The Case for anAI-Enabled Retail Supply ChainKey Findings from RSR Benchmark Report June 2020Commissioned by LLamasoft, Inc.

Research OverviewTariffs. Virus outbreaks. On top of it all? A customer who has not only grown more insistent that their every demand be met, but who is increasingly intolerant the moment it is not. Today’s supply chain is no longer a challenge – it’s a high-stakes, high-wire balancing act, with change constantly being introduced from previously unseen angles.

How is a retailer expected to navigate this ‘never normal’ business environment? And, is the promise of a supply chain made smarter by artificial intelligence the answer?

LLamasoft commissioned RSR Research to explore these questions through a global survey of senior retail executives. Read on for key findings and takeaways.

Download the full report here.

Survey DemographicsRSR conducted an online survey from February – March 2020 and received answers from 82 qualified retail respondents. Respondent demographics are as follows:

2% 2%

9%

35%37%

15%

Less than $50 Million $51 - $249 Million$250 - $499 Million $500 - $999 Million$1 - $5 Billion Over $5 Billion

2019 Revenue (USD Equivalent) Retail Presence

73%USA

28%Canada

26%Latin America

45%UK

32%Europe

10%Middle Eastand Africa

16%Asia/Pacific

Assuming industry average comparable store/channel sales growth of 4.5%, RSR defines those with sales above this hurdle as “Winners,” those at this sales growth rate as “average,” and those below this sales growth rate as “laggards” or “also-rans.”

What Is a Retail Winner?

7

38

55

Worse than average

Average

Better than average (Winners)

Year-Over-Year Sales Growth Rates (assume average growth of 4.5%)

Monitoring and Modeling – Then ActingThe survey asked retailers to rank their reaction to a number of statements relating to their current capabilities. Not surprisingly, Retail Winners feel much more strongly than average and underperforming retailers that they already have the ability to monitor supply chain capacity, to model contingency plans, and to simulate the effect of supply chain changes before implementing them.

These findings underline an important difference between Winners and their lesser-performing peers. Non-winners approach winning behaviors in only one way; they are willing to do “whatever it takes” to work around bottlenecks when they see them. While that’s admirable, we get a picture of non-winners bouncing from pillar to post as supply chain disruptions occur. Winners on the other hand don’t want to be victimized by events beyond their direct control – they want advance warning, and they want to understand their options.

Rate Your Reaction to the Following Statements ('Strongly Agree')

Winners Others

27%

43%

28%

33%

31%

35%

48%

51%

53%

56%

56%

73%

We know how to model the impact of business mergers on the supply chain

We can currently understand the impact of bottlenecks and prioritize which ones need to be addressed most

We can currently simulate the effect of changes to the supply chain before implementation

We are able to determine the optimal number of facilities and locations considering mid- to long-term demand projections

We have the ability to model contingency plans for severe supply chain interruptions

We currently monitor capacity and have the ability to add more when market conditions demand it

Key TakeawayThe desire to be informed and make more timely decisions is what underpins retailers’ interest in using AI analytics to enable a more agile supply chain.

Lack of Agility Equals Higher CostsRSR asked retailers to identify the top three business challenges an AI-enabled supply chain would help them to address. While geopolitical tensions and changes in consumer demand are the most worrisome disruptors, the general theme is an inability to respond quickly and flexibly to changes in the environment.

Over-performing retailers indicate a greater awareness of the impact of two big disruptors that pre-date the coronavirus: greater demands by consumers for direct delivery, and the imposition of tariffs among trading nations. These two disruptive elements are closely related, at least from a supply chain perspective; for example, a retailer may choose to ship direct-to-consumer from a free trade zone close to the point of manufacture, bypassing its traditional supply chain completely.

What Are the Top Three Challenges That an AI-Enabled Supply Chain Might Help Your Business to Address?

Top 3 – Winners Top 3 – Others

#1We must create more

flexible sourcing strategies due to

geopolitical issues

#2Consumer demand

changes rapidly, undercutting our

ability to buy big and lower costs

#3We need to offset the

higher cost of omnichannel

fulfillment with smarter supply chain

strategies

#1Consumer demand

changes rapidly, undercutting our

ability to buy big and lower costs

#2We need to be ableto monitor supply chain and make adjustments innear real time

#3Customer demand for

more omnichannel fulfillment options is

creating inefficiencies that eat into our

profitability

Key TakeawayThere are simply too many variables that affect both demand and supply for decision makers to handle manually – more automated responses to market conditions are increasingly viewed as necessary to help retailers perform better in a complex and volatile marketplace.

An Inefficient Ground GameWhen asked to rate the operational challenges in their company’s transportation operations, it was clear that respondents see plenty; in fact, every issue they were asked to consider was identified as being at least ‘somewhat of a problem’ by the majority.

Interestingly, more Retail Winners cite having a “big problem” in key aspects of transportation management than average performers and underperformers do, likely because they are more aware of the inefficiencies associated with managing the consumer side of the supply chain.

