Replenish

Intelligent forecasting & replenishment

from source to shelf

Accurate forecasting has never been more

critical. Even before COVID-19 turned the world

upside down, grocery retailing was a difficult

business. Maintaining customer loyalty isn’t easy,

especially with new trends constantly appearing

and online options changing the food shopping

experience. Fundamental business processes

don’t offer visibility into continually shifting

demands and legacy systems are unable to

meet the needs of these changing times.

Legacy forecast systems are at the heart of

the problem. These systems were designed

to emulate a process in which yesterday’s

purchase patterns are expected to be repeated

today. These rigid systems don’t account for

promotional calendars, local events, weather

patterns and dozens of other variables.

Lacking complete visibility into the many

factors that drive consumer demand

and hindered by restrictive and manual

replenishment methods, many grocery

retailers find it difficult to respond to the

day-to-day needs of their customers, and

to the larger objectives of the business.

The result is out-of-stocks, lost sales,

excess inventory, and unnecessary waste.

New normal, new approach

Food waste is costing retailers and the environment

Learn more

Retail has hit a critical juncture. The potential

for artificial intelligence (AI) to successfully

transform the retail paradigm is here, and it is

driven by three primary factors:

• An abundance of data

• A growing desire to embrace automation

• The low cost and innate scalability of cloud

computing models

Blue Yonder’s AI-driven replenishment solutions

deliver significant business benefits, including

30 percent less gaps, two to three days less

inventory (even off a high base), and a typical

return on investment of 12 to 18 months – and

sometimes as rapid as six to nine months.

These solutions leverage an abundance of

available data to help grocery retailers gain a

better understanding of customer behavior.

By evaluating multiple costs across multiple

outcomes, they allow grocery retailers to make

informed decisions about pricing, replenishment,

and assortment.

Surveys indicate that only half of retailers plan

to invest in AI-driven solutions, machine learning,

and data science in the next five years. Will the

retailers that don’t invest in these technologies

be able to survive as others pull ahead?

Click here to read the Blue Yonder and Warwick

University Retail Digital Readiness survey.

Organizations that have

deployed AI grew from 4% to 14%

between 2018 and 2019,

according to a Gartner

CIO survey.

Blue Yonder origins at CERNRead how Michael Feindt’s NeuroBayes

algorithm (now patented by Blue Yonder),

developed during his many years of

scientific research at CERN, enables

grocery retailers to automate complex

decisions across the entire value chain.

Learn more

Traditional forecasting approaches use sales

history as the main driver. A single number

baseline forecast is typically adjusted to account

for predictions such as promotions, weather,

and events. These adjustments are manually

formulated and complex to plan accurately and

granularly. The fundamental weakness with

this approach is that it ignores the relationships

between the various influences on customer

demand, which do not operate in isolation.

Consider the demand for ice cream on a hot sunny bank holiday by the beach: Which factors might

cause you to buy ice cream? Do you buy because it is sunny? Or because it is a weekend? Or because

you have chosen to be beside the beach? Or is it because you are by the beach on a hot Saturday during

a long weekend?

These influences are all connected. You buy ice cream because of all of these simultaneous influences.

Considering each in isolation ignores the underlying reality.

Do you know what your customers are thinking?

Layered Approach – an estimation of behavior

The realityUnderlying reality is different

Many interconnected reasons customers buy

Machine Learning can reveal and predict the underlying dynamic and interconnected reality of demand

A reasonable process for the technology as it stood

Essential ingredients of an AI-Driven demand forecastUnderstand inter-relationships

Dynamic hierarchical learning across products and stores

Explainable AI in simple terms

Automated forecasting by item, store and day at scale

Understand inter-relationships Without AI, it is impossible to understand the

relationships between influencing factors at the

speed and scale a modern grocery retailer needs.

Intelligent solutions look beyond the isolated

influences of each factor. They seek to

understand how each factor impacts the others.

They understand how promotions, events,

weather and other variables impact individual

locations over time, and they even understand

day-level variations.

Good machine learning algorithms can discern

these impacts to deliver new levels of forecast

precision at the item-location-day level.

Blue Yonder AI is able to not only understand

the impact of up to 200 different factors. It also

understands their inter-relationships, driving

significantly higher levels of accuracy.

