rsr's brian kilcourse presents the state of retail demand forecasting 2011

Your GoToWebinar A/endee Viewer is made of 2 parts:

1. Viewer Window 2. Control Panel

Type your quesAon here

#LiveWebinar hashtag.

  Launched in 2007

  Over 20,000 subscribers

  To provide executives with relevant,

insightful content across a variety of

digital medium

Free subscription to our weekly newsletter: www.retailtouchpoints.com/signup

SP

EA

KE

R

FEAT

UR

ED

SP

EA

KE

R

Brian Kilcourse Managing Partner Retail Systems Research

Rafael Gonzalez Caloni EVP Marketing Predictix

Debbie Hauss Editor-in-Chief Retail TouchPoints

MO

DE

RAT

OR

Crystal Ball 2.0: The State of Retail Demand Forecasting

BRIAN KILCOURSE MANAGING PARTNER, RSR RESEARCH MAY, 2011

A LITTLE BIT ABOUT RSR…

Our Mission: To provide the best research in retail built on:!  Expertise gained through real world practitioner experience"  Objective views"  Unique, high value products & services"  Perspective: industry view from consumer to source"  Focus on customer experience"

Because RSR is built entirely of retail veterans, we are the only analyst firm that can truly provide:!

  Genuine insight into business and technology challenges facing the extended retail industry"

  Thought leadership and advice on navigating these challenges for specific companies and the industry at large"

Study Premise: “past results are no predictor of future performance” The statement “past results are no predictor of future performance” is almost a cliché when it comes to both financial performance and retail trends, as proved by the recent economic downturn. As retailers add more optimization capabilities to everything from assortment planning to pricing to space management and replenishment, both the sophistication and the role of demand forecasting present new opportunities for retailers."

RSRʼs first annual benchmark study into retailersʼ demand forecasting capabilities explored how changes in the business cycle and in channels have impacted the discipline. We wanted to identify:"• Whether forecasting accuracy has improved!• Whether the output of a demand forecasting integration with various parts of the retail organization is improving!•  Whether retailers think it is possible to have a single demand forecast for everything and why or why not – and how close they come to their ideal.!

The Growing Importance of Demand Forecasting

9

Grown more

important 85%

Grown less

important 3%

Unchanged 12%

Demand Forecasting's Importance Over the Last 3 Years

Two events have catapulted Demand Forecasting in importance:

#1 The Recession #2 Focus on The Customer

Demand Forecasting Touches Everything

10

But, while there are areas where retailers have grown fairly sophisticated in their demand forecasting abilities, up until now those abilities have

existed as isolated pockets within siloed organizations.

53%

63%

49%

37%

20%

16%

16%

23%

32%

16%

55%

43%

60%

50%

37%

13%

13%

10%

20%

27%

13%

60%

62%

67%

46%

33%

26%

18%

21%

23%

36%

18%

54%

Bus

ines

s/fin

anci

al

plan

ning

Sup

ply

chai

n pl

anni

ng

Mer

chan

dise

fina

ncia

l pl

anni

ng

Ass

ortm

ent p

lann

ing

Spa

ce p

lann

ing

Siz

e pl

anni

ng &

op

timiz

atio

n

Pac

k pl

anni

ng

& o

ptim

izat

ion

Pric

e pl

anni

ng

& o

ptim

izat

ion

Mar

kdow

n pr

icin

g

Cha

nnel

pla

nnin

g

Rep

leni

shm

ent

Where Demand Forecasting is Currently Used

All Winners Others

Very Different Attitudes About What “One Version Of The Truth” Means (But No One Attitude Prevails….)

11

8%

10%

12%

38%

32%

7%

7%

27%

17%

43%

A single demand forecast isn’t as important as a single set of demand assumptions

A single demand forecast is impossible to achieve

A single source for forecasts, or a consolidated forecast, is the best way to get

to "one version of the truth"

A single demand forecast is critical to achieving a “single version of the truth”

Different uses require different forecasts that should then be reconciled across the

enterprise.

