Supply Chain Management at World Co., Ltd.

World Co., Ltd. From your review of World Co. website , what did you learn about the company? Markets? Where are the stores? Strategic focus?

From your review of World Co. website , what did you learn about the company?

Markets?

Where are the stores?

Strategic focus?

World’s Inventory Performance What explains World’s great inventory performance? 11% 32% of sales Markdowns 6.3 2.55 Inventory Turns 42% 34% Gross Profit Margin World Co. U.S. Department Store Average

SPARCS – What does this stand for? Super, Production, Apparel, Retail, Customer Satisfaction

Super, Production, Apparel, Retail, Customer Satisfaction

Product Timeline for Style No. 15122 2,713 256 -1 39 2,970 329 1,279 272 38 2,020 408 -3 37 1,448 445 1,428 36 1,448 495 9 35 1,943 450 1,395 1,060 34 998 324 522 1,430 33 800 250 32 1,050 244 1,335 31 1,294 92 520 30 1,386 30 5 29 1,411 1,411 28 61 27 26 1,350 25 Stock Sales Receipt Order Week

Some interesting features of World Co merchandising… typical brand “Untitled” Japanese women – approx. 25-29 years old Bottoms frequently offered in only two or three sizes, tops and dresses in only a single size. Japan is a smaller geographic area  much less climate variation. Very fast changing fashion trends. Try to impart a sense that the customer was purchasing a “one of a kind” garment Minimal store inventory – only one unit of a product on the shelf  necessitating more frequent restocking by sales staff.

Japanese women – approx. 25-29 years old

Bottoms frequently offered in only two or three sizes, tops and dresses in only a single size.

Japan is a smaller geographic area  much less climate variation.

Very fast changing fashion trends.

Try to impart a sense that the customer was purchasing a “one of a kind” garment

Minimal store inventory – only one unit of a product on the shelf  necessitating more frequent restocking by sales staff.

Information System Features Realtime information SKU (item/size/color by store) Shipments to the stores Shipments between stores Shipments back to the distribution center Accuracy close to 100% during the selling season Semi-annual sales at a few larger department stores where items returned to the distribution center are marked 50% off.

Realtime information

SKU (item/size/color by store)

Shipments to the stores

Shipments between stores

Shipments back to the distribution center

Accuracy close to 100% during the selling season

Semi-annual sales at a few larger department stores where items returned to the distribution center are marked 50% off.

How does World achieve such quick response times? Note that the typical U.S. department store lead time often exceeds six months, while World achieves a two-week response time.

Note that the typical U.S. department store lead time often exceeds six months, while World achieves a two-week response time.

Manufacturing System Features – “Untitled” Brand Domestic factories (20 vendors)  focus on quick response rather than low cost. Reserves capacity each season without having actual purchase orders or even actual styles finalized Measurement and patterns sent electronically from headquarter to factories. Include specific instructions for the line workers. Fabrics, due to the long lead time, are purchased in advanced. Much of the fabrics are undyed (dying takes approximately one week).

Domestic factories (20 vendors)  focus on quick response rather than low cost.

Reserves capacity each season without having actual purchase orders or even actual styles finalized

Measurement and patterns sent electronically from headquarter to factories. Include specific instructions for the line workers.

Fabrics, due to the long lead time, are purchased in advanced. Much of the fabrics are undyed (dying takes approximately one week).

Store Sales for World “Untitled” Brand $2.2 million given an average floor space of 870 square feet, $2,500 per sq ft. Compared to $155 per sq ft in U.S. specialty stores. Ranges from $750,000 -- $7.5 million.

$2.2 million given an average floor space of 870 square feet, $2,500 per sq ft.

Compared to $155 per sq ft in U.S. specialty stores.

Ranges from $750,000 -- $7.5 million.

Forecasting Aggregate Demand Distribution Side Forecast Store sales plan for category in the sales period Average unit price in the category Number of stores in the chain Example: store sales = $200,000, average unit price = $100, number of stores = 110 ($200,000/store x 110 stores)/ $100/unit = 220,000 units Category Side Forecast Aggregate demand for category (per week) Duration of sales period Example: 45,000 units/week, 4 weeks 45,000/week x 4 weeks = 180,000 units Choose larger of “Distribution” and “Category” forecast Max (220,000, 180,000) = 220,000

Distribution Side Forecast

Store sales plan for category in the sales period

Average unit price in the category

Number of stores in the chain

Example: store sales = $200,000, average unit price = $100, number of stores = 110

