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.
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.
8 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.
1. Efficiency and performance gains require predictive, data-driven insights.
2. Analytics are the wave of the future for next-generation supply-chains.
SAS Case Study STEEL MANUFACTURER IMPROVES PERFORMANCE, PROFITABILITY
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.