: This slide marks features of supply chain analytics software which includes supplier forecasting, demand planning, supply chain management etc.
Slide 46: This slide projects overview of supply chain analytics tool which includes elements such as manufacturing scorecard, supplier rating system, tracking reports etc.
Slide 47: This slide pertains to comparative assessment between supply chain analytics solutions such as Microsoft power BI, Birst , PeopleSoft oracle.
Slide 48: This slide is an introductory slide.
Slide 49: This slide showcases applications of big data analytics in business environment such as demand planning, inventory management and procurement assessment.
Slide 50: This slide contains overview of leveraging big data analytics for demand forecasting which includes elements such as financial planning, risk assessment etc.
Slide 51: This slide caters to importance of product demand forecasting through inventory management, customer satisfaction and production planning.
Slide 52: This slide concludes demand forecasting framework which helps generating insights based on social media patterns, competitor analysis, sales numbers etc.
Slide 53: This slide entails various techniques used in supply chain planning such as regarding macro, micro, short term and long term demand forecasting methods.
Slide 54: This slide puts steps for effective demand forecasting in supply chain such as data collection, assessing factors, recording demand trends, take action etc.
Slide 55: This slide demarcates various strategies adopted by business to improve demand forecast accuracy such as collaborative prediction, market research etc.
Slide 56: This slide is an introductory slide.
Slide 57: This slide denotes overview of analytics techniques used for inventory optimization which includes elements such as performance monitoring, stock availability etc.
Slide 58: This slide demarcates impact of using data analytics techniques for effective inventory management such as improving process efficiency, sales revenue etc.
Slide 59: This slide entails dashboard to track sales and inventory turnover which includes elements such as sales, inventory turnover, account payable etc.
Slide 60: This slide elaborates best practices adopted by business to improve inventory turnover rate such as effective marketing, restocking and smart pricing.
Slide 61: This slide embarks dashboard to monitor product stockout levels and identify reasons such as ineffective forecasting, late delivery, product quality issues etc.
Slide 62: This slide showcases various tips to prevent stockouts and improve inventory accuracy such as creating reorder points, determining lead times and demand planning.
Slide 63: This slide shows dashboard to track sales orders which includes KPI’s such as inventory turnover ratio, average inventory, perfect order rate etc.
Slide 64: This slide is an introductory slide.
Slide 65: This slide provides overview of procurement analytics which is used in supply chain with elements such as supplier assessment, supplier defect rate, lead time etc.
Slide 66: This slide shows data sources used to gather supplier and procurement information such as supplier, transactional, public and third party data sources.
Slide 67: This slide entails impact of leveraging procurement analytics in supply chain including improving supplier performance, category and risk management.
Slide 68: This slide presents comparison between various procurement management tool based on software, features, price, free trial and user ratings.
Slide 69: This slide represents dashboard for tracking total defected quantity per supplier which includes elements such as rejected products rate, poor material quality etc.
Slide 70: This slide elucidates various initiatives to improve supplier quality such as supplier quality management, quality control measures and staff trainings.
Slide 71: This slide contains dashboard for analyzing supplier lead time and identifying reasons such as lack of raw material, transportation delays, labor shortage etc.
Slide 72: This slide caters to best practices to reduce lead times and improve cash flow such as prioritizing domestic vendors, increasing order frequency etc.
Slide 73: This slide concludes dashboard for analyzing supplier performance with KPI’s such as defect rate, on time supplies, average procurement cycle, delivery time etc.
Slide 74: This slide is in the continuation.
Slide 75: This slide highlight benefits of implementing strategies to decrease supplier defect rates such as real time tracking, supplier quality check, supplier scorecards etc.
Slide 76: This slide is an introductory slide.
Slide 77: This slide showcases roles and responsibilities of supply chain management team which includes supply chain manager, supply chain analyst and materials manager.
Slide 78: This slide shows comparative assessment of supply chain management software based on features, price, user ratings and best fit.
Slide 79: This slide illustrates effective supply chain management process which includes stages such as planning, procurement, manufacturing, logistics etc.
Slide 80: This slide mentions strategies effective supply chain planning such as demand planning, supplier assessment, production and inventory management.
Slide 81: This slide puts comparative assessment of supply chain management systems such as SAP, Oracle, Blue Yonder, Logility.
Slide 82: This slide showcases supply chain management plan which includes objectives such as strategic planning, procurement, quality control and effective reporting.
Slide 83: This slide is an introductory slide.
Slide 84: This slide puts benefits of implementing big data analytics to optimize supply chain operations and improving order processing etc.
Slide 85: This slide elucidates impact of leveraging big data analytics techniques in supply chain based on KPI’s such as on time delivery, lead time, carrying cost etc.
Slide 86: This slide illustrates benefits of deploying big data analytics for enhancing supply chain product visibility, product quality, product planning and forecasting etc.
Slide 87: This slide is an introductory slide.
Slide 88: This slide puts case study for supplier performance improvement which includes objectives, challenges, initiative and potential impact.
Slide 89: This slide provides case studying showcasing use of predictive analytics to develop internal logistics platform.
Slide 90: This slide is an introductory slide.
Slide 91: This slide showcases future trends in supply chain management such as AI with automation, IoT and new product innovation.
Slide 92: This slide shows future stats related to adoption of supply chain analytics.
Slide 93: This slide shows all the icons included in the presentation.
Slide 94: This slide is titled Additional Slides for moving forward.
Slide 95: This slide showcases supply chain planning processes based on production phases such as procurement, production, distribution, sales, return and support.
Slide 96: This slide shows dashboard for monitoring procurement quality based on KPI’s such as no. of suppliers, return cost analysis, vendor rejection rate etc.
Slide 97: This slide is a thank-you slide with address, contact numbers, and email address.