Retail

  • Profitable Product Mix

    Product Mix

    Overview

    Merchandise selection is a foundation element and drives many things. Product mix, layout and presentation of outlet/ECommerce store. Prompts at Physical store and recommendation engine in online format, help consumers make choices and store in terms of added revenue.

  • Available on Shelf

    Inventory Management

    Overview

    This is the biggest challenge of Retail Industry. Which product, where and what quantity to hold. Dynamics of Forecast, Promotions, Service level, space utilization, presentation, replenishment all comes into picture.

  • Dynamic Pricing & Profit Optimization

    Managing Promotions

    Overview

    Promotions are common way of constant enticements to consumers to bring them back to store (Physical or online stores). Planning campaigns, seller negotiations, communication, inventory & logistics planning all has to be executed like a clock work.

  • Supplier Management

    Replenishment

    Overview

    Backbone of Retail is highly efficient warehouse and logistics operations. Showroom cannot shine without the warehouse being in order. Reordering level triggers, warehouse automation, Replenishment Logistics requires effective and efficient process and high level of digitization.

  • Warehouse, Trucking Operations

    Logistics

    Overview

    Physical and online Retail formats are hugely dependent on Logistics management. Online format is complex on last mile delivery and may be equally complex at regional store tier, depending on nature of business and strategy in use.

    3PL, Vehicle Routing, close coordination with sellers, warehouse, stores and consumers is key to execution success. Flow of goods, information and money can be seen at scale in Retail Industry.

    INDUSTRY CHALLENGES & SOLUTIONS

    Complete Picture- Visibility

    Challenges of Visibility

    Sales data in context with consumer meta data provides lot of insights. Slicing, dicing, segmentation, correlations can help devise better marketing plans.

    Effectiveness of Marketing campaigns is important. There is enough learning to be derived from each campaign by tracking key indicators.

    Drilling down from deviating KPI to data or to Balance scorecard contributors, helps identifying and fixing the root cause.

    Insights into magnitude of churn by segment, geography etc. And discover reasons for churn or at what point in sales / consumption cycle are customers dropping off. These insights are extremely valuable for retention and market share.

    Usually there are signs before something goes wrong. Leading indicators can come in different forms- data points, derived calculations, or some form of unstructured data.

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    VISIBILITY

    Dashboards, KPI/Metric Management, Drill downs, Root cause analysis, Business Intelligence.

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    Accurate Reliable Predictions

    Challenges of Prediction

    Several market and competition events influence demand and price. Being able to predict the pattern well in time is valuable. Some of the data required is unstructured, some structured and some available with field agents requires collaboration. Putting different pieces in right context could give a complete picture.

    Retailed product demand are affected by seasons, promotions, neighborhood (physical format) consumer profile. Ecommerce has more information available for analysis, but still the invisible hand is at play. Geographically spread and large number of Products  poses significant challenge in data handling and Demand planning activity.

    Attributing reasons/ events to significant bump or slip in sales is an important step in curating data. It yields rich dividends. Factor analysis can help keep a pulse of market of what works and what doesnt.

    Targeted marketing is not possible without proper segmentation based on characteristics which set the customers homogeneous within segment and different from rest. Orientation of product features, launch, positioning, service and engagement can all be tuned properly.

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    PREDICTIONS

    Forecasting, Risk Assessment, Pattern Study, Segmentation, Factor Analysis, Price Elasticity, Predictive Maintenance.

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    Optimal decisions

    Challenges of Decision Making

    There are more challenges behind the scene in retail that what is visible in showrooms. Timely Reordering, Replenishment, being ready for promotions requires seamless supplier, logistics, warehouse and retail outlet visibility, intelligent planning & close coordination.

    Promotion should over deliver than anticipation and that needs a lot of moving parts have to work flawlessly in the hectic promotion period. Pricing decisions, mechandise planning, layout changes, supporting backend supply chain, each of these processes can use some decision support intelligence for better revenue targets.

    Balance between Inventory Management and customer service is difficult. Synchronizing the reordering, storage with demand signals will keep working capital and customer service levels in balance.

    Price and demand are inversely related. Revenue and/or profit optimization aims at pricing in such a way that topline or bottomline is optimally increased by inducing enough customer pull.

    Delivery efficiency for inbound or outbound shipments rely on effective use of logistics. Ecommerce players are creating competitive advantage by self managing logistics because of scale they have now. Selection of right size vehicle, selection of route with combination of customers has bearing on cost and service level. For any decent scale operation, planning by hand or on spreadsheet is not going to be effective.

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    Decision Support System

    Supply Chain Planning, Scheduling, Simulation, Line Balancing, Inventory Optimization, What-if Scenario Study, Revenue Optimization- Dynamic Pricing.

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    Efficient Execution

    Challenges of Execution

    Sampling based quality management, SOP’s not followed, Shift Operator availability & skill issues affects Quality management. Compliment your quality assurance process with computer vision, sensor based data gathering and machine learning/AI model based assistance.

    Automation with high precision execution is desirable. And now it is possible with complex identification, segregation or multi-step workflow tasks with help of sensor (IoT field devices) and ML/AI models.

    Surveys inherently are open to many biases and not the best tool for tracking consumer preferences.  There is a lot of unstructured data, news feed, chats, emails, consumer complaint blogs that cannot be consumed by humans. But ML algorithms can assess risk and change in preferences accurately.

    Recommending products and services based on similar characteristics of consumer or his past consumption preferences improves engagement and boosts revenue.

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    Machine Learning & Artificial Intelligence

    Defect Identification, Comprehensive Quality Testing, Automation of Tasks, Sentiment Analysis (Consumer), Early Indicators and Risk Assessment.

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    WHAT'S NEW?

    Need Help?

    Past decade has seen major changes in economics of Oil, Gas & Chemicals.
    Shale Gas, Alternate energy cost coming at par with conventional energy sources, advances in material sciences.

    Ever Improving Delivery Service Levels

    There is a race for improving delivery service levels. Single day delivery is quite possible in developing countries and now the aim is to get to delivery within few hours of order placement.

    Innovative ways in warehouse operations have cut down the time drastically. Delivery using drones or self driving cars are coming out of proof of concept and will soon be reality.

    The age of eCommerce

    ECommerce is replacing brick and mortar stores. Some product segments like electronics the transition is complete, some others it is underway.

    Use of Analytics

    Need of Analytics for insights, foresight, decision and smart execution is ever increasing. A large amount of information is being collected and analyzed to get customers mind share and piece of wallet.

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