Grocery Retail, Connected...understand the dynamic nature of these inter-connected influences. AI...
Transcript of Grocery Retail, Connected...understand the dynamic nature of these inter-connected influences. AI...
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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
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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.
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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.
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Sharper insight into shopper behaviours at the macro and local market level.
Case study
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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
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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.
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Significant improvementof on‑shelf product availability
High level of replenishment automation
Case study
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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
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30% shelf gap reductions 2‑3 days reduction in stockholding
In‑store minimisation of waste and markdowns
Case study
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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.
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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.
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>2% pick accuracy improvement
Complete visibility into stock deliveries to the stores
Case study
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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.
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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