Rate the Following Operational Challenges in Your Company’s Transportation Operations

Big Problem Somewhat of a Problem Not Much of a Problem Not a Problem

Our transportation network is inefficient; too many empty miles

We have difficulty identifying the best source for regional distribution points

Omnichannel supply chain requirements are resulting in more frequent deliveries to fulfillment locations

Our current private fleet size is not aligned with the volumes and frequency of shipments and optimized routing

We use third-party carriers, and often experience capacity problems

Consumers and regulatory agencies are demanding a greater focus on carbon emissions

26%

35%

38%

1%

Key TakeawaySupply chain issues that could be mitigated by an infusion of AI enablement aren‘t merely limited to long international links between suppliers and retailers. Empty miles, more frequent shipments and deliveries, capacity issues, and reliance on third party carriers – all of these challenges beg for better real-time monitoring and optimization.

24%

30%

41%

4%

24%

38%

35%

2%

17%

40%

37%

6% 13%

50%

37%

10%

50%

37%

4%

Differences Between Winners and Others Can Be StarkWhen it comes to the opportunities relating to their challenges, the difference between Retail Winners and all others is pronounced.

Overperformers tend to want visibility all the way from the point of manufacture through to the point of customer order fulfillment, while underperformers tend to view their supply chain only from the moment when they take possession of the inventory. That’s an important distinction; as the saying goes, “If you can’t see it, you can’t manage it.”

To What Extent Will an AI-Enabled Supply Chain Help Your Company to Address the Following?

27%

38%

30%

32%

27%

27%

30%

30%

32%

19%

42%

47%

47%

49%

49%

51%

53%

56%

58%

58%

Winners Others

Key TakeawayWinners aren’t satisfied with merely coping; they want to manage – and they want to operationalize the insights that can be had fromAI-enabled analyses to help them achieve that objective.

Meet service-level requirements

Demand forecasting

Response to disruptions

Alerts on unexpected supply shortages that could impact a supplier’s ability to meet performance targets

Visibility to uncover supply chain inefficiencies

Identify inefficient suppliers

Identify unprofitable customers

Alerts on critical inventory situations anywhere in the supply chain

Visibility into available-to-sell inventory anywhere within the enterprise

Identify network bottlenecks

Consensus – and Then NoneThe survey asked retailers to identify the internal roadblocks that are preventing them from achieving a more effective supply chain. While there was little consensus among Winners and others, they were in forceful agreement over one thing – that a focus on efficiency at the expense of flexibility was holding them back.

Retailers at different performance levels find little common ground. For Winners, the two most frequently cited issues have become the lack of top-down leadership required to bring the change required to transform a channel-based supply into one prepared for a “channel-less” world, as well as the inability to split their freight costs from first cost. For average performers and underperformers, the hit list is much more diverse and provides a glimpse into how different their current reality is.

Quite simply, no one is without real problems here.

Top Three Inhibitors That Prevent You From a More Effective Supply Chain

Top 3 -- Winners Top 3 -- Others

#1Our supply chain metrics are too

focused on efficiency at the expense of

flexibility

#2Lack of executive

top-level support for supply chain

transformation in support of an

omnichannel strategy

#3We lack visibility at

key points in the supply chain /

We don't split out freight costs

#1Our supply chain metrics are too

focused on efficiency at the expense of

flexibility

#2Lack of

coordinationamong supply

chain, merchandising, and marketing /

Our legacysystems aretoo fractured

#3We lack visibility at

key points in the supply chain

Key TakeawayRetailers’ supply chains have long-sacrificed flexibility for efficiency. While this may have worked in the past, it does not work right now. They must endeavor to introduce nimbleness into how they source products, where they source them from, and what the entire delivery process looks like –and quickly. Those who are clever will be quick to abandon past practices in order to meet customer demand in the “never normal” era.

AI and ML Make Their Presence FeltWhen asked about the potential of artificial intelligence and machine learning to deliver value in their organizations, it is clear that retailers really like what they are hearing. While demand forecasting took top honors, retailers think AI and ML have the ability to improve virtually every process RSR put before them – from capacity planning to customer segmentation to last mile delivery optimization.

While retailers’ enthusiasm for using AI and ML technologies to improve their demand forecasting models made perfect sense in a pre-COVID environment, forecasting in general has morphed very quickly from a topic that was already trending, to one of critical importance. The assistance of AI and ML will be most welcome here, as the forecast must be generated from far “fuzzier” numbers than in the past.

Percentage Citing 'High Value’ for AI and ML Technologies to Enable the Following:

35%46%46%48%49%51%51%52%52%54%55%55%

59%73%

44%45%

43%46%

40%38%40%40%39%39%35%

40%39%

24%

21%9%

11%6%

11%11%

9%7%9%7%

10%5%

2%2%

Autonomous vehiclesCurrent demand vs. forecast analysis & rebalancing

Network cost modelingEnd-to-end product flow-path optimization (Product or SKU level)

Performance simulationContingency planning (risk mitigation)

Customer segmentation and analysisAutomated forecasting processes

Last mile delivery optimizationPredictive analytics for supplier selection

Real-time insights embedded into operational processesData cleansing and data robustness

Capacity planningDemand forecast modeling using macro indicators (geo-market, weather, competitive, social, etc.)

