Mojro Boosts FMCG Giant's Logistics Efficiency by 20%


How Mojro Helped an Indian FMCG Giant Achieve 20% Logistics Cost Savings


India's Fast-moving Consumer Goods (FMCG) industry is “among the top five contributors to GDP.” Currently pegged at roughly $164 billion, it can quickly double by 2027.


With context to this industry, the urban segment commands a market share of more than 65%, and it’s forecasted to grow even further. The offline distribution channel, which includes supermarkets, speciality stores, convenience stores, retail stores, etc., holds the majority share.


With a swiftly growing middle class, an expanding workforce, and sharp urbanisation, businesses must realise that a resilient and efficient urban distribution network is a significant lever for the growth of FMCG in India.


The Indian geography isn’t without its twists and turns. Organisations rapidly seek intelligent automation software to streamline logistics operations and elevate business success.


The client is a renowned player in the Indian FMCG Industry. From rural Indian villages to vibrant metros, their products are present in nearly 90% of all Indian households and about 9 million retail outlets.


Additionally, with a portfolio spanning over a dozen categories, the statistics highlight the client’s near-dominance across product volumegeographic distribution, and localisation strategy successes.

Challenges Faced by the Client

The client needed help with reducing their Lead Time from T+3 to T+1 days.


Furthermore, the client faced recurring losses due to damages caused by packaging, loading, and order deliveries of manually sorteddisorganised SKUs. Pilferages, order returns, and trade receivables further exacerbated the problem.


The model was as follows: Warehouse → Distributor → Outlets.


Additionally, the existing network brought its fair share of problems. Critically, the CFAs controlled distribution of FMCG products without a consistent methodology, resulting in the client needing more visibility into how it fulfilled the demand.


Such shortcomings were verifiable through different metrics:


  1. Inefficient Lead Time, leading to order losses

  2. Lack of knowledge on related costs borne by the CFAs

Shaky Foundation

The client was unequipped to cope with fluctuating orders due to seasonalitydemand spikes, or random acts of God. Such circumstances made them particularly vulnerable to dealing with the impact of business segment-specific scenarios and resuming normal operations swiftly.


A logistics transformation was vital.

Problem Statement

Armed with a deep understanding of the Mojro platform’s capabilities, the client wanted to solve the following problems:


  1. Faster Order Fulfilment with end-to-end real-time visibility of ground-level operations

  2. Enhance Fleet Utilisation to reduce first-attempt delivery failures

  3. Optimal Increase in Labour Utilisation


At a fundamental level, the client required Mojro’s assistance to implement material changes to restrain its unchecked Unit Costs. The team established a baseline to serve as a benchmark for tracking improvements in logistics efficiencies.

Mojro’s Solution

Mojro implemented multi-leg planning across multiple dimensions. Essentially, the Mojro platform helps in the optimal planning of:


  1. Order fulfilment duration

  2. Each vehicle’s container volume

  3. Delivery fleet’s overall tonnage carrying capacity

  4. Total travel distance within each cluster

  5. Time dimensions, such as:

    1. Waiting Time

    2. Service Time

    3. Transit Time

  6. Maximum touchpoints each vehicle can serve in individual trips and

  7. Work around each SKU’s volume


Mojro utilised the client’s data from the previous six months to perform network optimisation and implement a new-age distribution model. Mojro parsed every serviced outlet’s historical data to provide inputs, including buying patterns and distinct geographical constraints.


It implemented Mojro PlanWyse to execute the digital transformation process. Mojro provided the client with the following solutions:

Revised Distribution Model

Mojro assisted the client in implementing a new-age Multi-leg Distribution Plan. Mojro better identified each Cross Dock’s optimal location using the organisation’s dataset.


Going forward, the client migrated to a Hub and Spoke Model. The new flow was as follows: Warehouse → (Mid-mile) → Cross Docks → (Last-mile) → Outlets.

Improved Distribution Network

Mojro helped the client work towards removing dependencies—single points of failure—previously, the CFAs. The changes allowed the client to gain complete ownership of an updated, leaner distribution network.

Optimised Fleet Mix

Together with Mojro, the client migrated towards a revised, optimal Fleet Mix of appropriate size. The goal was to avoid overplanning or underplanning day trips and reduce Total Travel Distance, thereby increasing each trip’s profitability.


Mojro assisted the client in achieving the following:

Logistics Cost

20% savings on Logistics Costs. Mainly, Mojro’s functional capabilities in transportation planning were instrumental, such as:


  1. Space Optimization

  2. Bay Planning

  3. Cross Dock Planning and,

  4. Route Optimised Delivery Plans


Lead Time

The client’s target Lead Time was reduced from T+3 to T+1 days. To achieve this metric, Mojro’s functional capabilities for dispatch and address resolution were vital, including:


  1. Day Planning

  2. SLA Adherence and,

  3. Urban Delivery Planning


Fleet Mix

The client revised its Fleet mix and included new, optimal modes for Last-mile deliveries of each PTL. This approach allowed the client to increase its asset utilisation percentage and decrease the total travel distance of its fleet.


Deploying Mojro’s functional capabilities in transportation planning and driver job automation was critical. These included:


  1. Manual Dispatch

  2. Auto Dispatch

  3. Smart Dispatch

  4. 3D Loading/Unloading Instructions


Warehouse Management

Mojro’s platform played an instrumental role in automating warehouse management, allowing Mojro to “interact” in real-time with:


  1. Warehouse Management System and,

  2. Proprietary Execution Software


Mojro allowed the client to track the delivery status of each order. Monitoring the delivery status refined the client's understanding of changing order types, levels, and distribution across geographies. This shift paved the foundation for proactive logistics planning.


This automation was made possible by Mojro’s functional capabilities in integrating with enterprise systemsdriver job automationtracking/monitoring, and metadata management. These included:


  1. Support for API Integration

  2. Pre-Existing Integrations

  3. Package/SKU Management

  4. Delivery Tracking for Client's Customers



Integration with Mojro has empowered the client in multiple facets.

Digital Transformation

After Mojro’s intervention, the client began implementing a streamlined digitised process:


  1. Migration to an online order-capturing process

  2. Successful migration to Multi-leg planning

  3. Automation of picking and packing processes

  4. Establishing cut-off times for the optimal execution of Mid-mile and Last-mile journeys

Change Management Facilitation

Mojro deployed a two-pronged planning strategy—primary and secondary—resulting in the following gains:


  1. Improved order filling at the Primary (Warehouse) Level for:


  1. Increase in asset utilisation percentage

  2. Improved trip planning

  3. Revamped order segmentation and packing, leading to an increase in volume utilisation percentage


  1. Faster order segmentation at the Secondary (Cross Docks) Level for:


  1. Minimisation of order returns

  2. Optimal utilisation of vehicle types (per tonnage) to increase:

    1. Asset utilisation percentage

    2. Decrease in the total number of trips

Building Resilience

COVID-19 threw a wrench in the global supply chain.


Yet, with the help of Mojro, the client was able to navigate the COVID-19 pandemic and beyond better to fulfil:


  1. Fluctuation in demand from outlets

  2. Spike in order count and,

  3. Change in order sizes


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