Supply Chain Collaboration through Business Intelligence

• Process Metrics that measure the cost and service performance of the processes that cross functional areas. Typically data from these processes are stored in different systems. Therefore, insightful metrics must pull data from different systems. Examples of these metrics include cost-to-serve, total landed cost of imported items, customer profitability, adjusted product profitability, cycle times and the cost of quality.

• Extensions of Functional Capabilities that current applications (transaction systems or execution systems) do not or can not provide, such as an inventory planning timeline; the reason for a stock out; how to respond to a runaway ad or promotion to minimize the chances of out-of-stocks; predictive analytics regarding excess or short inventory, excess or short capacity, or ability to meet demand; comprehensive inventory analysis (such as the “time supply histogram”); a complete view of and ability to manage all transportation costs; and, the ability to conduct spend management and strategic sourcing analyses.

• Collaboration with customers, suppliers or trading partners which could include vendor scorecards; customer service measures such as “The Perfect Order;” a common view of demand forecasts including inventory, open orders, goods-in-transit, cancelled orders, returns, damaged items across the channel; delivery status and performance; and, asset management for an equipment company with a shared view of maintenance schedules and costs, spare parts visibility, up-time and reasons for downtime.

Performance of the supply chain must be accurately understood to make decisions to improve costs or performance, and actions taken to affect those decisions. These analyses require information from multiple, distinct applications systems which is often quite challenging for most companies to obtain because information sits in many different databases and must be extracted, mapped, and integrated into a common database. And in the case of the extended supply chains, one must bring together, analyze, and share data from vendors, customers or trading partners, which becomes even more challenging.

For example, take the creation of a vendor scorecard for a food retailer to measure the performance of its vendors. A simple scorecard would track information on sales, on-time delivery (percentage or orders delivered within 30 minutes of the scheduled delivery time), in stock availability (percentage of the items cut from an order), inventory levels at the retailer (in both units and days of supply), committed and actual lead times, damages and miss-picks (number, dollar value and percentage of items damaged in shipment or not ordered), and billing accuracy. This information should be tracked over several time periods such as current week or month, quarter-to-date, and year-to-date, and it must be compared to last year as well as against the approved budget. Then, using this insight, the retailer can rank their vendors not only on each metric, but also on weighted aggregate performance. If a supplier is ranked in 50th or higher among the top 100 vendors on any criteria, it is a powerful incentive for that vendor to improve its performance!

• lowering its warehouse inventories by ten-plus per cent or more;

• improving in-stock availability and thereby sales by one percent or more;

• and, improving operating efficiency in its warehouse, buying office, and scheduling/expediting group by a significant amount.

However, most retailers do not utilize such a scorecard, because of the effort required to assemble it. Some will conduct an arduous, time consuming effort on a periodic basis to assemble this information from their many different systems, departments, or locations. But, they do not produce this scorecard regularly and use it as a tool to manage their vendors, due to the enormous amount of time and effort required to pull it together. Even those companies that already own a business intelligence tool are not often deploying it against supply chain applications. This is due to the long and expensive process required to implement a traditional business intelligence application.

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