Order Management & Fulfillment

Edition 4: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases

By Jagadesh Hulugundi posted Tue August 03, 2021 04:45 AM

  

With advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) widely adopted by businesses, there is a large push towards building solutions with modern tools and technical frameworks.. However, most of the time, technical advancements are perceived as growth in the technical maturity ladder for individuals, teams, IT or business organizations. Such situations often downplay the value proposition of embracing new transformations.

As a result, businesses tend to deboard such solutions which can cost precious dollars to the business. In several occasions, businesses have not understood the AI engine making decision on behalf of an experienced personnel who does it manually or overrides AI engine outcomes eventually pushing the developed solution to obsolete path. The trust factor of such AI algorithms is at stake with explainability being the centre of a hidden rationale.

This is the fourth blog in this series which attempts to discover the potential real-word use cases for AI infusion into our IBM Sterling Order Management suite of applications from a business perspective in our customer organisations.

Read the previous blogs here:

Edition 1: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Call Center Agent
Edition 2: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Fulfillment Manager
Edition 3: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Customer Relationship Manager



Edition 4

Persona : Store Manager


Problem statement

  • Retail outlets experiment with planograms suggested by traditional systems to increase footfall conversion rates. Efforts are typically focused on tapping data of store traffic, offline surveys, marketing, and store personnel interests 

  • Very often, is the results are not enough to generate excitement and create new shopping interests for customers in store. Here are a few sample scenarios: 
    -  Ex 1: Return kiosks and POS sales counters have long queues, crowding and inconveniencing other shoppers.
    -  Ex 2: Too may carts pushed by customers in an aisle disrupt shopping flow traffic and create frustration as customers bump into each other
    -  Ex 3: Heavy traffic aisles are narrow while other deserted aisles are wide, which inconveniences customers and dissuades some from entering certain aisles. Because traffic patterns are dynamic in nature throughout the day, it is difficult to understand and position the merchandise for maximizing sales.

  • Experimentation with planograms is generally done and feedback is collected as a post-facto event to re-jigger the store layout models

  • What is needed is a more proactive approach to planogram modelling to create excitement for in-store shoppers and increase dollar value for footfall conversion

       

      Solution outline

      • Dynamic planogram generationtakes into account the context of individual stores and their unique layouts, product selections and customer demand

      • This is driven out of item physical dimensions, shelf/aisle/bay size constraints, current and future demand (orders) at a respective store, and predicted future inventory for stocking up on a week-by-week basis or periodic basis

      • Store managers can perform a what-if analysis on planogram re-modelling and provide justification around several data labels such as:
        • Predicted % rise of footfalls
        • ROI % increase due to newly proposed floor space
        • Store Asset appreciating /depreciating indices
        • Increased % of orders fulfilled through Ship-from-Store (SFS) and Buy-Online-Pickup-in-Store (BOPIS) fulfillment modes

       

      Sample Use Case



      Read the previous editions of this blog series that details the use case for a Call Centre Agent:  

      Edition 1: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Call Center Agent

      Edition 2: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Fulfillment Manager

      Edition 3: AI infusion opportunities for IBM Sterling Order Management suite of applications with real-world use cases | Persona: Customer Relationship Manager

       


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