Forecast Attitudes Winners Others

Not Surprisingly, Winners Have Improved

12

2%

10%

48%

40%

10%

3%

17%

70%

Grown less accurate

Don't know

Stayed the same

Grown more accurate

Forecast Accuracy Over the Last 3 Years Winners Others

Business Challenges

31%

37%

29%

31%

46%

19%

23%

38%

54%

38%

27%

31%

32%

40%

42%

Seasonal and erratic sales patterns

Consumer behavior has fundamentally shifted and we haven’t figured it out yet

Fragmentation of demand makes it difficult to forecast an accurate aggregate picture

Too many promotions in the marketplace make demand difficult to forecast

Recent economic factors make it exceedingly difficult to forecast demand

Top-3 Business Challenges of Demand Forecasting All Winners Others

The Top Challenge: Recession-Era Promotional Activity To Trigger Demand

14

The Forecasting Challenge Closely Reflects Another Challenge: The After-Effects Of Aggressive Pricing To Trigger Demand

15

16%

7%

6%

28%

11%

38%

46%

10%

14%

32%

32%

38%

40%

48%

58%

Respond to segment blurring

Need to provide more localized pricing

Need to provide consistency in price across channels

Increased promotional intensity of competitors

Need to protect our brand's price image

Increased price transparency - the impact of comparative price shopping

Increased pricing aggressiveness from competitors

Increased price sensitivity of consumers

Top Three (3) Business Challenges Driving Pricing Strategies

2011 2010

N/A

Source: Optimizing Price in a Transparent World, Benchmark Study, RSR Research, April 2011

Aggressive Pricing + Transparency = Increased Price Sensitivity = Difficulty Forecasting Future Demand

16

29%

47%

50%

53%

39%

33%

38%

42%

42%

54%

Assortment sensitivity

New product introductions

Long term forecasts

Promotions

Price sensitivity

Forecast Types That Present A "Major Challenge" Winners Others

47%

29%

32%

41%

45%

21%

31%

31%

31%

38%

42%

42%

Inconsistent or non-existent in-process forecast performance metrics

Poor understanding of customer behavior by channels

A “throw it over the wall” mentality across assortment, price, promotions, space, and

replenishment planning

Un-integrated multiple demand signals in planning and logistics

Information lags or “holes” both on the supply chain side, sales side, or the marketing/

promotions side

Difficulty in capturing cross-channel events that affect customer behavior and channel demand

Operational Challenges ("Major Challenge") Winners Others

Winners Are Most Keenly Aware Of The Omni-Channel Effect

17

Opportunities

21%

30%

25%

30%

28%

21%

45%

12%

47%

44%

46%

16%

26%

33%

36%

42%

46%

53%

59%

60%

65%

68%

Intermittent items

Short lifecycle items

Markdowns

Assortment sensitivity

Seasonal items

Short term forecasts

Price sensitivity

Baseline demand (continuity goods)

Promotions

New product introductions

Long term forecasts

Value vs. Challenge of Forecast Accuracy by Forecast Type Very Valuable Major Challenge

The Best Near-Term Opportunity: Getting Better

19

Directionally, Most Retailers Agree – Except About The Omni-Channel Effect (And What That Might Mean To The S/C Network Design)

19%

37%

16%

42%

13%

55%

53%

67%

63%

68%

52%

25%

33%

40%

48%

52%

52%

62%

71%

76%

81%

81%

Reduce or even eliminate delivery “latency”

Inventory postponement strategies to increase flexibility (for example, “manufacture to order”)

Supply and distribution network redesign

A single demand forecast

Improved cross-channel demand forecasts

Optimize inventory investment to reduce the portion of inventory that is stocked for protection against demand

variability

Improve execution to better respond to changes in demand

Better forecast models to reduce forecast error

An integrated forecasting infrastructure

A single view of demand, inventory, and supply across the supply chain and all selling channels

A forecast suitable for multiple situations (new products, promotions, end of life, etc.)

Opportunities to Overcome Forecast Accuracy Challenges ("A Lot of Value")

Winners Others

Organizational Inhibitors

Top Inhibitors…

For Winners, the top inhibitors have to do with siloed activities that are disconnected to the hyper-competitive realities of today’s retail landscape;

For Others, it’s the system….

