The Decision Framework 800 words.
pvcgp0607
ppt.pptxDecision model for selecting the new products for H&M
Kunzang Tenzin 2160305
Huiping Yu 2123479
Qinmeng Miao 2155257
Yao Shen 2146218
1
Presentation Outline
Background
Scope and Assumption
Problem statement, benefits and Consequences
Decision framework
Descriptive analytics
Sales forecast
AHP
Conclusion
Background
The H&M group is one of the world’s leading fashion companies. There are many brands that belong to the H&M group: H&M and H&M Home, COS, Monki, Weekday, and Cheap Monday.
The focus of this report is on the brand H&M and H&M home.
There are 4087 stores (includes 25 stores in Australia) and 41 online markets in 66 markets (H&M, 2017).
For the 2018, the company is planning to launch new products
Scope and Assumptions
Focus of our decision problem- Which are the 5 products that should be launched in Australia 2018?
Similarities in the demand structure within the Australian market
Strategic Decision Making
Demand for new products are similar with the existing products
Problem Statement
The decision problem is to select 5 new products out of 20 for the H&M and H&M home for the Australian market
Products selected based on its impact on net profits in the first two stages
A range of criteria is also considered once the products have been filtered using the criteria of net profits
Studies shows that one of the major components of sales are derived from new product launch (Anthony, Benedetto, 1999). PDMA report shows that 49% of the sales are attributed to new product launch (Anthony, Benedetto, 1999). Moreover, it is observed that this share is increasing every year (Anthony, Benedetto, 1999). Evidently, the launch of new products for H&M and H&M home becomes very critical.
5
Benefits of Launching New Products
Studies shows that one of the major components of sales are derived from new product launch (Anthony, Benedetto, 1999).
PDMA report shows that 49% of the sales are attributed to new product launch (Anthony, Benedetto, 1999).
Moreover, it is observed that this share is increasing every year (Anthony, Benedetto, 1999).
Evidently, the launch of new products for H&M and H&M home becomes very critical.
Consequences
It is observed that new products have a very high failure rate. Sivadas and Dwyer (1998), claimed that half of the new products fail every year.
Implications on sales, profits, and competitive strength.
Therefore, choosing the right products for the Australian market is very important for H&M and H&M Home.
Level of decision and owner of decision making
The market department is responsible for selecting the appropriate products for H&M and H&M Home.
Other departments that are involved in the decision-making process are logistics, expansion, IT, accounting, and business development.
Strategic decision concerned with product and market issues
Decision Model
Descriptive
Define the new products
Estimated sale price for the new products
Assign the weights – select 10 products
Sales forecast
How to conduct forecast for the new products
Define the criteria (highest sales)
Make selection (8 products)
Optimization
AHP – select 5 products
Define the criteria
Pivot Table
Regression method
AHP method
9
Define the new products
What are the products of H&M:
Bags, beauty, Dresses, Jackets&Coats,Jeans
Jumpsults, Knitwear, Lingene, Shirts, Shoes
Shorts, Skirts, Socks, swimwear,tops,
nightwear, Trouses.
Categories the products – women men kids
50% Women products – 10 products
25% Men products -5 products
25% Kids Products -5 products
| Products | Category | Descrption |
| 1 | women | Dresses |
| 2 | women | Shirts |
| 3 | women | Shirts |
| 4 | women | Dresses |
| 5 | women | Jeans |
| 6 | women | Skirts |
| 7 | women | Dresses |
| 8 | women | Jackets&Coats |
| 9 | women | Jackets&Coats |
| 10 | women | Dresses |
| 11 | men | Shirts |
| 12 | men | Jackets&Coats |
| 13 | men | Shirts |
| 14 | men | Jackets&Coats |
| 15 | men | Jeans |
| 16 | kids | Shirts |
| 17 | kids | Shirts |
| 18 | kids | Skirts |
| 19 | kids | Jeans |
| 20 | kids | Jeans |
Estimated price
Where the data come from
- From Marketing department
How to estimate price:
- What the market is willing to pay
- pinpointing target customer
- Cost of goods
- Competitor products
| Estimate sale price | ||
| Products | Category | Price range |
| 1 | women | A-B |
| 2 | women | A-B |
| 3 | women | A-B |
| 4 | women | A-B |
| 5 | women | A-B |
| 6 | women | A-B |
| 7 | women | B-C |
| 8 | women | B-C |
| 9 | women | C-D |
| 10 | women | C-D |
| 11 | men | A-B |
| 12 | men | A-B |
| 13 | men | B-C |
| 14 | men | B-C |
| 15 | men | C-D |
| 16 | kids | A-B |
| 17 | kids | A-B |
| 18 | kids | B-C |
| 19 | kids | C-D |
| 20 | kids | C-D |
Assigned weights
Most profitable products : Women
Criteria: profit
| Estimate sale price | ||
| Products | Category | Price range |
| 1 | women | A-B |
| 2 | women | A-B |
| 3 | women | A-B |
| 4 | women | A-B |
| 5 | women | A-B |
| 6 | women | A-B |
| 7 | women | B-C |
| 8 | women | B-C |
| 9 | women | C-D |
| 10 | women | C-D |
Past Profit data
Concept evaluation (literature review)
Concept evaluation
- one of the methods for sales forecast (common use)
Potential customers are presented with descriptions of new products, and asked to express their intentions to purchase the new product.
