Merchandise Planning

Lessons learnt in real world applications, examples shown only (data made up).

This presentation covers:

• Value in accurate planning

• Real world data challenges

• The approach

• The technology

• The process

Ben Post Analytics Client Success Professional

Version 1, 23rd June 2015

What’s not covered

Affinity analysis and Lift analytics to optimise merchandise assortment, pricing, placement and promotions. 2 min video on that here: https://www.youtube.com/watch?v=XEe7fquFvBI

Smarter Merchandise Planning Benefits

Reduce lost sales

due to stock outs

Maximise Inventory Turnover

Reduce write-downs

Store/department level participation

Less time and hassle spent preparing a forecast

Model with all important effects from social to weather

The Forecasting Challenge

Time series techniques such as ARIMA work well with fast moving items that have clear seasonal and trend patterns BUT most SKUs in large retail are relatively slow moving.

300,000+ SKUs Binned by TRX_Count and Total Sales Revenue

High Volume Items (easier to forecast)

Low Volume Items(harder to forecast)

This means your series are often sparse and noisy

Sparsity = lots of zeros in your series

Window is the time period that you aggregate POS data to e.g. Week

Common time series models like ARIMA or Holt-winters need nicely prepared baselines (>3 cycles of history free of noise).

Time series models propagate error so are best for short term. Merchandise Planning requires short to medium term due to supply chain constraints!

Holt-Winters model types and features

Time series models often miss due to real world factors

Causation: for two events c (cause) and e (effect), c causes e if (1) c and e both occur and (2) if c had not occurred and all else remained the same, then e would not have occurred

Trend + Seasonality + Employee but, sometimes a miss Factors

Can we spot the residuals and, determine causation Trend + Seasonal + State Space

What factors typically affect demand?

1. Is it on the shelf? Shrinkage, smart rolling stock checks

2. Price, Promotion, Placement

3. Staff Coverage

4. Availability and price of substitute items in store

5. Competing retailer/online offers

6. Emerging trends (social media analysis)

7. Seasonal & Events

8. Econometric Factors

9. Weather

The Forecasting Challenge

Techniques like Dynamic Linear models using key factors can outperform common time series techniques for most SKUs. i.e. value in sophisticated forecasting techniques (less error)

Each combination of SKU and location will require a model for accuracy. That translates into a lot of compute for large retailers requiring a scalable solution.

The SPSS platform and an appropriate database such as Netezza or a SPARK cluster delivers the ease of use and speed to deliver results.

Merchandise Planning Challenges and the TM1 Engine

Large dimensions e.g. Product can be in excess off 1M elements. More than a spreadsheet can handle and relational databases don’t aggregate well.

Calculating and analysing sales plans is a multi-dimensional problem. SKU by Time window by Scenario by your measures. Spreadsheets are two dimensional (rows and columns) and relational databases are not modeling tools.

Collaboration with store managers and merchandisers requires enterprise planning capabilities. Spreadsheets are single user desktop tools and relational databases are just databases.

IBM Cognos TM1 is the leading OLAP (cube) based business modeling tool. It is used by many leading retailers for Merchandise Planning and Analysis. It is scalable & fast.

Merchandise Planning Process (method)

Summarise POS• Aggregate by SKU, day of week and

week and store• Variance analysis forecast vs actual –

top 50

Classify Series• Volume/Seasonal• Slow moving• New like existing• Substitutes (rules & adjustments)• New line (rules & adjustments)

Plans & Assumptions• Inventory (receipts & balances)• Events calendar• Promotions• Weather• Economic • Staff

Baseline • Remove the effects of promotions • Impute probable demand for stock

outs (lost sales) or exclude these periods from training set

• Remove anomalies e.g. large one off orders from baseline

• Targeted rolling stock take

Forecast• High turnover items e.g. ARIMA• Low turnover lines e.g. Dynamic

Linear Models

Adjust, Agree and Execute• Store/Department review and adjust• Purchasing review supplier lead times• Purchasing review and adjust• Generate purchase orders and reports

Weather and Events

Include nulls as 0s (sparse)export 7 day fields for each week and factor

IBM TM1 Concert - guided merchandise planning process/tasks with social

https://www.youtube.com/watch?v=KqRBS6p2KwI3min demonstration of product features

Summarise POS

Sample 52 week

TRX Count

Demonstrate Analysis speed with Cognos Analysis for Excel over TM1

Accuracy

TM1 CAFÉ report highlights the largest

misses for investigation as top and bottom 10 Actual – Forecast $

Merchandise Planning Process (method)

Summarise POS• Aggregate by SKU, day of week and

week and store• Variance analysis forecast vs actual –

top 50

Classify Series• Volume/Seasonal• Slow moving• New like existing• Substitutes (rules & adjustments)• New line (rules & adjustments)

Plans & Assumptions• Inventory (receipts & balances)• Events calendar• Promotions• Weather• Economic • Staff

Baseline • Remove the effects of promotions • Impute probable demand for stock

outs (lost sales) or exclude these periods from training set

• Remove anomalies e.g. large one off orders from baseline

• Targeted rolling stock take

Forecast• High turnover items e.g. ARIMA• Low turnover lines e.g. Dynamic

Linear Models

Adjust, Agree and Execute• Store/Department review and adjust• Purchasing review supplier lead times• Purchasing review and adjust• Generate purchase orders and reports

Weather and Events

Include nulls as 0s (sparse)

Classify series for forecasting

TM1 calculates statistics on each series like when it was last sold, # of weeks it sold in the last year and avg trx count

TM1 classifies the series based on these properties (which you can override) and applies a default forecast method/profile (which you can override)

Demonstrate example using spread profiles to enter a forecast (smartco)

Note this example is simplified; there are other attributes which feed into the inventory planning such as shipping costs, minimum order quantities etc. used in the rules governing order creation.