24%

17%

14%

28%

34%

21%

38%

34%

34%

41%

31%

15%

20%

20%

20%

25%

25%

30%

30%

30%

50%

55%

We cannot tell how new marketing initiatives in non-store channels such as social media is affecting demand in stores

Restrictions in how we replenish prevent us from taking advantage of demand

Demand management is built around stores; doesn’t work well for other channels

Organizational differences prevent us from working well together to meet demand

Time and investment required to replace our current forecasting system

Getting consensus between departments involved in developing forecasts takes too long

Our systems prevent us from forecasting at a low enough level of granularity

Our processes prevent us from responding quickly to changes in demand

The “80/20” rule: 20% of our forecast challenges take up 80% of our time

Our current solution has difficulties with challenging forecasting problems (such as promotions, new product introductions, short

lifecycle products, intermittent items)

Purchase of supply is disconnected from fulfillment of demand

Top Organizational Inhibitors Winners Others

41%

14%

34%

28%

41%

41%

43%

69%

59%

64%

26%

33%

47%

50%

55%

63%

67%

70%

74%

74%

Process changes to allow greater flexibility in responding to demand

Cross-channel fulfillment processes to make all inventory available in every channel

New or improved KPIs to measure not only forecast accuracy and service levels, but also process measures like number of

forecast adjustments

Technologies that facilitate forecast consensus building between departments

More management-by-exception analysis capabilities

A stronger demand management process, to sync forecasts with sales & ops plans

Technologies that enable more granular demand forecasts

Executive-level support of more coordinated demand management processes

Technologies that produce better forecasts for challenging events (promotions, new product introductions, intermittent

items, short lifecycle items)

Technologies that enable better monitoring of changes in demand or deviations from forecasts

Overcoming Inhibitors ("Very Valuable") Winners Others

But, Retailers Agree: Better Tech IS A Key To Overcoming Inhibitors

26

41%

14%

34%

28%

41%

41%

43%

69%

59%

64%

26%

33%

47%

50%

55%

63%

67%

70%

74%

74%

Process changes to allow greater flexibility in responding to demand

Cross-channel fulfillment processes to make all inventory available in every channel

New or improved KPIs to measure not only forecast accuracy and service levels, but also process measures like number of forecast

adjustments

Technologies that facilitate forecast consensus building between departments

More management-by-exception analysis capabilities

A stronger demand management process, to sync forecasts with sales & ops plans

Technologies that enable more granular demand forecasts

Executive-level support of more coordinated demand management processes

Technologies that produce better forecasts for challenging events (promotions, new product introductions, intermittent items, short

lifecycle items)

Technologies that enable better monitoring of changes in demand or deviations from forecasts

Overcoming Inhibitors ("Very Valuable") Winners Others

But, Retailers Agree: Better Tech IS A Key To Overcoming Inhibitors

27

Let’s Take A Look

4%

4%

4%

10%

6%

6%

6%

10%

31%

46%

38%

38%

29%

58%

35%

42%

48%

50%

52%

58%

60%

62%

65%

65%

66%

69%

76%

79%

Faster Order-to-delivery cycle rates

Improved Replenishment cycle time

Fewer forecast exceptions

Better yielding investment in safety stock

Reductions in inactive stock

Fewer forecast adjustments

More efficient forecasting process (staff productivity)

Lower Inventory Investment

Improved sales per category, sub-category, item

Lower Out of Stock rates

Forecast Accuracy

Lower Inventory Carrying Costs

Increased Turns per category, sub-category, item

Improved margins per category, sub-category, item

Value vs. Use of Forecast KPI's Very Valuable In Use Today

The Use Of KPI’s Lags Their Perceived Value – By a Long Shot!

28

Technology Enablers

28%

29%

32%

36%

32%

43%

47%

32%

32%

22%

25%

36%

35%

21%

62%

41%

45%

48%

52%

24%

43%

41%

59%

39%

72%

45%

30%

35%

35%

30%

65%

65%

55%

50%

20%

20%

20%

50%

50%

20%

32%

35%

45%

50%

50%

53%

55%

55%

55%

58%

70%

74%

In-process forecast performance measures that align with a multi-channel environment.

