By Bruce Jones, sales manager: commercial division, SAS Institute
There is an urgent and growing need for organisations to complement their transactional enterprise resource planning (ERP) and supply chain management (SCM) systems with predictive analytics.
That`s because although these transactional systems serve as the foundation for supply chains, providing an awareness of operations and potential problems, it is predictive analytics and the knowledge it brings as well as the fact-based decision-making it enables, that drives financial performance.
In addition, companies across the entire extended supply chain need to fill the gap between planning and execution systems. Visibility needs to replace the intelligence void in order to increase profits and customer satisfaction.
Consider the following:
* You know that your company spends R100 million annually on direct and indirect services. In an effort to reduce cost, eliminate risk and rationalise a base of 10 000 suppliers, whom do you select for the best combination of cost, quality and on-time delivery? And which commodities should be sourced through request for proposals versus reverse auctions?
* Inventory is currently held equally at 10 warehouses around the country. But given anticipated customer demand next quarter, where should that inventory be held to serve those customers most profitably?
How you answer these questions impacts efficiency, cost and profitability. But determining the right answer isn`t easy, due in large part to the visibility gap.
SCM applications are focused on driving efficiencies in the production process while ERP systems are geared to managing the transaction-processing aspects of the supply chain. Each is essential, and together they`ve made supply chains more efficient and have reduced overall cost.
The problem comes when they`ve done their job and processes are already as efficient as possible. Where, then, does a company`s subsequent revenue growth/cost reduction come from?
One of the most powerful ways is by making better decisions, whether they`re procurement, manufacturing, logistics, sales or marketing-related. Current supply chain decision-making is akin to making a bet and rolling the dice: you think you know what the outcome will be, but you can`t be sure. But if the bet involves millions of dollars in inventory, jeopardising customer relationships or selecting a supplier that can`t meet projected growth, can you afford to be wrong?
Knowing what to do is a challenge because the most common decision-support tool for supply chain managers is the Excel spreadsheet. While charts display a wealth of information, they were never intended to predict the quality, cost or profitability outcomes of specific decisions.
In any economy, this practice doesn`t lend itself to driving inefficiencies and cost out of the supply chain. This is why industry analysts are advising organisations to complement their transactional ERP and SCM systems with predictive analytics. A quick look at the research landscape bears this out. In his February 2002 Viewpoint titled "Linking Supply Chain Analytics to Operations: The Key to ROI," IDC Research vice president Henry Morris said: "[The] linking of analytics to operations and ongoing measurement is the key to maximising ROI." In a presentation "An Analyst`s View of Supply Chain Analytics," Gartner`s Karen Peterson predicts that by 2006 supply chain analytics, more so than ERP or supply chain planning applications, will be used by organisations to gain an advantage.
To understand how predictive analytics turns supply chains into financial drivers and sources of competitive-edge, consider the following:
* A baseline capability is query and reporting, or the ability to slice/dice data to understand current business operations. This helps answer questions such as: How much am I currently spending on transportation to ship 500 pallets of paint from a warehouse in Gauteng to a retail outlet in the Cape once a month.
* This is important, but the larger and more value-adding piece of the puzzle is the ability to accurately predict the future and to pre-empt potential business problems. For example, if the retailer asks that delivery be switched to twice a month, do you agree without having a clear understanding of not only how that will impact your product cost and profitability, but also the retailer`s? Using predictive analytics, you see that this would turn the product into a money loser due to the cost of unloading and stocking the paint on a more frequent basis. So instead, you collectively decide the right financial decision is not to alter the delivery schedule. This predictive capability comes from analytics` roots in business intelligence. So while ERP and SCM transactional systems capture supply chain data, predictive analytics takes that information and models the outcome of virtually any decision supply chain managers make.
* Predictive analytic software leverages the multimillion-rand investments made in existing IT infrastructures. Sitting on top of ERP and SCM systems, it continuously monitors and analyses the supply chain - always on the look out for opportunities to optimise the buy, make, store, ship and sell activities.
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