Merchandise Planning Process (method)

Summarise POS• Aggregate by SKU, day of week and

week and store• Variance analysis forecast vs actual –

top 50

Classify Series• Volume/Seasonal• Slow moving• New like existing• Substitutes (rules & adjustments)• New line (rules & adjustments)

Plans & Assumptions• Inventory (receipts & balances)• Events calendar• Promotions• Weather• Economic • Staff

Baseline • Remove the effects of promotions • Impute probable demand for stock

outs (lost sales) or exclude these periods from training set

• Remove anomalies e.g. large one off orders from baseline

• Targeted rolling stock take

Forecast• High turnover items e.g. ARIMA• Low turnover lines e.g. Dynamic

Linear Models

Adjust, Agree and Execute• Store/Department review and adjust• Purchasing review supplier lead times• Purchasing review and adjust• Generate purchase orders and reports

Weather and Events

Include nulls as 0s (sparse)export 7 day fields for each week and factor

Plans & Assumptions in TM1

Staff cover by department, summarised into categories

Inventory plans based on prior forecast

Plans & Assumptions in TM1

Weather forecast This doesn’t have to held in TM1 but it’s a convenient place to store categories by day

Events calendar by DayUse several fields e.g. School holidays this week, School holidays next week. Special Events etc. IBM Social Media Analytics can help spot major events. Watson can read the paper.

Promotions PlanningPromotions being run in the forecast week and weeks prior by department

Economic FactorsIf it add value (accuracy to your models) then you can also compile economic factors

Merchandise Planning Process (method)

Summarise POS• Aggregate by SKU, day of week and

week and store• Variance analysis forecast vs actual –

top 50

Classify Series• Volume/Seasonal• Slow moving• New like existing• Substitutes (rules & adjustments)• New line (rules & adjustments)

Plans & Assumptions• Inventory (receipts & balances)• Events calendar• Promotions• Weather• Economic • Staff

Baseline • Remove the effects of promotions • Impute probable demand for stock

outs (lost sales) or exclude these periods from training set

• Remove anomalies e.g. large one off orders from baseline

• Targeted rolling stock take

Forecast• High turnover items e.g. ARIMA• Low turnover lines e.g. Dynamic

Linear Models

Adjust, Agree and Execute• Store/Department review and adjust• Purchasing review supplier lead times• Purchasing review and adjust• Generate purchase orders and reports

Weather and Events

Include nulls as 0s (sparse)

Merging baseline and other plans and assumptions from TM1 in SPSS

Baseline SKU volume, price, inventory plan, substitutes and other attributes, promotions, weather and event plans and assumptions are pulled from TM1, transformed and used for model training, evaluation and forecasting in SPSS

Demonstrate SPSS Modeler data preparation and modeling

Stock showing in inventory but no sales?

Use analytics to identify categories most at risk of being out of stock

Implement a targeted rolling stock take

Use data mining to identify shrinkage specific factors e.g. store, employee, shift, department, line, location, time, events, school holidays..

Merchandise Planning Process (method)

Summarise POS• Aggregate by SKU, day of week and

week and store• Variance analysis forecast vs actual –

top 50

Classify Series• Volume/Seasonal• Slow moving• New like existing• Substitutes (rules & adjustments)• New line (rules & adjustments)

Plans & Assumptions• Inventory (receipts & balances)• Events calendar• Promotions• Weather• Economic • Staff

Baseline • Remove the effects of promotions • Impute probable demand for stock

outs (lost sales) or exclude these periods from training set

• Remove anomalies e.g. large one off orders from baseline

Forecast• High turnover items e.g. ARIMA• Low turnover lines e.g. Dynamic

Linear Models

Adjust, Agree and Execute• Store/Department review and adjust• Purchasing review supplier lead times• Purchasing review and adjust• Generate purchase orders and reports

Weather and Events

Include nulls as 0s (sparse)export 7 day fields for each week and factor

Forecast Generation, Behind the scenes

The SPSS platform:

Automatically re-trains a model, evaluates accuracy and pushes a forecast for each SKU/Store (on a schedule) to TM1

In-database scale (SPARK, Netezza, DB2, SQL etc.) read/write from TM1

Automation with Python, R integration for the more exotic models

Powerful and easy to use data-mining GUI

Demonstrate SPSS Modeler merging data from TM1 and pushing forecast back to TM1

Accuracy, feature and model selection

The forecast modeling process involves:

Transforming data for the best results

Selecting fields/features that matter

Selecting a model produces the least error (ARIMA, GLM, Dynamic Linear, Dynamic Baysian etc.)

Example test data set Forecast SKU unit sales vs Actual.Factors used; weather, holidays, price but missing everything else. The tighter the dots around the line, the better the model

Dynamic Bayesian Model: Projects the future

Agree and close (execute purchase orders)

Forecasts are now ready for review in TM1

TM1 inventory planning model uses rules to minimise stock outs and minimise probably stock outs using:

Lead time

Carrying costs

Minimum order quantities

Forecast demand

Purchase orders can be exported as a file to execute in your ERP

Adjust, Agree and Execute• Store/Department review and adjust• Purchasing review supplier lead times• Purchasing review and adjust• Generate purchase orders and reports

Demonstrate Store/department review/sign off

Demonstrate TM1 merchandise planning solution

Thank You! Important links:

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