Common forecast performance metrics across organizations

Forecasting workflows to manage the process

Integrated optimization of size and pack

Modeling process to convert insights into quantitative forecasts

Continuous, time-phased demand forecasting

Bottom-up (or “DRP”) forecasting capabilities

Customer-based demand forecasting

Integrated optimization of assortment and space

Predictive analytics that warn of deviations from forecast

Integrated optimization of space and replenishment

Integrated replenishment, purchasing and forecasting processes

"What-if" scenario modeling

Technology: Value vs. Implemented Winners-Value Winners - Impl. Others - Value Others - Impl.

Opportunities For Retailers

Tier 1 vs. Tier 2: Different Problems To Overcome

70%

10%

0%

40%

30%

40%

40%

30%

22%

26%

30%

30%

35%

35%

35%

61%

Our systems prevent us from forecasting at a low enough level of granularity

Getting consensus between departments involved in developing forecasts takes too long

Organizational differences prevent us from working well together to meet demand

Purchase of supply is disconnected from fulfillment of demand

Time and investment required to replace our current forecasting system

Our processes prevent us from responding quickly to changes in demand

The “80/20” rule: 20% of our forecast challenges take up 80% of our time

Our current solution has difficulties with challenging forecasting problems (such as promotions, new product introductions,

short lifecycle products, intermittent items)

The Top Organizational Inhibitors T1 Mid

33

RSR recommends four steps:

• Examine Forecasting as a Stand-Alone Process • Every Process Requires an Owner • Should Disconnected Forecasting Processes Remain Disconnected? • Don’t Rely on the Technology to Force Process Change

Contents Proprietary & Confidential © 2011 Predictix LLC

What we do

We help Retailers WHOLESALERS

Make be+er

DECISIONS

Pricing/promo ( ) Assortment Planning Forecasting

Replenishment

… on the cloud


Key challenges in forecasting

1  Difficult forecasts = promo3ons, new products, …

2  Omni-‐channel = new demand signals to consider

3  Silos = inconsistent, disconnected forecasts

4  Heavy investments = too costly to replace systems


Cracking difficult forecasts: Design to take advantage of the cloud

The iPad 2 is as fast as a Cray 2 supercomputer from 1985 – and would have s3ll been on the list of top supercomputers in the mid-‐90s

May 9, 2011


Cracking difficult forecasts: Design to take advantage of the cloud

Unlimited computing power on demand =

More powerful science =

30 – 50% better forecasts

The iPad 2 is as fast as a Cray 2 supercomputer from 1985 – and would have s3ll been on the list of top supercomputers in the mid-‐90s

May 9, 2011


Meeting the omni-channel challenge: Be prepared to adapt to what’s next


Meeting the omni-channel challenge: Be prepared to adapt to what’s next

Forecast engines 100% configured

fit for purpose/data fast time to value

high performance


"   Different forecasts for different needs, and

"   One version of the truth, and

"   No rip and replace

Breaking down silos and avoiding heavy investments: Unified forecasting layered on existing systems

Planning Silo Supply Chain Silo Pricing Silo

Unified forecasting


Meeting the key challenges in forecasting

1  Use the cloud to drive beNer forecasts

2  Adapt to and integrate new demand signals

3  Overlay beNer forecasts across silos

4  Extend, don’t replace, exis3ng systems

Your GoToWebinar A/endee Viewer is made of 2 parts:

1. Viewer Window 2. Control Panel

Type your quesAon here

SP

EA

KE

R

FEAT

UR

ED

SP

EA

KE

R

Brian Kilcourse Managing Partner Retail Systems Research

Rafael Gonzalez Caloni EVP Marketing Predictix

Debbie Hauss Editor-in-Chief Retail TouchPoints

MO

DE

RAT

OR

For a free copy of RSR’s May 2011 Benchmark Report:

Crystal Ball 2.0: The State of Retail Demand Forecasting

http://www.rsrresearch.com

You can download this presentation here:

http://rtou.ch/Crystal-Ball

Contact Info:

Brian Kilcourse [email protected]

Rafael Gonzalez Caloni [email protected]