Business Situation : US-based manufacturer of personalized gift items; with presence across North America, Australia, Europe and Middle East. They have over 3,000 sku’s on offer; across 8 product platforms and 13 countries. SKU’s are supplied from Far East suppliers, with a 3-month lead time. And almost 60% of Customers purchase gift items for immediate consumption; with the remaining having a deferred shipment date through out the year. In addition, the manufacturer runs SKU-level promotions through-out the year, which result in SKU-specific demand. Critical to have accurate sku-level demand forecast so that all orders are met; while maintaining optimal inventories.

The Task : - Develop a framework and relevant forecasting models for improving the forecast process and accuracy.

- Obtain a robust and accurate SKU-level forecasting for each week over a year.

- Implement the predictive models so that forecasting is improved, inventory levels are optimal and customer satisfaction is improved.

Analytical Framework : The solution was aimed at simplifying the process and improving the timeliness and accuracy of demand forecast:

1. Simplify and automate some of the current processes that were cumbersome and susceptible to human error.

2. Use statistical analysis to learn from historical trends, project future demand, and create an Early Warning System to predict weekly excess and stock-outs at SKU level.

3. Improved the existing process of predicting repeat business using cannibalization models and also provided shipment profiles with insights on patterns of how products shipped out to customers. This helped in placing timely and appropriate Purchase Orders with suppliers.

4. Provided dashboards for measuring forecast accuracy and also performance of shipments.

5. Adhoc analytics to support current forecasting, customer care and marketing decisions e.g. quantifying financial impact of late shipments

The Result : • Better sku-level forecasts and ability to react faster to products becoming hits.

• Less obsolete inventory at the end of the year meant freeing up working capital and reducing waste. Lower stock-out rates also meant better customer satisfaction in addition to revenue. Since repeat customers are their main focus, this factor is critical in preventing unnecessary attrition.

• More scientific approach to forecasting, thereby eliminating any bias in subjective forecasting logic.

Analytics in Action Improve Demand Forecasts. Sales up by $ 3MM, stock-outs down

Client : A US-based Manufacturer of Customized Gifting Products

Forecast Variance

Sale

s gr

ow

th (P

Y)

Over- forecasted Growing

Under-forecasted Growing

Under-forecasted Declining

Over-forecasted Declining

WC57001A, +, +

TD72601B, -, -

WC87901A, -, -

WC74846A, +, +

WC57001B, -, -

WC59401A, +, +

WC69501A, +, +

WC62545A, -, -

WC93001A, +, +

WA38001A, -, -

WC62545B, -, -

WC58802A, +, +

WC59004A, -, -

WC74846B, +, +

WC30146A, -, -

WD25646A, +, +

WC74903B, +, +

WD06001A, -, -

WC74903A, +, +

WC59401B, -, -

WC59004B, -, -

WC83504A, +, +

WD31503B, -, -

WC80446A, -, -

WC28801A, +, +

WC81001A, -, -

WC83945A, +, +

WC28301A, -, -

WC87901B, +, +

WA85401A, -, -

WC58802B, +, +

WC88102A, -, -

WD11202A, -, -

WC89803A, +, +

WC83945B, -, -

WD01993A, +, +

WC30146B, -, -

WD18006A, +, +

-5000

0

5000

10000

15000

20000

25000

30000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43

MAPE - 23.47%

2UCL

3UCL

2LCL

3LCL

Variance

235386

359051

198157211658.2184196496.3737

0

50000

100000

150000

200000

250000

300000

350000

400000

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152

Plan C

FY_2009

YTD_2010

Regression 1

Regression 2

forecast

YOUR PARTNER FOR

DATA ANALYTICS SERVICES

MANAGEMENT TEAM GLOBAL EXPERIENCE.

PROVEN RESULTS.

Roy K. Cherian CEO Roy has over 20 years of rich experience in marketing, advertising and media in organizations like Nestle India, United Breweries, FCB and Feedback Ventures. He holds an MBA from IIM Ahmedabad.

Anunay Gupta, PhD COO & Head of Analytics Anunay has over 15 years of experience, with a significant portion focused on Analytics in Consumer Finance. In his last assignment at Citigroup, he was responsible for all Decision Management functions for the US Cards portfolio of Citigroup, covering approx $150B in assets. Anunay holds an MBA in Finance from NYU Stern School of Business.

Buck Chintamani EVP, Strategic Initiatives & Business Development Buck has extensive experience working with global clients across sectors. He was an early employee at Infosys, a founding team member at supply-chain software startup - Yantra, and part of the management team at RFID sector startup - Reva. Most recently, he was the Vice-President for Service Partner Strategy and Programs at product lifecycle management software company, PTC. Buck has an MBA from IIM Ahmedabad.

Kakul Paul Business Head, CPG Kakul has over 6 years of experience within the CPG industry. She was previously part of the Analytics practice as WNS, leading analytic initiatives for top Fortune 50 clients globally. She has extensive experience in what drives Consumer purchase behavior, market mix modeling, pricing & promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.

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1.212.837.7827 (o) 1.208.439.5551 (fax) info@marketelligent.com

CONTACT www.marketelligent.com

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