Analytics in Collaborative Planning, Forecasting and Replenishment (CPFR)

Gagan Nema, JDA Software India Private Limited

Bangalore, Karnataka, India

(gagan.nema@jda.com)

Abstract - With the changing consumer needs,

technological advancements and modes of selling, the

manufacturer lags behind the customer in the

fulfillment race. Reaching the epitome of

manufacturing excellence, now the focus need a shift

to shelf which is the contact point for the customer.

The Collaborative Planning, Forecasting and

Replenishment (CPFR) brings manufacturers closer to

consumers where they are able to plan efficiently and

effectively. CPFR is a platform to create synergy from

individual rationality and analytics is a mode to link

this to actual facts. This paper, with the support of

real industry examples, explains how store analytics in

CPFR can facilitate the decision making process to

make a successful Supply Chain (SC).

I. INTRODUCTION

Manufacturer and Retailers do not work in silos any more

but come on a common platform to discuss the health of

supply chain and work together to get the most out of it.

"Many manufacturers have been caught off guard by a

dramatic increase in retail orders or a significant drop off

in order levels without an apparent reason for the order

shift. After due diligence with the retailer, it often

becomes apparent that the root cause is a change in

replenishment policy. For instance, changes in service

levels, safety stock settings, lead times, transportation

modes, and order parameters can drive large swings in

order patterns. Manufacturers need visibility into retail

order strategy parameters to better predict future time-

phased orders coupled with store-level forecast

collaboration to move to a shelf-connected supply chain

mode"[1]

. CPFR aims at creating an environment of trust

between trading partners where the benefits of sharing

information are known [2]

.

CPFR as a typical SCM (Supply Chain Management)

strategy, seeks to reconcile production planning and

associated inventories with customer demand. Demand

management, as such, becomes a key issue. Beside the

inventory reduction, CPFR is also expected to reduce out-

of-stock items, improve asset utilization, and rationalize

deployment of resources. However, its usage is still not

widespread and, where implemented, the results are not

always encouraging [3]

.

II. FRAMEWORK

CPFR was first applied by Wal-Mart in 1995, after which

in 1998 Voluntary Inter-industry Commerce Standards

(VICS) Association launched one of the most

comprehensive set of guidelines in this domain called the

Nine-step Process Model[4]

. In 2004 The original "nine

steps" of CPFR have been refined to a set of eight

Collaboration Tasks (Ref : Fig-1) that are easier to

understand, and yet more comprehensive than the original

model[4]

. These include:

COLLABORATIVE

ACTIVITIES:

COLLABORATION

TASKS:

Strategy and Planning 1. Collaboration Arrangement

2. Joint Business Plan

Demand and Supply

Management

3. Sales Forecasting

4. Order Planning/Forecasting

Execution 5. Order Generation

6. Order Fulfillment

Analysis 7. Exception Management

8. Performance Assessment

Fig-1 CPFR Model - Manufacturer and Retailer Tasks

[5]

Analysis includes Exception Management and

Performance Assessment which act as a driver for

correction in Strategy & planning, Demand & Supply

Management and Execution.

Exception Management involves active monitoring for

pre-defined “out-of-bounds” conditions. Performance

assessment involves calculation of key metrics to evaluate

achievement of business goals, uncover trends, or develop

alternative strategies. Performance assessment is essential

to any understanding of collaboration benefits. The

specific measures can vary from one situation to the next,

but generally fall into two categories[5]:

1. Operational measures: fill rates, service levels,

forecast accuracy, lead times, inventory turns, etc.

2. Financial measures: Costs, item and category

profitability, etc.

Performance assessment of inventory requires KPI

measurement on a regular basis. The leading practice for

inventory planning is to enter key performance indicator

(KPI) values based on historical performance and derive

the inventory values. There are six methods for planning

inventory values[6]

:

1. Forward Weeks of Supply (FWOS)

2. Weeks of Supply

3. Stock to Sales Ratio

4. Sell Through Percent

5. Turn

6. Basic Stock

III. METRICS OVERVIEW

VICS performance measurement metrics in combination

with the above six inventory planning methods give us

comprehensive list of metrics that should be used in

measuring Supply Chain health.

