Even though brick and mortar still dominate the total sales revenues, retailers are under pressure to evolve as customer expectations rise in the backdrop of digitalization and supply chain resiliency goals.
Omnichannel (B2C) that is driven by Consumer Analytics.
Global inventory visibility across stores, DCs, and omnichannels (e-marketplace).
Supply chain control tower to manage order tracking and cost-to-fulfill.
Omnichannel (B2C) commerce is a fast-paced operation. Timing is everything.
CBC delivers improved demand forecast to your volatile operations using cognitive methods (we use AI/ML to sense demand).
Find out how you can engage us for a quick POV (proof of value) and improve forecast accuracy.
Social media listening, focus group interviews, surveys, emails, chats, call data, consumer reviews, blogs, and forums – are all presenting valuable data that can be turned into opportunities to sell more.
While selecting and implementing a Consumer Analytics solution, CBC ensures ease of use, integration to external data signals, scalability, advanced analytics, and finally customization for brands and their product categories.
The ability to respond and satisfy the advanced needs of Customers requires the ability within the value chain to tailor specific MAKE-MOVE-DELIVER models.
To build a Cost-to-Deliver (C2D) model, digital supply chains with capabilities in manufacturing (product traceability and flexible production planning), distribution (network connections and their cost contribution to transportation and warehousing), and quality (control systems to ascertain quality and sustainability) will be a prerequisite.
Increasing customer demand, disruptions caused in supply chain storage due to Covid, operational nimbleness, and need for flexibility, increasing challenges with labor are causing loss of productivity and escalating costs for operating warehouses.
Digitizing warehouses and preparing them for the future is a ripe opportunity. With technology advancement, use of cognitive methods, and push from Industry 4.0 compliance, warehouse operators can take advantage of the changing world of consumer and supply chain execution.
Average lead times, supplier reliability, demand distribution, and changing consumption patterns – all have led to problems of rising transaction costs, holding costs, and expiration costs of inventory across industry verticals. Poor visibility on inventory availability further deteriorates the situation.
Data science and edge technologies such as IoT can be leveraged to predict and deliver forecasts/plans to rebalance inventory in the supply chain network. Machine Learning is an apt method to decipher patterns and recognize opportunities to lower procurement and storage costs preemptively.
Retailers have gotten themselves into a corner. This time with their own inventory policy and unidirectional supply chain design that’s now grown old to keep up to challenges including agility.
Retailers need global visibility of their inventory in real-time. Not just that. They need to predict and develop cost-effective strategies to rebalance inventories across their stores, DCs, .Com fulfillment channels including e-marketplaces.
Discount and Price Optimization are critical for Retailers and Consumers alike. Buying decisions for both depend upon their ability to foresee “cost-to-deliver”. Further, markdowns, promotions, product-mix, omnichannel fulfillment, and anticipatory commerce – all need attention to detail.
Can Retailers accomplish all the value benefits and fast-impact making programs fast enough?
With the onset of the pandemic in March 2020, this lifestyle retailer was under pressure to conceptualize and implement a cost-effective strategy for their last-mile delivery.
How did the retailer know where to start keeping their last mile in mind?
