Deathlok Outdoor Corporation produces five shoe brands and needs to forecast demand to plan inventory and expand internationally. The document discusses traditional forecasting methods like qualitative techniques, time series analysis, and causal models. It also covers inventory management topics like economic order quantity, order points, leveling demand, and supply chain challenges of international expansion. Future research areas include testing new products and evaluating push-pull supply chain strategies in different markets.

1. DEATHLOK OUTDOOR
COOPERATION
ISE 311 – Enterprise Systems
Dr. Christopher M. Greene
Team 2
Laura Capobianco
Ludovico Cestarollo
Alexander Drake

2. Agenda
• Introduction
• Purpose
• Assumptions
• Traditional Forecasting Methods
• Making a Forecast
• Inventory Control
• Forecasting Challenges
• Leveling Aggregate Demand
• International Expansion
• Future Research
2

3. Introduction
• Deathlok Outdoor Corporation
• Five brands: UGG, Teva, Deathlok, Simple Shoes, Tsubo
• Three types of shoes: carry-over, similar models, new
designs
• Need to forecast for each shoe, especially new designs
• Need to control inventory
• Plan to expand internationally
3

4. Purpose
• Assess and provide solutions to Deathlok’s inquiries to
expand their company internationally and create cross-
seasonal brands through:
• Forecasting
• Inventory Control
• Leveling Aggregate Demand
• Supply Chain and Operations Management
4

5. Assumptions
• Deathlok is in good financial standing
• Deathlok properly applies EOQ and Order Point
Techniques
• Deathlok’s capacity is greater than the production rate
• Not every retail customer wants the orders at the same
time; therefore as stated from Deathlok there is a 1-2
month period where orders are waiting to be shipped to
retailers
• All suggestions are for the fiscal year 2008
• Fiscal Year is Mar. Year X to Feb. Year X+1
• All brands forecast according to the four standard
seasons
5

6. Assumptions
• There are 2 style seasons per year (4 quarters)
• Spring Season 2008: Mar. 2008 – Aug. 2008
• Fall Season 2008: Sept. 2008 – Feb. 2009
• All brands have one seasonal catalog per year (no year
round catalogs)
• All forecasts and orders are available sufficiently in
advance for the product to be manufactured and returned
to distribution centers 2-3 months before the retailer’s
expected needs
• Each brand sales team forecasts on a weekly basis
• Existing and previous patterns will continue into the future
6

7. Supply Chain
• Flow Chart
7

8. Traditional Business Forecasting
• Prediction of future events for planning purposes
• The methods of typical business forecasting are based on
1. Judgment
2. Available Data
3. Historical Data
• Three techniques to make forecasts:
1. Qualitative techniques
2. Time series analysis and projection
3. Casual models
8

9. Qualitative Techniques
• Delphi Method
• Executive Opinion
• Historical Analogy
• Visionary Forecast
• Diffusion Modeling
• Market Research
• Panel Consensus
9

10. Time Series Analysis and Projections
• Drift Method
• Exponential Smoothing
• X-11
• Simple Moving Average
• Weighted Moving Average
• Kalman Filtering
• Autoregressive Moving Average (ARMA)
• Extrapolation
• Linear Prediction
• Trend Estimation
• Growth Curve
10

11. Causal Models
• Average Approach/Point Method
• Naïve Approach
• Seasonal Naïve Approach
• Statistical Survey
• Machine Learning/Pattern Recognition
• Econometric Model
11

12. Essential Steps in Making a Forecast
1. Define the purpose
2. Prepare the data
3. Select the techniques
4. Make the forecasts (and estimate error)
5. Track the forecast
27

13. Maintain Accurate Inventory Records
• Manage the updating carefully
• Track actual versus forecasted sales
• Find and fix the bad forecasts
• Forecast only what you must; calculate whatever
you can
• Agree in advance when forecasts will be revised
13

14. Inventory vs. Order
• Economic Lot Size = Size of lots in which material
is produced
• Balance between two costs:
1. Cost of placing an order
2. Cost of keeping inventory on location
• Result: total cost is minimized
• EOQ (Economic Order Quantity)
14

15. EOQ – Economic Order Quantity
• EOQ responds to
the question “How
much to order?”
• EOQ has flat
curve
• Order quantity can
be a bit off
Plossl, George W. – Production and Inventory Control, 2nd Edition,
Prentice Hall, 1985
15