For example, running a promotion at the

weekend can lead to different buying

patterns from a mid-week promotion.

There can be significant differences

between an Easter in April and an

Easter in March.

This is particularly important at the location

level, where individual price elasticities, local

events and weather come into play. The more

localized the assortment, the more critical

this becomes.

Cust

omer Demand

Modern forecasting systems use AI and ML to understand complex relationships among all influencing factors, and use this as the basis for future predictions.

Dynamic hierarchical learning across products and stores Blue Yonder’s AI forecasting model self-learns

from other items and stores to create forecasts

even when there is little or no historic data,

such as when you are running a promotion or

introducing a new product.

Forecasts can draw on information from similar

promotions or items, including how the new

product will respond to different prices, weather

and events. This enables forecasts to respond

quickly to demand fluctuations. Retailers

can evaluate changes across multiple stores

and products to understand whether these

fluctuations represent an isolated incident or

an emerging trend.

Hierarchical LearningProduct

group

Product

Stores

Format

LocationRegion

New item

Price

Brand

Explainable AI in simple termsOne of the biggest obstacles to AI adoption

is trusting a “black box.” Blue Yonder AI is

different. Because it understands and measures

the importance of influencing factors at the

item, store and day levels, it can inform users

about what drives the forecast, so they can

improve their output. Interventions are lower

and more targeted.

While an AI-driven system may make decisions

using calculations beyond human cognition,

its recommendations should not defy human

understanding. The right forecasting and

replenishment system must present deductions

in an explainable form.

Blue Yonder’s modern user interface helps

planners view, monitor, and understand the

influences to improve performance. Our next-

generation UI highlights key decision-making

factors in the machine learning models to reveal

what is behind the optimization decisions.

User-centric and KPI-driven exceptions

Strategic control of orders

Explainable AI forecast

AI-driven forecasting systems are able to discern different impacts for every unique item

Sparkling water Still water

Automated forecasting by item, store and day at scaleThe best machine learning models evaluate and learn impacts at a granular level to identify

local and regional trends. They make hundreds of millions of calculations every day.

Products behave differently in individual

locations and at different points of the year.

Treating your products as part of a product

group in a cluster of stores fails to recognize

the local dynamics at work.

For example, when planning stock levels for an

event such as Easter, you should not expect

all variants of a water to sell in equal volume

(sparkling water may have more prestige).

These effects may be more pronounced at

some locations than others, as well as be

inter-connected to other demand influencing

factors such as time of year ... a March Easter

often varies from an April Easter, for example.

The best machine learning models evaluate

and learn impacts at a granular level to identify

local and regional trends. They make hundreds

of millions of calculations every day, allowing

complexity to be automated while improving KPIs.

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To accurately forecast at this level, the model

understands when items sell out during the day

and not just the end-of-day stock position. It also

notes demand transference to substitute items

so that out-of-stock situations can be handled

holistically – it considers not just the out-of-stock

products, but their impact on similar products.

Analyzing these temporary demand shifts prevents

bias in future forecasts and minimizes the chance

that more will be ordered than is needed.

The Blue Yonder model is able to learn and react

quickly and automatically to changes in sales

patterns. By understanding correlations across

multiple hierarchies, it can evaluate whether a

recent change is a pattern or a “one-off” event.

The model is far quicker and more accurate than a

typical human, detecting trends we often cannot

see due to time pressure and scale of data.

What the model has learned from the long term trends

Model learns and adapts quickly to new sales patterns differing from long term behavior

A self-correcting prediction to better match actual demand

Original forecast

Adjusted forecast after exposure to

actual sales

FASTER DECISIONS13,000,000 every day

Fully automated self adjusting forecasts

Utilizing 200+ influencing factors

PROVEN SCALABLE AI99% automated

430,000,000 calculations daily

A forecast that only predicts averages does not

provide the information you need to run your

business. Blue Yonder’s AI forecast considers all

possible outcomes and turns this uncertainty

into a strategy.

It performs daily calculations of the specific

demand factors affecting each product in

each location, rather than assuming standard

probabilities across all items.