1. Instock and Overstock percentage

2. Weeks of Supply/ FWOS

3. Inventory Turns

4. Stock to Sales Ratio

5. Sell Through Percentage

6. Forecast accuracy

7. Promotional impact

8. Lost Sales % (Service level)

9. Price adjustment/price protection cost

10. Profitability/Return on Investment

CPFR process brings in people from Sales, Marketing,

Production, Finance and Higher Management to

collaborate the Demand Plan and finally execute the

collaborated Supply plan for replenishment and

distribution. Execution team works on the forecast from

the CPFR called the consensus forecast. A behavioral

challenge in CPFR is the Individual Rationality of looking

at the problem. This brings in different perspective of the

same situation and an equal (or more number) of

solutions. A missing bridge to link individual rationality

lead to chaos and rather than choosing the best solution,

the team ends-up with an average solution to please every

member of the CPFR platform.

The 4th Collaborative Activity formulated by VICS called

Analysis is the bridge that links various rationalities to

look at the problem from an overall perspective.

The primary goal of any supply chain management

system should be to get the right product to the right

place, at the right time, at the right price, and at the right

cost [7]

. Thus it becomes more important to avoid

common inventory issues that obstruct in achieving the

above goal. JDA's recent engagement with many

manufacturer's and retailer has brought this theory to

reality.

Recently, a consumer Electronics giant in US saw a

tremendous improvement in metrics like Instock (from

70% to 98%), Weeks of Supply (from 12 to 8) , Forecast

accuracy (from 30% to 80%) and Cash Flow in million

dollars with the use of store analytics in CPFR. Benefits

from analysis are not restricted to execution but sinks

deep in the organization with better information flow to

planner and executives. Finally, the planners of Consumer

Electronics were able to control inventory and

replenishments in retailer's DC, with common targets of

inventory and forecast accuracy thus benefitting both the

manufacturer and the retailer.

A one-stop solution that could generate metrics and

analysis to highlight the above heath issues with an

interface to collaborate retailer with manufacturer will

solve many issues. Transforming analysis in a three step

process of Observation, Root cause and Resolution will

bring metrics and analysis on a dashboard to leverage the

best out of CPFR. This dashboard will act as a SC health

report for decision making while addressing key issues.

IV. METRICS DETAILS

1. Instock and Overstock Percentage

It is a pulse-check for a channel in SC. A view of the

Instock trend (Ref: Fig 4.1.1) will help in understanding

and tuning safety stock, lead time variability and the

distribution methodology.

Store is considered as Instock only when it has a sellable

unit at shelf. Instock targets vary by the desired customer

service level and the industry. For eg in Consumer

Electronics an Instock of 95% in the right stores can still

achieve 99% of the service level.

Fig 4.1.1 : Instock Trend graph example

Store is considered as Overstock when it has selling

inventory units more than a threshold level to be termed

as 'X' days/weeks of supply. An internal analysis on a

leading Consumer Electronics brought us to a conclusion

that anything more than 2.5 times of lead time stock for

an item selling average 1 unit at the stores, does not

affect the Customer Service level at all and thus

considered high. Considering the fact that it is impossible

to run with zero excess percentage it is considered

optimal to run at 15-20% excess.

2. Forward Weeks of Supply

Forward Weeks of Supply (FWOS) is superior for

planning appropriate inventory levels in plans to the week

level. Using FWOS allows a planner to think about their

inventory across time and is essential to effectively

managing inventory levels. The goal of effective

inventory management is to have enough inventory on

hand at any given time to support planned sales until the

next delivery arrives. FWOS is calculated as the number

of weeks of planned sales from the next week forward

that the current inventory value represents. When FWOS

is entered in a plan, it will calculate the ending period

inventory (EOP) by counting the forward number of

weeks of sales and summing the value to calculate the

required ending inventory. When calculated EOP is

known then FWOS can be calculated by reducing the

demand from the inventory by week till it becomes zero.

A comparison of 'entered FWOS' and 'calculated FWOS'

will highlight the potential overstock and under-stock

situations.

Fig 4.2.1 : FWOS Example

Weeks of Supply (WOS) is an inventory measure

calculated by dividing current inventory by average sales.

WOS helps to educate a planner to think of inventory in

terms of time.