Compare to existing products to identify similar products.
(Urban, Weinberg & Hauser 1994)
Customer survey
Sales forecast
Steps
Survey and Data
Do customer survey – customer behaviour
Define the similar products
Use the past five years sales data
(From accounting department)
Forecast sales for the new products
Sales forecast
| Product A | ||
| Year (Past five years) | Sales | Forecast |
| 2013 | 242 | 248.80 |
| 2014 | 235 | 231.50 |
| 2015 | 232 | 214.20 |
| 2016 | 178 | 196.90 |
| 2017 | 184 | 179.60 |
| 2018 | 162.30 |
| Product B | ||
| Year (Past five years) | Sales | Forecast |
| 2013 | 230 | 242.80 |
| 2014 | 251 | 236.60 |
| 2015 | 244 | 230.40 |
| 2016 | 205 | 224.20 |
| 2017 | 222 | 218.00 |
| 2018 | 211.80 |
Simple linear regression analysis
The equation that describes how y is related to x and an error term.
Variables
Similar products sale over time of H&M
Outcome
The predicted sales for 2018
Decision model
Applying simple regression model to forecast sale
The equation using past five years sales to predict sales for each new products
Sales forecast
Ranking
Compare the forecast sales for each product for 2018
Select the top 8 sales
| Ranking | Forecast sales (2018) |
| Product G | 251 |
| Product H | 244 |
| Product A | 242 |
| Product B | 235 |
| Product C | 232 |
| Product F | 230 |
| Product J | 222 |
| Product I | 205 |
| Product E | 184 |
| Product D | 178 |
Goal- Best Five products Selection
To choose 5 out of 8 top items
Analytic Hierarchy Process (AHP)
AHP is a technique that was developed by Saaty (1980) to prioritise the alternatives in a decision problem by formulating the problem as a hierarchical structure consisting of a goal, criteria and alternatives.
AHP is a semi-quantitative decision making technique that calculates the relative weights of alternatives by taking input from expert judgment in the form of pairwise comparison between the alternatives with respect to the criteria, and between criteria with respect to the goal.
AHP uses a scale from 1 (equally important) to 9 (extremely more important) to make the pairwise comparison.
Define Criteria
Delivery
Flexibility
Cost
Sales Performance
Customers Satisfactions
Super Decision
Ranking
| Products | Rank |
| Product A | 6 |
| Product B | 2 |
| Product C | 4 |
| Product F | 7 |
| Product G | 3 |
| Product H | 1 |
| Product I | 8 |
| Product J | 5 |
| PRODUCT H | 1 |
| PRODUCT B | 2 |
| PRODUCT G | 3 |
| PRODUCT C | 4 |
| PRODUCT J | 5 |
Conclusion
Descriptive
Predictive
Prescriptive
Three different analytics to solve problem
Pivot Table/ Simple Linear regression Model/ AHP Model
References
Anthony, C., Benedetto, D. (1999). Success factors in new product launch. J PROD INNOV MANAG. Vol. 16, pp. 530–544. Viewed on 7th July 2017 [http://www.dep.ufmg.br/old/disciplinas/epd034/artigo04.pdf]
H&M (2017) About Us. Viewed on 7th July 2017 [http://about.hm.com/en/aboutus/markets-and-expansion/market-overview.html]
Urban, GL., Weinberg, B., Hauser JR. (1994). Premarket Forecasting of Really New Products. The International Center for Research on the Management of Technology. Viewed on 13 July 2017 [https://dspace.mit.edu/bitstream/handle/1721.1/2513/SWP-3689-32616447.pdf;sequence=1]
Wang, X., Chan, H.K., Yee, R.W.Y., Diaz-Rainey, I. 2012, “A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain”, International Journal Production Economics, vol. 135, pp. 595-606.
Gupta, M., Narain, R. 2015, “A fuzzy ANP based approach in the selection of the best E-Business strategy and to assess the impact of E-Procurement on organizational performance”, Information Technology Management, vol. 16, pp. 339–349.
