Supply Chain Analytics – Introduction and Bibliography

What is Supply Chain Analytics – IBM –

What is Supply Chain Analytics – McKinsey –

Deloitte on Supply Chain Analytics

Like in various others parts of business, analytics which is a combination of statistics and data processing power of computers has enabled the processing of supply chain related data to point out cost reduction or profit improving niches.

Deloitte consultants pointed out the following.

Parametric pricing The new parts a manufacturer procures differ only in small, specific ways from earlier versions in number of cases. A company with good parametric price modeling ability can identify these parameters of change in new parts and use them to determine what the net price change should be. That expedites the negotiating process and helps a company avoid overpayment.

Commodities price volatility:  Raw materials fluctuate in price and makes business planning difficult. Unexpected price jumps can damage margin. Companies can use analytics to develop macroeconomic models and come out with better predictions – and use options, futures and contract provisions to hedge.

M&A integration: When the merger is between two companies in the same industry,   they may be using the same parts/materials in their operations, but may have different material numbers and most likely different purchasing prices. After merger, such parts can be identified using analytics so that buyers can rationalize their procurement and save money.


September 9, 2019 Kumar Singh

“Without the right tech investment, you aren’t optimized and you aren’t synchronized.”

Domo’s approach,  is that organizations need to link supply and demand in order to understand the customer in the middle and ensure transparent reporting throughout.

Synchronizing the two creates collaboration, and collaboration starts with data. Domo offers a variety of simple, intuitive ways to get the right data to the right people, from live data dashboards with predictive alerts to internal reports delivered across the organization. Colgate-Palmolive’s Ann Tracy argued that collaboration is needed to study, analyze and apply data analysis. Data needs to be accessible across each function of the business—from senior leaders on down to more junior staff—and ultimately add value for the end user.


In 2013 the Journal of Business Logistics published a white paper calling for   research into the possible applications of Big Data within supply chain management. Since then, significant steps have been taken.

IWCR SAS Report on Supply Chain Analytics in 2010

Predicting demand accurately in volatile conditions requires sophisticated math based forecasting that can include downstream consumption data such as point-of-sales data, and model the impact of sales promotions, price, and other factors on demand. Analytics provides the capability.

SAS identified the follow levels of analytics.


Level 1: Standard reports
Level 2: Ad hoc reports
Level 3: Query drilldown (or OLAP)
Level 4: Alerts
Level 5: Statistical analysis
Level 6: Forecasting
Level 7: Predictive modeling
Level 8: Optimization

When a company’s supply-chain management is fueled with data-driven insights, it is more effective at controlling costs, thereby protecting profits.

SAS emphasizes:
1. Efficiency and performance gains require predictive, data-driven insights.
2. Analytics are the wave of the future for next-generation supply-chains.


A large, Asian manufacturer of steel (19,000 employees working to produce 28.5 million tons of steel annually), provided analytics support to  two of its process innovation (PI) programs using sas’s software.  the PI programs had a goal of updating 30-year-old business practices to improve efficiency and competitiveness. First, the company used sas to extract, transfer, and transform its ERP and legacy data into a data warehouse. secondly, the company combined sas’s analysis capabilities with its six sigma Project  tracking system. this combination allows managers to gather data on PI
projects, identify most-critical quality issues, and analyze them for root causes. By enabling daily and monthly monitoring, the company can resolve issues early on and improve overall manufacturing processes with the first PI phase, the company achieved a 50 percent reduction in lead times for standard hot coil production (from 30 days to 14 days), and a 60 percent reduction in inventory (from 1 million tons to 400,000 tons).

Further, by analyzing and then making necessary improvements to the manufacturing process, the company was able to reduce the scrap ratio on hot coil from 15 percent to 1.5 percent, leading to additional savings and  resulting in a total ROI of over $15.5 million in less than two years.

Source:  IW/SAS Supply-Chain Analytics Survey.  Email survey: Between June 8 and June 15, 2010,
Penton Research e-mailed invitations to participate in an online survey to 37,629 IndustryWeek print subscribers.  By June 30, 2010, Penton Research received 398 responses, a 1.1 percent response rate. Of those, 210 respondents that were involved in their companies’ supply-chain operations were considered qualified to answer the questions.

Updated on 9 September 2019, 14 April 2017.