3. Inventory Turn:

Inventory Turn, also known as turnover, refers to the

number of times during a period that the average

inventory is sold and replaced. Turn is a ratio of sales to

inventory for a long period of time, usually season or

year. While Turn is the most commonly used Key

Performance Indicator, it is best suited for analysis rather

than planning, since inventory fluctuations across time are

flattened. Turn is typically calculated by dividing sales

by the average inventory value.

Inventory Turn can provide direction in starting the

inventory planning process. Typically Turn Targets are

developed early in the planning process and can be used

to roughly estimate inventory levels by month.

4. Sales to Stock Ratio

Stock to Sales Ratio (SSR) is ideal for planning

appropriate inventory levels in plans to the month level.

Stock to Sales Ratio forecasts how much inventory is

required to achieve the projected sales. SSR represents

proportion of merchandise on hand at the beginning of a

period to the expected sales for that period. SSR is

calculated by dividing stock at the beginning of the period

by sales for the period.

Stock to Sales Ratio is the most logical key performance

measure to plan inventory values in a month level plan.

SSR calculates inventory levels to meet planned sales,

resulting in the potential for overstock situations to be

diminished.

5. Sell Through Percentage

Sell Through Percent (ST) allows a planner to understand

the rate at which inventory is consumed as compared to

sales. While ST is a Key Performance Indicator, it is best

suited for analysis rather planning. Sell Through Percent

represents the ratio of sales to beginning period inventory.

ST is calculated by dividing sales for a time period by

stock at the beginning of the period.

It illustrates the relationship between sales and inventory,

providing guidance to historical results and industry

standards.

6. Forecast Accuracy:

Forecast accuracy is calculated in different forms in

different industries. Commonly used accuracy methods

are Mean Absolute Deviation (MAD), Mean absolute

Percentage Error (MAPE) and Tracking Signal (TS).

When are products are similar in technology, look and

sales pattern then MAD can be used but for the products

in different geographic locations, technology, sales

pattern should use MAPE as it gives a sense of

comparison of forecast performance.

Accuracy targets vary by the type of industry. Eg. For

Consumer Industry a accuracy greater than 60% is

considered best in-class. But based on our analysis of

Consumer Electronics we observed that 70% accuracy is

tough to achieve and is competitive in the in considered

good for forecast taken 4 week ahead.

7. Promotional Impact

1. Lift % : Lift % is the increase in sales over the

base sales.

2. Elasticity: Elasticity is the unit change in

quantity with a unit change in price.

Promotions are the key drivers for sales. Promotions are

run to get the most from the seasonality wave or are run to

get rid of excess inventory.

In both the cases it is very important for the planners to

calculate the right lift so that future plans are moving in

the right directions.

Different types of promotions include

• Buy Downs

• Bundles

• Price moves

• Advertisements

Each promotion has a different lift % due to different

impact on the customer, thus it is important to use the

right lift from the history pertaining to the similar

promotions in the future.

A tool with a capability of maintaining a comprehensive

library of lift% and elasticity should be used along with

statistical forecasting tool to generate the forecast.

8. Lost Sales

Lost sales is the amount of sales lost due to non-

availability of product at the store location. Daily/weekly

POS data comprising of Sales quantity and Inventory

Quantity with high level of accuracy is required to

compute Lost Sales. The amount of Lost Sales would be

equal to the forecast minus the sales for the week when

the inventory is below saleable units (Ref : Table 4.8.1,

Fig 4.8.1). A trend of Lost sales percentage is the best

way of tracking the performance of the Supply Planning

in combination with Demand Planning.

Table 4.8.1: Lost Sales Example

Fig 4.8.1 : Lost Sales Example

9. Price adjustment/ price protection cost.

Price adjustment/price protection cost is paid by the

manufacturer to the retailer whenever the product price is

permanently moved to a lower level by the manufacturer

(Ref : Table 4.9.1, Fig 4.9.1). The price move works best

when the inventory in the channel is least, as it costs

minimal to make such a sale boosting move.

A consistent low Price adjustment/price protection cost

defines a healthy Supply Chain.

Table 4.9.1 : Price Protection Example

Fig 4.9.1 : Price Protection Example

10. Profitability / Return on Investment

Return on Investment is the key driver of supply chain.