Understanding this variability allows for better

replenishment decisions. Blue Yonder calculates

the probability of out-of-stocks along with

expected costs, such as the waste associated

with various scenarios. This helps grocery

retailers optimize their orders in the most

cost-efficient way.

By understanding probabilities, you can equate

the likelihood of demand with service levels,

allowing you to unite your supply chain around

a single demand engine, irrespective of the

prediction horizon.

There are many possible futuresWhy is your forecast a single number?

Each item you stock has unique attributes including its importance to the customer, profit margins,

replenishment costs and shelf-life. How do you evaluate all these factors to find the right balance

between product availability and costs?

Risk Versus Reward

Too much inventory leads to waste and locked

capital. Too little leads to lost sales and

disappointed customers.

A cost-optimized order balances forecasted

risks against expected rewards based on your

strategy for each location and product group.

Each order minimizes risks and costs while

meeting your business objectives. You are

assured of the most cost-effective decision

based on the unique forecast for each product,

each store, each day. Because it is dynamic

and aligned to your strategy, it can be highly

automated.

In addition to minimizing waste, you may wish

to consider the trade-offs among multiple

KPI’s such as working capital, backstock,

replenishment handling, delivery costs and

risk of markdown, all while balancing customer

facing-measures such as product freshness and

presentation of stock. Intelligent algorithms can

optimize and ensure the right level of inventory

to deliver the improvements you seek.

“For our organization, it is critical to have

the right amount of fresh meat available

for the customers in each store. Automated

replenishment based on accurate sales

forecasts plays a key role. Working with

Blue Yonder has resulted in optimizing our

processes significantly.”

Turning uncertainty into strategy

Consider two different stores that have

the same average forecast for the same

day and same product, but different

weather drives different customer

behavior and therefore a different risk

of out-of-stocks or waste.

Learn more

– Ralph Dausch, Executive Board Member of Fresh

Meat Products International, Kaufland

10%

80%

The customer at the heart of your end-to-end supply chainIn a connected customer-driven supply chain, the right availability is

delivered throughout the network by starting with customer demand.

By digitally mapping your supply chain – from suppliers, warehouses, and

distribution centers to physical and virtual stores – inventory requirements

can be optimized based on servicing the customer.

With an accurate probabilistic forecast, inventory

levels can be dynamically adjusted at all

nodes through risk and cost-aware decisions.

By taking account of multiple factors such as

the inventory strategy and demand variability

as well as lead-times and constraints in each

location, inventory decisions are optimized

holistically. Each decision is cognizant of the

impact upstream and downstream, eliminating

the segmented supply chains of old.

Customer-driven supply chains automatically

balance across multiple KPIs to meet your

business to ensure the right level of orders and

inventory throughout the network to deliver for

your customer every day.

Customer Demand

22 8 17 24

Pricing

Supplier

Supplier

Warehouse

The Connected Supply Chain

Warehouse

Warehouse

Online

Stores

Assortment

Blue Yonder delivers real results

INCREASED SALES30% improved forecast accuracy

1-3% revenue growth

IMPROVED AVAILABILITY Up to 30% shelf-gap reduction

10× product availability

REDUCED COST AND WASTEBy reducing waste and unnecessary inventory

95% forecast accuracy levels, 10% reduced waste

REDUCED INVENTORY2–3 days per store,

>2% pick accuracy improvement

4% improved inventory

80% out of stock reduction

IMPROVED ALLOCATION ACCURACY20% allocation accuracy improvement with micro-level precision forecasting

INCREASED MARGINBy reducing waste

BLUE YONDER’S EXPERTISE “Blue Yonder’s forecasting software was the only solution capable of intelligently forecasting the sales per store on a daily basis and the only one to consider external data as additional parameters.” – Managing Director IT, dmLearn more

HEMA ACHIEVES FORECASTING ACCURACYForecast accuracy increased by +/-30%

No manual interventions needed for seasonal/ promo shifts

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MORRISONS USES AI TO TRANSFORM STORE REPLENISHMENT -30% shelf gap reduction (ambient, frozen, fresh)

2–3 days’ reduction in store stock holding

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See how our cloud-based solutions power autonomous supply chains in grocery and other industries, from smart planning to optimized pricing

and digital warehouses to intelligent store operations.

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