Sunday, August 31, 2014

Become a Prophet in Supply Chain Management

Last night my friend told me about his experience back in UK. He went to a gipsy prophet in Manchester to ask about his future. And the prophet is so magical, as he described, that she named out the name of his every relative without any background information. Whether does it come from super nature or not,  it would be wonderful if we hired a gipsy prophet in our supply chain management team. Just kidding…

As I look into the 4 basic forecasting methodologies, 5 key concepts to be taken into account and 7  ways to optimize the process of forecasting, I realized that there are so many variables in real world  forecasting that some of them might be impossible to explain by using simple causation analysis, such as human behavior.However, with the development of cloud computing and big data analytics, correlation can now be nearly regarded as causation as massive data is being collected and analyzed.

If I added a pair of Nike shoes into my Amazon wish list, does it mean that I will definitely purchase it in the future? Absolutely not. But what if 10,000 people like me added the same nike shoes into their wish list? Things would have completely changed. Nike will procure more raw materials of this specific kind of shoes, rearrange production line, and even tailor the production amount of each size by categorizing the potential customers. Amazon will increase the stock of this item in case further demand go beyond their inventory.

There’s no doubt that companies, especially those who possess booming e-commerce business are able to utilize cloud computing and big data analytics to modify and optimize their forecasting process in supply chain management. And the more information they can efficiently exploit from their data, the more accurate the forecasting could be.

Now here’s an example. Cloud computing and big data analytics is making one company the quickest product producer of top trends in affordable fashion, Zara. This company has always shown strong growth and is incredibly sustainable through application of their Business Model Innovation. We all know that Zara, by shipping only a few of each item to its retail stores, successfully reduced the risk if the trend they created doesn’t meet the market demand. But this is just what has been told.

1.     Translate human behavior into data
Every Zara store has surveillance cameras. Every sales representative carries a PDA with them. Every feedback from the customer has been briefed by the store manager through Zara’s information network to the headquarter. By collecting and analyzing customer feedbacks, Zara could then optimize its supply chain forecasting process and form the best strategy for each segment.

2.  Online testing
Zara sees its online business as “testing the water”. In order to achieve higher forecasting accuracy so as to efficiently manage the whole supply chain, Zara collects the data from the browsing and searching history of its customer, analyzes consumption habit, monitors online purchase and wish list, etc.

3.      Vertically Integrated supply chain
Zara’s vertical supply chain enables it to carry out management decisions as soon as possible. Once the data has been processed, the whole arrangement of supply chain reacts in a short period of time.

30,000 Stock-Keeping Units per year, unsold items account for 10% of stock versus industry average 17%-20%, commits 50%-60% of production in advance of the season versus 80%-90% for other…Zara is taking the lead by combining new technology with the forecasting process of its centralized supply chain. It has its own scientific ways to become a prophet in the apparel industry that  “One day it’s in and the next day it’s out”.

P.S. Zara has invested in high-tech equipment and extra capacity that allow its factories to accommodate sudden production increases or changes, which reminds me of the zone of strategic fit. Its goal is to build a more responsive supply chain to meet the uncertain demand. After all, fashion is so unpredictable.

My sources:

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