Business intelligence (BI) has come a long way from its management reporting roots. Analytical decision support is embedded within today’s supply chain planning, manufacturing and logistics solutions. Users gain insights based on real-time data rather than yesterday’s batch roll-ups. They can manage by exception, get automated recommendations and evaluate trade-offs before they enter transactions—whether they’re transferring inventory, releasing work orders or arranging shipments.
These recent technical advances in BI are impressive. But their translation into a more intelligent supply chain that sustainably optimizes business performance remains elusive. There are three primary challenges:
To optimize long-term performance, organizations must balance multiple business objectives, such as satisfying customers and controlling costs. No company can be the best at everything, but with proper focus, design and management, companies can leverage analytics to reveal when customer and cost objectives complement or conflict with one another. They can then make better supply chain decisions.
For example, many businesses can calculate their perfect order percentage in a BI solution, but few can exploit supply chain analytics to drive their perfect order performance to the next level. That means going beyond on time, shipped complete to the underlying factors that drive customer behavior. What is the customer profile for each business segment? How about his or her preferred product mix, primary delivery channel and degree of engagement? Broader awareness is the first step to improving customer loyalty, brand reputation and forecasting ability, but it requires dramatically more discipline and data.
Assessing and controlling landed costs may seem simple on the surface. However, direct material, production and logistics costs tell only part of the story. Many companies also struggle to allocate overhead and transfer costs in a way that aligns with their strategic objectives.