($200,000/store x 110 stores)/ $100/unit = 220,000 units

Category Side Forecast

Aggregate demand for category (per week)

Duration of sales period

Example: 45,000 units/week, 4 weeks

45,000/week x 4 weeks = 180,000 units

Deriving SKU Level Forecasts Derived from the “Aggregate” forecasts. Meeting of approx 20 store managers and assistants (all women aged 25-29) twice each for Autumn-Winter (June and August) and Spring-Summer (December and February) collections Room set just like the stores, price tags are affixed to finished samples. Can try on the clothes (just like a customer would). Managers record their thoughts on “ballots”, judge overall rank (1-7), and ranks of fabrics and colors. 4 (noncommittal) are not permitted This allowes the rank of the fabrics/colors as well as the styles, often find better matches of fabrics/colors and styles. Weighted mean and standard deviation of rank derived. Use an ABCD analysis. “A” SKUs are the top 10% and expected to produce 40% of sales, “B” next 20% of SKUs represent 30% of sales, “C” next 30% produce 20% of sales, and “D” 40% of SKUs that produce 10% of sales. So if there are 400 SKUs in the category: (220,000 x 40%)/(10% x 400) = 2,200 units/A-SKU

Derived from the “Aggregate” forecasts.

Meeting of approx 20 store managers and assistants (all women aged 25-29)

twice each for Autumn-Winter (June and August) and Spring-Summer (December and February) collections

Room set just like the stores, price tags are affixed to finished samples.

Can try on the clothes (just like a customer would).

Managers record their thoughts on “ballots”, judge overall rank (1-7), and ranks of fabrics and colors.

4 (noncommittal) are not permitted

This allowes the rank of the fabrics/colors as well as the styles, often find better matches of fabrics/colors and styles.

Weighted mean and standard deviation of rank derived.

Use an ABCD analysis. “A” SKUs are the top 10% and expected to produce 40% of sales, “B” next 20% of SKUs represent 30% of sales, “C” next 30% produce 20% of sales, and “D” 40% of SKUs that produce 10% of sales.

So if there are 400 SKUs in the category:

(220,000 x 40%)/(10% x 400) = 2,200 units/A-SKU

Why does World, in spite of great inventory management and supply chain management, fail to generate good ROA or ROE? (only about 2.5% vs. 40-50% for the GAP and Limited) So many smallish brands, economies of scale are not great…

Can World’s supply chain processes be replicated at other apparel companies? What about non-apparel supply chains? What are some potential barriers?

SCM at World Co. – Key Points Fashion Retailing – factors for success Having the right product, at the right store, at the right time. Need to minimize the need to discount. Maximize sales per square foot. Amazingly responsive process Merchandisers working directly with the factory Very flexible ordering of products Very short order to delivery lead time Notable features of the process Forecasting new product demand Initial product ordering logic Reservation of factory capacity without committing to production of specific product Material ordering – staged for use when and if needed Great product focus 25-29 year old female customers Very homogeneous target group Limited sizes needed Predictable preferences and demand characteristics from year to year

Fashion Retailing – factors for success

Having the right product, at the right store, at the right time.

Need to minimize the need to discount. Maximize sales per square foot.

Amazingly responsive process

Merchandisers working directly with the factory

Very flexible ordering of products

Very short order to delivery lead time

Notable features of the process

Forecasting new product demand

Initial product ordering logic

Reservation of factory capacity without committing to production of specific product

Material ordering – staged for use when and if needed

Great product focus

25-29 year old female customers

Very homogeneous target group

Limited sizes needed

Predictable preferences and demand characteristics from year to year

Time-Series Forecasting Models Moving Average Model Given a number of periods (N) Forecast = Average demand of the past “N” periods Exponential Smoothing Model Given an “Alpha” value (smoothing constant) Forecast = Alpha x Current Demand + (1 – Alpha) x Past Forecast Mean Absolute Deviation (MAD) error measure Average past absolute error Similar to Standard Deviation (Std Dev = 1.25 MAD) Bias Average past actual error

Moving Average Model

Given a number of periods (N)

Forecast = Average demand of the past “N” periods

Exponential Smoothing Model

Given an “Alpha” value (smoothing constant)

Forecast = Alpha x Current Demand + (1 – Alpha) x Past Forecast

Mean Absolute Deviation (MAD) error measure

Average past absolute error

Similar to Standard Deviation (Std Dev = 1.25 MAD)

Bias

Average past actual error