04 Mar 2013
3 mins read
Supply Chain of Information
Supply Chain essentially is considered to be the flow of goods and services across the manufacturing / retail value chain in order to deliver the desired goods/service to the end-customer. With the proliferation of information technology, supply chain in addition to the above mentioned components is essentially the flow of information between the different entities – Suppliers, OEMs, Manufacturers, Distributors and Retailers.
Interestingly these entities have varied touch points outside enterprise boundaries – web (social, videos, blogs etc..) handheld devices (EOBRs), RFIDs etc that makes it a challenge to control the bullwhip effect. This necessitates for a strategy to handle unstructured data (Big Data). What the Research Says?
Organizations have realized this importance and are focusing on Big Data initiatives, but according to a research by Supply Chain Insights LLC – deriving intelligence from sources outside enterprise boundaries still remains to be a challenge.
Source: Supply Chain Insights The Landscape Big Data initiatives cannot be standalone rather they must be planned around the existing DW/BI infrastructure to derive meaningful insights. Following is an illustration of the big data landscape for supply chain:
Key aspects of the Big Data landscape are as detailed below:
Once the data from the sources outside enterprise boundaries are brought within the enterprise, data modeling can be done w.r.t the key supply chain entities and analytics performed on top of these models.
Having seen the landscape of Big Data, let’s explore how Big Data can bring about a difference in key Supply Chain areas: Demand Planning, Supply Planning and Order Fulfillment: Demand Planning: Current Process: Traditional process based on past sales data and any specific factors (e.g.: seasonality) influencing sales
Envisioned Process with Big Data: Critical inputs for demand planning from Web clickstreams, Videos, Facebook, Twitter feeds, News article shared, Enquiry (web, email, CRM) Supply Planning Current Process: Traditional process takes into consideration past supplier performance – lead time to delivery, quality, and proximity Envisioned Process with Big Data: Make the current process robust by bringing in the supplier stability quotient from Emails, News Feeds on Supplier w.r.t R&D, Re-Structure, Financials, Technology etc. Order Fulfillment Current Process: Traditional process focusing on distributor’s past performance and location proximity Envisioned Process with Big Data: Assess the Political, Economic, Social and Technological’ (PEST) factors, environmental factors and identify probable delays in delivery, suggest alternate routes. Key data sources such as News Feeds – Geographic, Political, XML feeds (weather.gov), Trailer/Pallet Tags, Electronic On-Board Recorder (EOBRs), Geographic Positing System, Mapping data
As seen in the above use-cases (a sample) identifying and processing big data will bring in new perspectives to an organization that when integrated with the existing BI environment can bring about valuable insights. I would appreciate your thoughts on this article and additional use cases for Big Data in Supply Chain.
Thanks for reading!
BI & Analytics
11 Jun 2020
28 May 2020
08 May 2020
24 Apr 2020
13 Apr 2020
06 Apr 2020
31 Mar 2020
26 Mar 2020
23 Jun 2017
06 Aug 2015
13 Jul 2015
28 Oct 2014
17 Apr 2014
24 Mar 2014
22 Jan 2014
20 Dec 2013
01 Nov 2013
26 Sep 2013
03 Sep 2013
26 Aug 2013
29 Apr 2013
21 Feb 2013
04 Feb 2013
03 Jan 2013
17 Feb 2012
Digital Assurance
06 Feb 2013
Digital Assurance, Enterprise Solutions
14 Feb 2013
18 Feb 2013
27 Feb 2013
Others
01 Mar 2013
Enterprise Solutions
05 Mar 2013
18 Mar 2013
Digital Assurance, Enterprise Solutions, Others
22 Mar 2013
12 Apr 2013
26 Apr 2013
13 May 2013
11 Jun 2013
17 Jun 2013
25 Jun 2013
19 Aug 2013
27 Aug 2013
10 Sep 2013
19 Sep 2013
24 Sep 2013
30 Sep 2013
01 Oct 2013
03 Oct 2013
19 Nov 2013
Enterprise Solutions, Manufacturing and Consumer
28 Nov 2013
03 Dec 2013
03 Jan 2014
27 Jan 2014
31 Jan 2014
12 Feb 2014
13 Feb 2014
20 Mar 2014
11 Jun 2014
Manufacturing and Consumer
26 Jun 2014
30 Jun 2014
10 Jul 2014
15 Jul 2014
16 Jul 2014
18 Jul 2014
26 Aug 2015
28 Sep 2015
07 Oct 2015
26 Oct 2015
Infrastructure Mgmt. Services
07 Mar 2016
22 Mar 2016
13 May 2016
23 May 2016
Application Transformation Mgmt.
11 Jul 2016
25 Aug 2016
03 Sep 2016
14 Sep 2016
15 Nov 2016
22 Nov 2016
25 Nov 2016
Business Process Services
25 Apr 2017
Banking and Financial Services
18 May 2017
30 May 2017
27 Jun 2017
18 Jul 2017
26 Oct 2017
Healthcare, Insurance
28 Nov 2017
11 Dec 2017
25 Jan 2018
21 Feb 2018
14 Mar 2018
22 Mar 2018
23 Apr 2018
15 Jun 2018
18 Jun 2018
( Mandatory field * )
The information you provide will be used in accordance with our terms ofPrivacy Policy
Please Check on "I Agree" to register for the blog.