ROI is often seen at an organizational level but a check at

a category level will reveal the benefits of execution level

improvements. Eg. ROI comparison on a various

promotion will give us the lead for demand Shaping and

making major decisions on choosing the best.

V. DASHBOARD AND ROOT CAUSE

ANALYSIS

Every analysis discussed here deals with one or more

issues in SC but none of them addresses all at one go.

Thus it becomes necessary to look at the problem as a

whole with a summary dashboard that brings on table a

set of analysis that are relevant for a situation. Dashboard

will enable our three step process of Observation, root

cause and resolution.

1. Root cause analysis for low forecast accuracy

Forecast requires a lot of tuning based on past experiences

and finding the root causes will definitely improve it.

Tuning the forecast to remove the most recurring root

cause will reduce the overall forecast error drastically.

2. Root cause for low or excess stock status

Postmortem of a various inventory issues leads us to the

solutions that can improve future planning. In the cases

when the stores reach a critical stage of excess, a root

cause analysis to gauge the reasons of this situation will

help in turning the situation in next planning cycle. A

Venn diagram (Fig 5.2.1)from a real situation for a

Consumer Electronics after Black Friday event shows that

high replenishment in low selling stores was the major

reason for excess.

Fig: 5.2.1 Root cause for Overstock

3. Stores segmentation/ clustering/ ranking

In widely spread retail industry the number of stores are

large and have dynamically changing sales pattern,

demography, seasonally pattern and social factors. It

becomes highly complex to study the pattern of each store

and plan for it individually. For example, Wal-Mart has

more than 3500 stores in USA. Analyzing the sales at the

aggregated level does not reflect the true picture of the

ground reality and the focus of the higher management

may get shifted to a non-significant issues.

As it is not possible for higher management to look at the

stores individually they would be happy to see the stores

in segment. In realistic scenario, 3 segment or 10 segment

scale is good for understanding. Creating segments

greater than 10 would make it too granular and non-

actionable. Fig 5.3.1 shows the segments for a TV based

on sales in Wal-Mart. It is evident that the focus on

Segment 1 on nearly 400 stores contributing to nearly

50% of sales will give us maximum benefit.

Fig 5.3.1: Store Segment vs Sales Contribution

VI. CONCLUSION

For almost two decades CPFR has been recognized as a

platform to bring consensus among manufacturer's and

retailer's sales, marketing and supply chain teams. With

the discussed analysis and KPIs the teams can focus on

specific issues and the solutions for them. With the clear

facts and direction, the planner and the executives will be

able to give full attention to the store health. The surprises

in low/high inventory situation will be the things of past

as daily/weekly analysis will let the organization know

the performance of the supply chain with key metrics

indicators. Overall, the methodology of observation, root

cause and resolution supported by analysis and KPI is a

step ahead in making SC better. Various analytics tools

are available from companies like Oracle, Microsoft, IBM

and JDA, which can link themselves with the legacy

system and build custom reports.

CPFR can be the catalyst for market leaders to transform

their organizations from traditional, linear supply chain

thinking to collaborative supply networks. And it need not

be limited to market leaders: Supported by sophisticated

business analytics and intelligence, companies of all sizes

can collaborate on a large scale at very low cost and thus

the analytics will be the focus in the coming decade to

transform business.

REFERENCES

1. Fred Baumann , "The Shelf-Connected Supply

Chain", Journal of Business Forecasting, Winter

2010-2011

2. DecisionCraft, "Collaborative Planning, Forecasting

and Replenishment", Online Article Issue No:10/05/1

2010

3. Stank, Daugherty et al., "Collaboration in the supply

chain: A need for a new technology paradigm",

UKAIS 2005, Newcastle, 2005

4. VICS Committee, "Collaborative Planning,

Forecasting and Replenishment (CPFR®)", CPFR

revised Guidelines, 2004]

5. Cecil Bozarth, " CPFR Model: 4. Analysis -

Performance Assessment and Collaboration:

Collaborative Planning, Forecasting", The SCRC

Articles Library, 2011

6. The Parker Avery Group, " Inventory Planning

Methods", Insights: Point of View, 2010

7. Syed Kamal CEO Gillani, Inc, " The 5 R's of Supply

Chain Management", Feature article on

www.solutions-daily.com, 2007]