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Grocery Retail, ConnectedEight strategies for an efficient, responsive,

and sustainable supply chain

Today’s grocery retailers need to establish demand-driven supply chains that accommodate

constantly shifting consumer preferences and unforeseen disruptions, such as the outbreak of the

COVID-19 virus and the subsequent economic upheavals. Competition is rising on multiple fronts—

not only from large online retailers such as Amazon, but also from a variety of direct-delivery

companies. Leading companies are embracing cloud, internet of things (IoT), advanced analytics,

artificial intelligence (AI), and machine learning (ML) technologies to create digital solutions that

can mitigate these disruptions and deliver the following benefits:

• AI-driven planning and replenishment solutions that increase profits, reduce waste, and

improve collaboration throughout the business

• Real-time supply chain visibility and issue resolution, driven by machine learning algorithms

• Greater choice and convenience for customers by accurately assessing the needs of each store,

every day

It’s an important evolution. For many grocery retailers, essential activities such as planning,

replenishment, and warehouse management are disconnected—each handled by different people,

all of whom have limited visibility into each other’s domains. A modern digital supply chain

platform combines these processes into a unified, end-to-end solution.

Here are eight ways in which these connected platforms can help you compete.

The future of grocery retail



Strategy 1

Broaden Your Perspective with Integrated Planning Tools

Whether it’s the growth of veganism or the focus on fresh, it is critical

that retailers stay ahead of changing consumer tastes. Strategic planning

tools help you set the right strategy and keep that strategy on track by

providing early insight into constantly shifting consumer preferences.

Automation is key. For example, predictive modelling technology helps

planners perceive such shifts in consumer behaviour and respond

accordingly. Analytic forecasts make it easier for planners to anticipate

sales volume, predict inventory needs, and maximise profit margins.

By tracking the correct metrics, you can achieve a forward-looking

view that allows you to plan for tomorrow’s needs.

“We relentlessly seek to improve the way we serve the needs of our customers. Having a predictive, autonomous and adaptive supply chain powered by world class technology products and Sainsbury’s Tech engineering means we can show up for our customers whenever and however they shop with us.”

John Elliott, Chief Technology Officer – Retail at Sainsbury’s.

https://media.blueyonder.com/2020/04/21/sainsburys-feeds-its-supply-chain-strategy-with-blue-yonder/?utm_source=linkedin&utm_medium=organic&utm_campaign=blueyonder


Strategy 2

Optimise Assortments and Store Layouts with Category Management Technology

Meeting the needs of local shoppers is imperative. The right category

management tools help you determine what assortment you should carry

and how your stores should be laid out, from aisle to aisle and shelf to shelf.

By ingesting sales data, demand forecasts, and customer loyalty insights,

data-driven plans help you allocate the right space to each product area. By

using analytics to connect the space and assortment planning processes,

you can determine the right range of products for each local demographic

and deliver optimal experiences to your customers.


Sharper insight into shopper behaviours at the macro and local market level.

Case study


Strategy 3

Predict Demand through Machine Learning

Customer demand is unpredictable and making stocking decisions based

on data history is no longer a reliable indicator of what customers will

want in the future. That’s why machine-learning applications do not

simply look at what happened in the past, they try to discern why. By

gathering data on sales, inventory and price, alongside many external

factors such as weather, events and holidays, these applications

understand the dynamic nature of these inter-connected influences.

AI technology helps your team understand the true drivers of demand at

a granular level to create accurate forecasts for each item, store and day.

30% improvement in forecast accuracy

Fully automated self‑adjusting forecasts that utilise 200+ influencing factors

Case study



Strategy 4

Automate Replenishment Activities

In a connected supply chain, demand forecasts drive automated

replenishment strategies, from suppliers through distribution centres

to stores. By aligning replenishment with customer demand, retailers

can ensure they have the right inventory in all parts of the supply chain

network. Machine learning technology helps these systems determine

which products to order, and in what quantities, so you can manage

the movement of inventory efficiently throughout your supply chain.

Replenishment plans should also be linked to Category Management

plans. For example, the plans can import shelf minimum/maximum data

from planograms to inform inventory needs.


Significant improvementof on‑shelf product availability

High level of replenishment automation

Case study


Strategy 5

Maximise Stock Availability

Availability remains one of your customers’ top priorities. How can

you maintain the right amount of inventory to meet demand whilst

minimizing waste? A complete replenishment solution uses machine

learning technology to understand variable demand as well as to

calculate opportunities and risks. AI technology can model millions of

different scenarios to recommend the optimum level of inventory to

meet your business strategy—too much inventory and you risk wastage,

too little and you lose sales. By understanding these dynamics at every

store—and for every item, every day—retailers can verify that they

have the optimum stock levels to meet customer needs in the most

profitable manner.N E W FRESH BACK IN STOCK OFFERS

30% shelf gap reductions 2‑3 days reduction in stockholding

In‑store minimisation of waste and markdowns

Case study


Strategy 6

Optimise Pricing Decisions

Making the right pricing decisions is now more critical than ever. By understanding price elasticity,

machine-learning solutions can help you understand the unique impact pricing decisions have

on every item in every store. The best solutions help you automate these decisions throughout

the product lifecycle, from everyday pricing to daily markdowns of expiring goods to delisting

products. By considering inventory levels, expiration dates, competitor pricing, weather, and many

other demand influences, you can analyse profitability for many different pricing scenarios to help

determine the right business strategy.



Strategy 7

Connect Warehousing and Transportation

By combining planning and operational data, an efficient warehouse

management system can help you understand precisely how to organise

your warehouses to enable efficient warehousing and transportation

operations. Wearables, machine learning and other new technologies

power additional efficiencies such as real-time task optimisation.


>2% pick accuracy improvement

Complete visibility into stock deliveries to the stores

Case study


Strategy 8

Flexible Workforce Management

All grocery retailers strive to achieve optimum staffing levels, but

maintaining the right headcount to properly serve customers is a constant

balancing act. Workforce management technology helps retailers allocate

the right number of staff for each store and shift based on forecasted

sales and expected activities. Today’s workforce values flexibility and

the best systems accommodate the lifestyle needs of employees as

well. By enabling your staff to swap shifts, enter time off requests and

do electronic call-offs, you can improve morale and increase employee

retention whilst minimizing costs.


Advanced scheduling for 1.1 million store associatesScheduling system saves time, improves consistency, and boosts flexibility

Case study

Machine learning, advanced analytics, artificial intelligence, and related

technologies have become key differentiators for grocery retailers. These

digital technologies are poised to transform the entire grocery industry

in the years ahead. Many retailers are interested in these technologies

but they are also looking for fast time-to-value. They want ready-to-go

software as a service (SaaS) and cloud solutions that can be configured

quickly and paid for on a subscription basis.

Blue Yonder has what the Grocery industry needs. Our cloud-based

Digital Fulfilment Platform enables connected planning, from category

management to replenishment to warehousing and transportation.

Utilise the Cloud for Rapid Deployment

Visit blueyonder.com/grocery‑retail‑connected to learn more

http://blueyonder.com/grocery-retail-connected

grocery retail, connected...understand the dynamic nature of these inter-connected influences. ai...

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