16. When to Order?
• Correctly answering this question is more
important
• Balance: Inventory-Investment Costs vs
Desired Customer Service Level or Costs
from Resulting Shortages
• Use Order Point Method
16

17. Order Point Method
• Replenishment (inventory) order is placed when
inventory at the order point
• Characteristics:
• Fixed order-quantity – variable-cycle time
• Order-quantity = EOQ
• Very good for “Independent Demand”
17

18. Order Point
• The initial level
of inventory A is
given by:
• forecasts
• calculated
demand
• A reserve (or
safety) stock is
needed
Plossl, George W. – Production and Inventory Control, 2nd Edition, Prentice Hall, 1985
18

19. Future Inventory
• Forecasting customer demand predicts future
level of inventory
• Order Point Method
• All statistical techniques are based on the
assumption that existing patterns will continue
into the future
• There are uncertainties
• The demand will not necessarily follow the
forecasts
• A reserve stock is necessary
20

20. Forecasting Challenges at Deathlok
• Four demographics: women, men, girls, and boys
• Various sizes per shoe style
• Various colors per shoe style
• New styles have no historical data
21

21. Poor Forecasting Costs
Hard Costs Soft Costs
Inventory Supplier Relationships
Expedites Administrative Costs
Excess Materials Customer Loyalty
22

22. Consequences of Over-Forecasting
• Increased inventory (working capital)
• Reduced profits
• Cut down on administration costs
• Excess materials
• E.g. Specifications change before the materials are
finished
23

23. Consequences of Under-Forecasting
• High expediting costs
• Lost revenue while waiting for materials
• Weakened supplier relationships
• Cut down on administration costs
• Weakened customer loyalty
24

24. Methods to Forecast New Product
Method Description
Delphi Method
Propose questions to a
panel of experts
Executive Opinion
Uses manager’s
technical experience and
customer opinions
Historical Analogy
Compares product to
similar ones in the
market
Visionary Forecast
Not scientific, uses
insight and judgment
Diffusion Modeling
Predicts how many
customers will adopt the
product and when
25

25. Leveling Demand
• Leveling is the process by which production is “smoothed”
over a period of time
• What does “smoothing” production look like?
26

26. Benefits of Leveling Production
1. No spikes in labor costs
2. Keeps production capacity from being
overbooked
3. Improved quality control
27

27. The Heijunka Box
• A production value box used to designate which products
are shipped, packaged, handled, etc. in which time period
28
www.lean.org

28. Changeover Time Reduction
• By leveling the demand over the entire fiscal year,
changeover time is drastically reduced
29

29. Deathlok’s Approach to Leveling
• They will maintain their seasonal products
• Focus on the 365 day portfolio
• New footwear lines are created for other seasons
• Results:
• Higher effectiveness and Total Quality
• Increased profits
• More responsive
• Lean production
• Increased forecasting accuracy
30

30. New Product in a Defined Market
1. Compare proposed product with present and
planned products of competitors
2. Build disaggregate market models
3. Compare a projected product with an old one
with similar characteristics
31

31. Expanding Internationally
• Robust
• Reactive
• Dynamic
• More responsive to
customers
• “Connected”
• Harmonized
• Visible
Supply chain challenges:
• Needs to be:
• Rule: a business can grow just if its supply
chain can grow faster than it
• Analyze potential and limitations of supply chain
32

32. Expanding Internationally
Ordering challenges:
• Wider market
• Larger EOQ
• Larger lead-time
• Larger safety stock
• More factories involved
• New contracts with factories and suppliers
34

33. Wider Market
• Factors that influence demand can be different in different
countries
• More investments
• More items produced
• Forecasts are more accurate for larger groups of items
• More raw materials needed
• Lower cost per unit of raw materials
• Items have a smaller cost per unit too
• Higher risks
35

34. Expanding Internationally
Supply chain strategies:
• Combine and balance push and pull strategies
• Customers should be aware of the existence of the
brands
• Suppliers should reach customers fast and effectively
• Invest in marketing communications
• Ethical norms have to be respected
• Conduct market research
33

35. Key Risk Considerations
• Choice of Market
• People
• Finance
• Sales and Marketing
• Tax and Regulation
36

36. Ishikawa Diagram
37

37. Future Research
• Testing and introduction of new items
• When rapid sales begin; rate of market penetration during the
rapid-sales stage; level of penetration, or sales rate, during the
steady-state stage
• Push and Pull Strategies
• Impacts of potential European and/or Asian distribution
centers
38

