The analytical landscape is one that is very much in flux. Big Data, cloud services, advanced analytics, data science, artificial intelligence, and machine learning are continually innovating & being implementing within organizations to maximize operational effectiveness and competitive advantage. There is a big shift towards businesses leveraging self-service analytics and will become an increasingly important component of a pervasive modern analytics platform deployment.
Organizations will continue to transition to easy-to-use, fast, agile, and trusted modern analytics platforms deployed across the enterprise to create business value from deeper insights into diverse data sources.
Choose Y&L for a scalable, coherent Analytics & Data Science implementation that is adaptable to your changing data requirements. We enable access to accurate and intuitive data for faster and improved decision making, and we implement the appropriate data governance, master data management, and data quality procedures to keep your organization’s data clean, trustworthy, and readily available.
Y&L with its domain expertise (gained through implemented ERP & EIM applications all over the globe)… coupled with superior technical knowledge and above all, the ability to
combine Information Technology and Information Systems to translate them as business technology
and business solutions is ideally poised to partner with you in your quest to continued business
Through combining data, both
internal and external, structured and unstructured (video, voice, text, images, social, et al.), the Y&L
Analytics & Data Practice identifies and implements the appropriate algorithms, data infrastructure,
and strategic roadmaps to either predict or optimize value through a true understanding of the
Exploratory – an approach that employs a variety of techniques to maximize insight into a data set, uncover underlying structure, extract important variables, detect outliers and anomalies and test underlying assumptions.
Descriptive – What is happening currently, based on incoming data. Typically, a real-time dashboard and/or email reports.
Diagnostic – A look at past performance to determine what happened and why. The result of the analysis is often an analytical dashboard.
Predictive – An analysis of likely scenarios of what might happen. The deliverables are usually a predictive forecast.
Prescriptive – This type of analysis reveals what actions should be taken. This is the most valuable kind of analysis and usually results in rules and recommendations for next steps.
Y&L’s global Analytics & Data Science practice encompasses several domains with solutions customized to derive best results for the underlying problems. Y&L’s global Analytics & Data Science practice brings together the latest in Data Science Techniques with leading Business Consulting skills to build models that provide insight and quantify the risks and benefits associated with solutions to complex business problems. The combination of our services portfolio and our global delivery model sets Y&L apart and provides exceptional value for our customers.
Our analytics team brings in-depth knowledge and expertise across a broad spectrum of industries. We believe our client’s success and our growth depends on maintaining strong partnerships with a wide array of key technology vendors and service providers across each of our core capabilities: Big Data, Enterprise Information Management, Business Intelligence and Advanced Analytics. Strategic partnerships with market-leading technology companies allow us to combine best-in-class skills with cutting-edge tools and resources. Key technology partners include: SAP, Oracle, Microsoft, IBM– Cognos, Qlik, and Tableau.
Visibility: Complete overview of the company’s supply chain to effectively monitor and modify processes for increased productivity
Responsiveness: A recommendation system to aid the user in decision making by considering various scenarios and how it affects ATP
Resilience: Estimating the tolerances of supply chain considering the risks occuring at various stages to meet the demand
Improved processes using historic data from established IoT systems.
Accurately forecast the demand through causal forecasting and sensitivity analysis to understand the impact of different variables on the overall demand.
Performing causal analysis to understand the accumulation of inventory, prediction of future inventory levels considering various influencing factors and prevention of inventory pile-up by maintaining the desired stock level.
Allocation and Procurement of Raw material as appropriate to minimize costs and wastage through optimization.
Transport Cost Optimization
Determination of the most efficient means of transporting goods and products to optimize the costs for transportation.
Transport Cost Optimization -3PL
Determination of the most efficient means of transportation for third party logistics.
Vendor Performance & Risk Assessment
Identify, assess, and monitor third-party-suppliers and vendors to assure delivery of services and formulate a risk mitigation strategy.
Production Impurity Prediction
Accurate estimation of impurities in the manufacturing line for products.
Production Routing Optimization
Jointly optimizing production, inventory, distribution and routing decisions using inputs from demand planning and inventory control systems, as well as routing constraints, speeds and cycle times to generate optimized production plans.
PO Release-Contextual Intelligence
Real time recommendation to the user as the transaction is in progress to enable effective decision making.
CAPEX Spend Decisions
Evaluate the impact of the capital spend to the organisation to ensure effective management of finances.
Identify the essential spends of an organization which yield returns and which doesn’t to ensure spend optimization.
Working Capital Management
Evaluate the financial impact of business decisions at various stages of the processes to provide visibility to the management on the cash flow.
Liquidity Forecasting/ Cashflow Projections
Estimation of a company’s ability to meet short term operating expenses using projected cash flows.
Product Portfolio Optimization
Maximizing revenue by optimizing the quantity of products being marketed.
Utilizing the client’s Excel data, a dashboard was created to manage and visualize help desk activities. The Excel data was imported into Tableau and our team implemented numerous best practice data metrics, dimensions, and aggregate key performance indicators.
The dashboard was able to drill-down from a top-level view to a specific resource. Tickets by team and by category were displayed in a bubble chart for easy issue identification. Tooltips were utilized to determine ticket age, owner, last modification, and more.
Y&L provided client real-time PowerBI Dashboards that automatically ingested, transformed, and provided visualization for operational metrics. The dashboards included drill-down into incidents, service requests, change requests, star performers, and trends.
Dashboards were built to be mobile-friendly and a landing page with helpful tips and feedback loops was implemented.
To maximize efficiency, the Q&A functionality within PowerBI was applied; allowing users to type an organic question and retrieve an answer quickly.
Tools Used: Service Now, SQL Server Analysis Services, PowerBI, PBI Q&A API
Predictive Revenue Model
The client wanted to predict, week-over-week, where revenue would be 10-weeks out. Our analysts determined the delta between revenue and guided revenue. If the delta was above the set threshold, our automated business rules identified the product line causing the impact down to the regional level so immediate action could be taken.
This solution was built using an R script for predictive forecasting and visualized via Tableau dashboard. The two most critical quality metrics were RMSE and AIC. The analysts trained the business on these metrics and explained how Tableau’s forecasting functionality worked in conjunction with statistical modeling.
Tools Used: R statistical modeling, Tableau
Addressing a Negative Closing Ratio
Client wanted to determine their pricing sweet-spot on their core product lines along with insight around their quoting lifecycle: won vs. lost quotes. The objective was to create an end-to-end solution prototype consisting of database integration with front-end delivery. Our analysts loaded a Teradata database and, using Tableau, began to compare operational performance against KPIs the analysts established.
In comparing “closing ratio percentage” against the year, quarter, market and sales region, our analysts discovered that one sales region had a significantly lower ratio than the others. After combining the “aggregate average price” with “quote status” at the year, quarter and product level it became evident that some products were priced too high in that region. After lowering pricing for specific products in that region their closing ratio percentage dramatically improved.