From this week’s reading, “Building a flexible supply chain
for uncertain times” and “Ten ways to improve inventory management”, clothes
inventory issue in one of China’s largest apparel companies Metersbonwe has
come to my mind. Because according to last year’s financial report of
Metersbonwe, the company’s inventory grew to 473.3 million which is accounting
for 83% of its net assets.
Metersbonwe is a loyal learner of Zara in China, trying to
copy Zara’s success in China. However, it’s not as fast as Zara. What’s more,
the value of those inventories, lying in the warehouse, decreases every day in
the fast fashion industry. From the view of management, Metersbonwe has not learned
from Zara how to attain low inventory in the condition of fast supply chain.
After reading our articles, I’d like to analyze why Metersbonwe is not a good
imitator from following two aspects.
Information acquisition
To establish a responsive, fast and flexible supply chain is not only the business of operating department. Some companies recruited leaders
from production, procurement, logistics, and sales to discuss and make decisions
of supply chain together in order to optimize the supply chain. By doing this,
the supply chain is healthy, and has a reasonable basis from comprehensive information
throughout company and customers.
Metersbonwe has a severe problem in information communication.
One critical reason is that Metersbonwe applies franchises plus directly-manage
stores sale chain, and the number of franchises is about 80% out of total store
number. Compared with leading fast fashion companies such as Zara, H&M and
UNIQLO which only has directly- manage store, it’s much more difficult to get
information from sale channels. Obviously, it will be very complicated to
manage, and the response to volatility will be slow. For example, Metersbonwe
changed a lot on women’s clothes design in 2011 autumn, which was not accepted
by franchises however. The order was very few, but the sales department
reported good sale performance. Because of this very lack of communication,
they can’t make the right decision in ordering raw material and so on.
On the other hand, Zara very focus on exchanging information
throughout every part of Zara’s supply chain. They insert layers of bureaucracy
that can communicate between departments and are designed to make information
transfer easily. Market specialists are in constant touch with store managers
and provide quick feedback about new designs and give possible market prices.
Dealing with bullwhip
In Metersbonwe sale architecture, franchises intensify the
bullwhip effects. Franchise is an additional component in bullwhip chain if
compared with directly-manage stores. Information distortion is even more amplified
in this architecture.
At the meanwhile, Zara keeps constant flow of updated data
which mitigates the bullwhip. As far as I know, communication and comprehensive
investigation is the best solution to deal with bullwhip effects. In this way, Zara avoids overproduction and
maximum their production effects.
Except for two aspects above, Metersbonwe still has other supply
chain management problems which are also mentioned in “Ten ways to improve
inventory management”. When Metersbonwe stores 160 million dollar inventories,
They still haven’t come up with any effective discount sale, and exacerbated
inventory pileup. This indicates that they don’t have action plans that curb
more excesses from being created.
Question
In the article " Cleaning the Crystal Ball", the author claims several common understanding which may lead to wrong forecasts, including average is not reliable and crowd can make better forecasts than experts. Some opinions are novel to me, but the author still doesn't provide some definite methods and principles when forecasting. So if the average is not the estimator, which or which range should be? And I don't believe crowd can make better forecasts just because they don't know much about statistic methodology and will think about drivers behind the events.
Question
In the article " Cleaning the Crystal Ball", the author claims several common understanding which may lead to wrong forecasts, including average is not reliable and crowd can make better forecasts than experts. Some opinions are novel to me, but the author still doesn't provide some definite methods and principles when forecasting. So if the average is not the estimator, which or which range should be? And I don't believe crowd can make better forecasts just because they don't know much about statistic methodology and will think about drivers behind the events.
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