38. References
• Plossl, George W. – Production and Inventory Control, 2nd
Edition, Prentice Hall, 1985
• ISE 312 – Manufacturing Systems II class notes
• “How to Choose the Right Forecasting Technique.”
Harvard Business Review. N.p., 01 Aug. 2014. Web.
• Simon, Herman – International Expansion New York: EY,
2015. Ernst and Young. Web. 10 Sept. 2016
• The Bass Model: Marketing Engineering Technical Note
(n.d.): n. pag. Web.
39

39. Appendix
87

40. Table of Contents (1 of 2)
Forecasting Techniques
Delphi Method
Executive Opinion
Historical Analogy
Visionary Forecast
Diffusion Modeling
Drift Method
Exponential Smoothing
X-11
Simple Moving Average
Average Approach/Point Method
Naïve Approach
Seasonal Naïve Approach
Statistical Survey
Machine Learning
Define the Purpose
Prepare the Data
Select the Techniques
Make the Forecasts
Track the Forecasts
EOQ with No Value
Lead Time
Reserve Stock
Relations
Calculating Standard Deviation
Forecasting vs. Lead Time
The Origins of Leveling
Benefits of Leveling Production
Methods of Leveling Production
Changeover Time Reduction
Deathlok 365 Day Leveling
Compare with Competitor
88

41. Table of Contents (2 of 2)
Build Disaggregate Market Models
Compare Model
…in an Undefined Market
Supply Chain Challenges
Push and Pull Strategies
Implement the Strategies
Factors that Influence Demand
More Accurate Forecasts
Choice of Market
People
Finance
Sales and Marketing
Taxes and Regulations
89

42. Forecasting Techniques
1. Qualitative Techniques use qualitative data (e.g. expert
opinion) when a product is first introduced into a market
2. Time Series Analysis and Projection focuses entirely on
patterns and pattern changes and thus relies on historical
data (used when several years’ data for a product or product
line are available and when relationships and trends are both
clear and relatively stable). Better for near future
3. Casual Models use highly refined and specific information
about relationships between system elements (such as
related businesses, economic forces, and socioeconomic
factors)

43. Delphi Method
• Features a panel of experts while maintaining anonymity
• The panel is asked questionnaires which then formulate
new questionnaires based on previous responses
• Eliminates the bandwagon of popular opinion
• Fair to Very Good accuracy for short to long term
projections
• High cost ($2,000+)
• Long developing time (2+ months)

44. Executive Opinion
• Involves forecasting using the opinions, experiences, and
technical knowledge of managers and customers
• Can be used to modify an existing quantitative method
• Useful for accounting for unusual circumstances
• Special promotions
• Special deals
• New packages
• Rollouts
• New technology

45. Historical Analogy
• Compares product to a similar one already in the market
• Uses the current patterns to produce projections
• Good to fair accuracy for medium and long term
projections
• Need years of data from a similar product
• High cost ($1,000+)
• 1+ month to develop

46. Visionary Forecast
• Not scientific
• Uses personal insight, judgment, and facts about future
scenarios
• Poor accuracy for short and long term projections
• Inexpensive cost ($100+)
• Short developmental period (1 week)

47. Diffusion Modeling
• The Bass Model
• Forecasts the adoption of a
new product and when
• Can be used for products
recently introduced into the
market and non-introduced
products

48. Drift Method
• A derivative of the Naïve Method in which forecasts are
allowed to “drift” over time (increase or decrease)
• Drift is calculated by taking the average change in the
historical data
• Formula:

49. Exponential Smoothing
• A moving average method that calculates the average of a time
series
• Most frequently used formal forecasting method
• Very simple and small amount of data needed to support it
• Only requires three items of data:
• Last periods forecast, actual demand for this period, and
smoothing parameter
• Formulas:
• First order smoothing rearranged: Fnew = old forecast + alpha
(sales – old forecast)
• Second order smoothing when trends exist:
• Anew = old forecast + alpha (sales – old forecast)
• Bnew = Bold + alpha (Anew - Bold)
• New Forecast = 2Anew - Bnew