Some ValueHigh Value Little/No Value

Key TakeawayRetailers are eager to find out just how much (and how quickly) all of the promise around artificial intelligence and machine learning will translate into real-world improvements to their supply chain efforts.

Winners Are Even More Enthusiastic

Performance View of 'High Value’ for AI and ML Technologies to Enable the Following:

41%

35%

32%

46%

49%

35%

65%

53%

56%

58%

60%

67%

67%

80%

End-to-end product flow-path optimization (Product or SKU level)

Network cost modeling

Current demand vs. forecast analysis and rebalancing

Predictive analytics for supplier selection

Capacity planning

Last mile delivery optimization

Demand forecast modeling using macro indicators (geo-market, weather, competitive, social, etc.)

Winners All Others

Though this represents sentiment – not yet action – it clues us in to how differently Winners are already positioning themselves to leverage AI and ML technologies in aggressive ways.

Winners place substantially more importance on the ability of next gen technologies to improve their existing operations, with more than half identifying high value potential in every use case.

Winners Are Even More Enthusiastic (Cont’d.)

‘Using & Satisfied’

22%

24%

35%

27%

35%

19%

24%

22%

43%

19%

22%

30%

43%

38%

33%

40%

40%

42%

42%

42%

42%

47%

47%

49%

51%

56%

58%

62%

Autonomous vehicles

Network cost modeling

Real-time insights embedded into operational processes

Contingency planning (risk mitigation)

Capacity planning

Last mile delivery optimization

Data cleansing and data robustness

Current demand vs. forecast analysis & rebalancing

Predictive analytics for supplier selection

Automated forecasting processes

Performance simulation

Demand forecast modeling using macro indicators (geo-market, weather, competitive, social, etc.)

End-to-end product flow-path optimization (product or SKU level)

Customer segmentation & analysis

Winners All Others

And, when it comes to action, Winners are also streets ahead, deploying these technologies at a surprisingly high rate compared to average and underperformers – in some cases at a ratio of 2:1.

Key TakeawayWinners have a tremendous head start when it comes to leveraging artificial intelligence and machine learning. If retailers who are just getting by hope to catch up, the time to move is absolutely now. Those who invest in making their business run smarter in these times will stand a much better chance of not only surviving – but in making up some ground.

Next-generation analytics enabled by artificial intelligence and machine learning create their own set of opportunities, which are in turn the result of the vast new types of data that are available to businesses today.

AI Is Emerging as a Must-Have CapabilityEven if we discount how many attributes are associated with stores, SKUs, customers, or channels, it remains an inescapable fact that business decision makers are simply drowning in data. With the explosion of attributes associated with products, locations, and consumers, retailers are challenged to understand relationships among those factors that could be important. Adding in data from third-party sources, such as supply chain partners, further magnifies the problem.

Key TakeawayThere are genuinely powerful use cases of AI and ML being used in retail right now, changing the way retailers make critical decisions. AI is no longer a concept car – it’s real, and it’s available to all willing to sift through the hype to discover its most powerful ‘right now’ applications.

# Stores # SKUs # Customers # Channels

Factors

New World

Old World

2,000

2,000

Attributes

Attributes

50

5

187,500,000,000,000,000,000,000

100,000,000,000

100,000

100,000

50

5

25,000,000

1

50

1

6

2

50

10

# of Combinations

vs

About RSR ResearchRetail Systems Research ("RSR") is the only research company run by retailers for the retail industry. RSR provides insight into business and technology challenges facing the extended retail industry, and thought leadership and advice on navigating these challenges for specific companies and the industry at large.

Objective Insights into the business challenges and opportunities that retailers are addressing in today’s marketplace, and how Winners win

Pragmatic Advice to both retailers and solution providers

Extensive Retail Industry Experience

A Deep Bed Of Research into retailers' technology investment plans and the business opportunities and challenges that drive those investments

To learn more about RSR, visit www.rsrresearch.com.

About LLamasoft, Inc.Delivering the science behind supply chain’s biggest decisions

Over 750 of the world’s most innovative companies rely on LLamasoft to design operational strategies to achieve profitability and growth goals. Powered by AI and advanced analytics, LLamasoft’s decision platform enables business leaders to solve problems in new ways and make smarter decisions faster as their business and operating models change. With a true digital twin of the extended supply chain, LLamasoft deploys decision solutions through enterprise-ready applications and an extensible no-code App Studio that enables LLamasoft or its customers to rapidly build their own business applications. Its customers have identified more than $15.5B in value leveraging insights from LLamasoft’s solutions. And to reach its goal to positively impact 100 million lives by 2022, LLamasoft partners with humanitarian organizations, government entities, and the World Economic Forum to design and optimize health supply chains.

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