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November 2010
From Logistics Data to Supply Chain Intelligence
-Robert Seaker, Trax Trusted Visibility Product Manager
The past two decades have seen the proliferation of large-scale supply chains where expanding markets and low-cost suppliers are operationally and strategically linked across the globe. A greater variety of sales channels, languages, currencies, and expectations, not to mention greater distances in the movement of goods, are now part of the everyday process. Expectations and operational structures have become much more dynamic, as well.
Whether pertaining to order cycle time, transportation costs, inventory availability, or simply tracking an order, there are many opportunities within these expansive and ever-changing environments for processes to be streamlined and excessive costs to be driven out. This is where data availability becomes foundational. It is data that feeds the analysis and it is the analysis that drives effective process designs and solutions.
More often than not, these opportunities are not readily observable or clearly understood. And even in this age of ERPs and data warehouses, there is no guarantee that requisite data exists much less is readily available. Therefore, formal strategies and structures for actively pursuing opportunities are necessary, even if they require crossing traditional boundaries.
Many leading edge firms are doing just that. They understand that effective management is only as good as data is complete, accurate, and accessible and they are being rewarded for it.
"[Accenture] research found a powerful link between organizations with notable analytical orientations and superior market performance. High performers are much more likely to value fact-based decision making and to have the skills and capabilities in place to effectively use analytics across their organizations." i
These firms have built formal business intelligence (BI) capabilities that include the tools and processes used to collect data across multiple stakeholders and they use this data to drive better business decisions. Data analysis can pertain to any level or breadth of scope, whether they are narrow, daily activities or something that is more strategic in nature. BI opportunities are particularly lucrative when focused on the inherently fertile supply chain environment. Applicable areas of analysis here may include:
- Expensive, out-of-contract spot rates
- Equipment load and capacity planning
- Shipment consolidation into lower-cost modes
- Co-loading with other shippers
- Network design and rationalization
- Demand forecasting by SKU and region
- SKU Inventory life cycle management
- Optimal materials sourcing based on total landed cost
Analytical Methods
Once the necessary data is obtained, there are a number of analytical techniques that can be applied to it. Approaches can be either exploratory in nature where there are no preconceived notions in the search for opportunities or potential issues or they can be confirmatory where added detail or root cause is sought for pre-defined problems.
A spreadsheet is perhaps the most readily available and common tool used for pinpointing specific issues or for shaping inferences about a given situation. By applying filters, sorts, pivot tables, or even a few built-in statistical methods, patterns among variables can be identified. Additional statistical packages such as SAS and SPSS are also available for offering greater power in deriving relationships among the many logistics variables.
Mixed integer programming and simulation software are more sophisticated tools for triangulating to the ideal state, whether it applies to a specific variable or an entire flow or network.
Logistics Service Provider (LSP) Applications
Many opportunities exist as well for LSPs to leverage BI and analytics. The vast amount of available data inherent to any carrier's operation makes it a natural for this type of initiative. Whether it relates to transit times, loss and damage, equipment utilization, or costing and rating, by deriving and rigorously evaluating data, service can be enhanced, costs can be reduced, and the brand improved.
The effort a LSP provides for its customers goes beyond what could be perceived as commoditized services. It is more than just pricing. If a provider's logistics offerings were fully understood by all parties as to their real value, then the relationship will be strengthened. There are plenty of operational successes that provide value. A self-scorecard conducted by the carrier and enabled by data and analytics, for example, would be an effective tool in growing and nurturing a partnership. Some of the areas that can be assessed are:
- Response rate / shipment status
- Billing timing and accuracy
- Equipment availability
- POD availability
- Lost and damage
- Assessorial patterns
For internal process improvement, carriers can also apply analytics to improve performance in areas such as billing cycles, capacity utilization, fleet management, and driver performance. The result is a greater return on assets, improved cash flow, and higher profitability.
As more firms come to appreciate the value gained through supply chain analytics, especially as it pertains to complex, dynamic, and high-cost global supply chain operations, there will continue to be a greater demand for IT infrastructures and processes to support it.
iJeanne G. Harris, "How Consumer Goods Companies Compete on Analytics to Achieve High Performance" Accenture-Research Note (July, 2007)
September 2010
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