50. X-11
• It decomposes a time series into seasonals, trend cycles,
and irregular elements
• It is probably the most effective technique for medium-
range forecasting (3 months-1 year)
• Very good to excellent in the short term and good in the
medium term
• Used for tracking and warning, forecasts of company,
division, or department sales
• Cost of forecasting with a computer $10.00, computer
required for calculations
• Time required to develop an application and make a
forecast: 1 day

51. Simple Moving Average
• Calculating the demand for n the most recent time
periods and forecasting that value for future time
periods
• Forecast found using sum of demands over time
period
• Formula:

52. Average Approach/Point Method
• An average of all available past data is taken and then
used as the “point” for the forecasted future
• Accuracy falls within 50% above point or 50% below point
• Used when past data is available
• Greater the number of data points, greater the projected
accuracy
• Formula:

53. Naïve Approach
• Production of forecasts equal to the last observed data
points
• Not mathematically or computationally expensive
• Cost effective
• Used in fields with patterns/trends that are difficult to
predict
• Formula:

54. Seasonal Naïve Approach
• Accounts for products and product cycles that have
“seasonality”
• Sets each prediction equal to the last observed value of
the same season
• Uses months and days
• Formula

55. Statistical Survey
• A systematic approach in which a survey or handout is
distributed among a population with questions on it
• The results are collected and data is analyzed
• That data is used to form a projection or forecast
• The forecast may be altered based on the circumstances
the questionnaire was delivered under
• Biases, variances, outliers, etc. must be accounted for in
the population

56. Machine Learning
• Also known as Pattern Recognition
• Usage of algorithmic processes to teach computers to
predict trends based on previous data
• Requires a lot of time, money, computing power,
hardware, and software expertise
• Payoff can be substantial if done properly

57. 1. Define the Purpose
• How important is the past in estimating the future (for
independent demand)?  crucial for carry-over items (#
1), marginal for items # 2 and 3
• To calculate dependent demand (Orlicky’s
independent/dependent demand rule)  raw materials
• For budgeting purposes  accuracy required
• Level of accuracy tolerated: 5 ≤ tracking signal ≤ 7
• Forecast Time Period
• Medium term (3 months to 2 years)
• Short term (0 to 3 months)  forecasting throughout
the year to get a “handle” on demand

58. 2. Prepare the Data
• Different data are used for different products
• For carry-over items (old designs) use historical
data (there are a lot) of the same exact models
• For updated designs use historical data (there
are some) relative to similar past models (other
than judgement)
• For completely new product designs use
people’s opinions, judgements

59. 3. Select the Techniques
1. Qualitative techniques
• For completely new product designs (and updated
designs)
• Executive Opinion
• Visionary Forecast  Char-Nicanor Kimball
2. Time series analysis and projection
• For carry-over items (old designs)
• X-11
3. Causal model
• For updated designs
• Seasonal Naïve approach

60. • The most
sophisticated
technique that can be
economically justified
falls where the sum
of the two costs is
minimal
• Increased accuracy
 lower safety
stocks
• Weight the cost of a
more sophisticated
and more expensive
technique against
potential savings in
inventory costs
3. Select the Techniques (cont.)

61. 4. Make the forecasts
• Use the technique to
make the forecasts
• Compare the forecasts
with the actual sales to
estimate the forecast
error
• For each period,
calculate the deviation
 deviation =
forecasts – sales (keep
sign)
Plossl, George W. – Production and Inventory Control, 2nd Edition,
Prentice Hall, 1985

62. 5. Track the forecast
• Calculate the Mean Absolute
Deviation (MAD)  MAD =
(∑Deviation (without sign)) /
(#periods)
• Calculate the running (algebraic)
sum of forecast error (sum the
deviations, with their relative sign,
for every period)  RSFE
• Tracking signal = RSFE / MAD
• Forecast is ok if
5 ≤ tracking signal ≤ 7
Plossl, George W. – Production and Inventory Control, 2nd
Edition, Prentice Hall, 1985

63. • Customer specifies the quantity (make-to-order)
• The production run lot is limited by equipment
capacity
• The shelf life of the product is short
• Tool life or tool maintenance limits the run size
When the EOQ concept has no value

64. Lead Time
• The lead time is the period of time between when the
replenishment order (inventory at order point) is placed
and the material requested with this order has been
received into stock and it is ready for use
• Lead Time = time to order + time to make + time to travel
+ time to get in system
• This is the most critical period for the industry because
here the item is most vulnerable to be out of stock
(inventory is at the lowest point)
• Determining how much reserve stock is required is
crucial

