# Supply Chain

## 挑战

丝绸之路就是最早的supply chain，从中国运丝绸和茶叶，从欧洲运香料。而现在的supply chain复杂度更高。

现在Supply Chain的挑战：Faster, more flexible, more granular, and more efficient 

1. Rising Customer Expectations:\
   \- Real Time Tracking \
   \- Precise Inventory Management \
   \- Wide range of product availability \
   \- High availability, reliable lead times, transparency <br>
2. &#x20;Fast Fulfillment and Large Volume of Orders\
   \- Fast-paced timeline of ordering (overnight shipping for holiday)\
   \- Quick growth of parcel and less-than-truckload shipment <br>
3. Uniqueness and Large Variation of Order\
   \- Special requirement of configuration, urgency and delivery locations\
   \- Ad hoc and real time planning (last minute change, change typo, etc.) <br>
4. Leader and Management Team requires 360 degree visibility \
   \- Leaders orchestrate demand and supply, information from different channels \
   \- End-to-End transparency throughout the supply chain <br>
5. Accelerating Scale, Scope and Depth of Data \
   \- Utilize such immense data sets to drive contextual intelligence \
   \- Enable more complex supplier networks that focus on knowledge sharing and collaboration instead of just completing transactions is critical&#x20;

![](https://cdn.mathpix.com/snip/images/v8oLY4R8s-EDhP6VifF7LgPGCTqbzscAgRu_Ulqf1CY.original.fullsize.png)

![](https://cdn.mathpix.com/snip/images/QzviNnOpUrlmfFNBCZdwuQYKdz0vt0T-I6xnrm9XBb0.original.fullsize.png)

RFID: Radio Frequency Identification&#x20;

Twitter feeds: sentiment 大众对产品的考虑，可以给出降价

[Bullwhip Effects](https://zh.wikipedia.org/wiki/%E9%95%BF%E9%9E%AD%E6%95%88%E5%BA%94)

## Data角度的Supply Chain&#x20;

![](https://492391472-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LiBoWdc5sh0EFrazIOe%2F-LnhITTJthMCQ8-msIwa%2F-LnhwwnpA-Tx2sRaZw-V%2Fimage.png?alt=media\&token=8a465536-7a1d-4091-8280-72a5c5ecfaa7)

### 正在解决这些问题的公司/平台

Oracle SCM, SAP APO (Advanced planning and optimization), JDA, IBM Watson supply chain, Kechie ERP, E2 Open, Deepsense.ai&#x20;

### Retailers

Demand forecasting, supply planning, A/B testing, root cause analysis, inventory optimization, price optimization, on-time delivery, customer survey etc.

![](https://492391472-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LiBoWdc5sh0EFrazIOe%2F-LnhITTJthMCQ8-msIwa%2F-Lni2oC_aqW-MnOS-IhW%2Fimage.png?alt=media\&token=f8937b61-fd5e-46a1-b645-8bd8d216cbd1)

#### Logistic Companies

Transportation/fuel optimization, lead-time prediction, on time performance&#x20;

![](https://cdn.mathpix.com/snip/images/_rP-vAQgS_YZfHqBFi64m9rYJOTX4lV214HZPXdTF9I.original.fullsize.png)

#### Consumer Goods Companies

![](https://cdn.mathpix.com/snip/images/1Lo00gm4HTujMwwD-jYH0VxaVX0fp1upDw711lzGdt0.original.fullsize.png)

#### Manufactoring&#x20;

![](https://492391472-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LiBoWdc5sh0EFrazIOe%2F-LnhITTJthMCQ8-msIwa%2F-Lni3TLs9giP0-Yf2AJY%2Fimage.png?alt=media\&token=d97236fb-ee53-4bbb-b2e3-6d854f5b058f)

#### Pharmaceutical&#x20;

![](https://492391472-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LiBoWdc5sh0EFrazIOe%2F-LnhITTJthMCQ8-msIwa%2F-Lni3fSUIdlC5qe1tTAV%2Fimage.png?alt=media\&token=c359e86b-96c4-49d7-b2c4-ab66a74708b7)

## Demand Forecasting&#x20;

1. Traditional Commercial Software: SPSS, SAS&#x20;
2. Commercial Machine Learning Platform: Google Cloud AutoML, DataRobot, Dataiku, H2O
3. Build in-house solution from scratch&#x20;

## 资源

{% embed url="<https://www.coursera.org/learn/supply-chain-analytics-essentials>" %}

{% embed url="<https://zh.coursera.org/learn/predictive-modeling-analytics>" %}

{% embed url="<https://zh.coursera.org/learn/supply-chain-logistics>" %}