65. Reserve Stock
• Calculating reserve stock is difficult because reserve stock depends
on:
1. Ability to forecast demand accurately  Uncertainty in the process
2. Length of the lead-time
3. Ability to forecast or control lead-time accurately  Uncertainty in this
process too
4. Size of the order quantity (fluctuating demand will exceed the average
half the time  normal curve)
• If the EOQ is large, the reserve stock is going to be large (Planned average inventory)
5. Inventory is proportional to the order quantity
6. Service level desired (given by number of SD’s)
• Order points require reserve stocks because of uncertainties that
cannot be eliminated

66. Reserve Stock (cont.)
• Apply statistical techniques for setting
reserve stocks only where their assumptions
are valid and only after testing
• All statistical techniques assume the future will
be like the past
• There will be changes along the way, underlying
assumptions may change, need signals to flag
• Tracking methods will indicate if corrections are
needed

67. Relations
• Planned Average Total Inventory = ½ Order Quantity +
Reserve Stock
• Order point (O.P.) = anticipated demand during lead
time + reserve stock
• Reserve stock = number of standard deviations
• number of standard deviations is given by service
level
e.g. service level = 98%  50% + 2 SD’s (95% / 2
= 48%)  reserve stock = 2 SD’s)
19

68. Calculating Standard Deviation
• Average D2 =
Variance
• SD = sqrt(Variance)
• Use service level to
calculate the
number of SD’s
• Calculate the safety
stock = # SD’s
Plossl, George W. – Production and Inventory Control, 2nd Edition,
Prentice Hall, 1985

69. Forecasting vs Lead Times
• What happens when the
forecast interval is not equal to
the lead time interval?
• Adjusted MAD = MAD (LT / FI )beta,
where LT = Lead Time Interval, FI
= Forecast Interval and beta =
constant depending on the
demand patterns of the particular
business (beta = 0.7 gives
reasonably good results)
• Or: Adjusted MAD = MAD x
(factor right column table)
Plossl, George W. – Production and Inventory Control, 2nd
Edition, Prentice Hall, 1985

70. Forecasting vs Lead Times (cont.)
• Anticipated demand during lead time
= (forecasted demand per week) x
(number of weeks of the lead time) ①
• Reserve stock must increase as lead
time increases, but this increase is not
directly proportional to the increase of
the lead time
• Reserve stock ≠ (reserve stock per week)
x (number of weeks)
• Reserve stock = (adjusted MAD) x (factor
right column table) ②
• (Or Reserve stock = SD x (factor left
column in the right part of the table))
• Order Point = ① + ②
Plossl, George W. – Production and Inventory Control, 2nd Edition,
Prentice Hall, 1985

71. The Origins of Leveling
• Production leveling was invented by the
Japanese
• Called Heijunka
• Goal was to reduce Mura (Mura = unevenness)
• Vital to the Toyota production and manufacturing system
• The goal
• To keep demand constant over a period of time, t
• Why?
• By keeping demand constant, leveling production is
easy

72. Benefits of Leveling Production
• No spikes in labor costs
• Labor costs won’t dramatically increase or decrease due to
unforeseen changes in demand
• Company won’t be paying idle workers and won’t need to hire more
workers
• Keeps production capacity from being overbooked
• Last minute orders can be handled and shipped
• Customers receive every order on time
• Improved quality control
• Machines require less maintenance and are running at full capacity
less often
• Production can be more easily monitored because demand level is
expected
• Error tracking improves

73. Methods of Leveling Production
• Leveling by volume
• Manufacturing for which there is a range of demand is done by
using the average, long term demand value
• Batches of product are made as small as financially possible
• Shipment can be smoothed by shipping date
• Leveling by product
• Smaller and smaller product batches used, reducing lead time and
throughput time
• Smaller batches incremented in a way that doesn’t lead to financial
losses or running out of product
• The Heijunka Box is used to organize production via date, time,
and quantity

74. Changeover Time Reduction
• Leveling demand reduces changeover time throughout
facilities
• Changeover time is the amount of time, t, is takes to switch from
one manufacturing process to another
• Changeover time has three components
• Clean up: breaking down the machinery of the old process and
storing it
• Set up: building/setting up the new machinery required for the new
process
• Start up: getting the new production process running
• Leveling demand year round (365 days) reduces the
amount of changeover time that is necessary

75. Deathlok 365 Day Leveling
• Deathlok will change from just making products that sell
quarterly, to catalogs that sell different products year round
• This will level both production and demand, allowing for
• Reduction of changeover time
• Increased accuracy of forecasting
• Increased production effectiveness
• Increased worker satisfaction
• Increased Total Quality ratings
• Better supply chain operation
• Reduction of bottlenecks in supply chain
• Reduction of swelling inventories
• Reduced defects in products
• Reduced customer dissatisfaction due to misplaced, delayed, or
mishandled orders

76. 1. Compare with Competitor
• Name: product differences measurement
• Inside experts must come from different
disciplines to make this approach successful
• Marketing
• R&D (research and development)
• Manufacturing
• Legal
• Etc.
• Their opinions must be unbiased

77. 2. Build Disaggregate Market
Models
• Divide different segments of a complex
market for individual study and
consideration
• Project the S-shaped growth curves for the
levels of income of different geographical
regions
• These curves give information about the rate of
penetration of the market we are interested in

78. 3. Compare Model
• Use data and information regarding an “ancestor”
product with similar characteristic of the projected
one
• This comparison let us estimate the variability to
be expected
• How much our projections ill differ from the actual
data
• Due to economic (prices, income, etc.) and other
factors

79. … in an Undefined Market
• The market for a new product is often weakly
defined or few data are available
• History seems irrelevant
• Applied for gas turbines, electric and steam
automobiles, modular housing, pollution
measurement devices, and time-shared computer
terminals
• We do not need this kind of study!

80. Supply Chain Challenges
• Supply chain needs to be:
• Robust to withstand changes in local demand
• Reactive  multiple markets to be served
• Dynamic  no traditional push and pull model
• More responsive to customers (products, sourcing, manufacturing,
transport)
• “Connected” to be more agile and organized
• Harmonized  new relationships with suppliers
• Visible
• Rule: a business can grow just if its supply chain can
grow faster than it
• An analysis of the potential and the limitations of the supply chain
is necessary

81. Push and Pull Strategies
Push Strategy Pull Strategy
Suppliers have an active role Customers have an active role
Suppliers “push” the goods
toward the customers
Customers “pull” the goods
they need
Products directly brought to
the customer (where he is
aware of the existence of the
brand)
The supplier should motivate
the customer to reach out to
the brand

82. Implement the Strategies
Push Strategy Pull Strategy
• New shops at the
customers’ locations
• Trade show promotions
• Sell products in new
retailer stores
• Direct selling to
customers (e.g.
showrooms)
• Engaging packaging
design
• Reach customers faster
with shipments
• Advertising
• Mass media
promotions
• Customer
relationship
management
• Sales
promotions and
discounts

83. Factors That Influence Demand
External Internal
Business Conditions Advertising
Nation’s Economy Promotions/Pricing
Competitive Forces Selling Efforts
Quality Improvements
On-Time Deliveries

84. More Accurate Forecasts
For a large
group of items,
the forecast is
more accurate
because positive
and negative
differences
between the
actual and the
forecasted
demand nearly
cancel out
Plossl, George W. – Production and Inventory Control, 2nd Edition, Prentice Hall,
1985

85. Choice of Market
• Over 200 countries to choose from
• Some countries are riskier than others
• Economy
• Safety
• Which market will accept the product the easiest?
• Which market has the potential for maximum
profits?

86. People
• Existing domestic workers may need to locate
abroad - is it safe?
• New hires will be made therefore there must be
an investment in training
• An analysis of the labor market in the potential
new market
• Are there jobs?
• Consider hiring some local employees

87. Finance
• Consider where the expansion will be funded
• The home country or the new one
• Local taxes and regulations must be studied
• Research incentives to bring business into new market
• A lot of areas offer incentives to foreign companies to set up in their
country
• Consider working with an international financial institution
if Deathlok is not already
• Partnering with companies who understand the local
economy is suggested

88. Sales and Marketing
• Culture is nuanced
• Be sure to study current successful marketing tips
in new market
• Translating the operations into the local language
may not be as easy
• Key concepts may be lost

89. Tax and Regulation
• The local market will have different legislative
laws, import restrictions, liability laws etc.
• Currency risk is present
• How stable is the local market?