Tough Problems Solved

A supply-chain logistics network sounds like a simple thing — a system developed to move goods from supplier to customer.

Dig a little deeper, and you’ll find that an increasingly complex relationship between an increasing number of variables has made the process more difficult than ever. Inefficient asset utilization, seasonal spikes in demand, market fluctuations and complex international regulations are just a few of the problems plaguing manufacturers and third-party logistics companies. Too many supply-chain logistics networks are hampered by redundant and inefficient network facilities, high inventory-storage costs and low truck-load rates. An ever-changing marketplace — one frequented by mergers, acquisitions, expansions into new territories, offshore manufacturing initiatives and new product introductions — further muddles the delivery of goods.

These challenges have prompted IBM Research and IBM Global Business Services (GBS) to take a fresh look at ways to optimize the supply chain.

A group of supply-chain researchers at the IBM China Research Lab have developed an advanced asset to better manage supply-chain logistics. It’s called the Supply-chain Network Optimization Workbench (SNOW). Built using these researchers’ expertise in network design, inventory optimization and transportation planning, SNOW is an integrated workbench providing end-to-end decision-making support. SNOW employs cutting-edge optimization, simulation and Geographic Information System technologies to streamline large supply-chain logistics networks.

Taking into account real-life constraints, SNOW helps develop better supply chains in part by evaluating existing supply-chain strategies and performing tactical transportation analyses. In doing so, SNOW assists in determining facility locations and ways to optimize the supply portfolio. It helps decision makers evaluate supply chains in terms of global tax exposure, strategic inventory allocation and other factors. By taking into account all transportation, operation, regulatory and inventory variables, SNOW can help develop a virtually seamless end-to-end supply-chain solution.

Only recently developed, SNOW is already proving itself in real-life scenarios. For example, one of the largest third-party logistics companies in China contracted with GBS to revamp its supply-chain networks. These networks include logistics management, transportation, warehouse management and regional distribution operations. A recent merger had left the company with redundant and inefficient network facilities, high inventory costs, high transportation costs and low truck-load rates. Working with this company, GBS delivered an optimized network solution, one that took into account logistics strategy design, network configuration and inventory optimization. Deploying SNOW, the GBS team was able to check the consistency of the company’s imported data, forecast the client’s demand, optimize its logistics network, perform “what if” analyses and recommend an optimal logistics network strategy based on quantitative analysis. As a result, the company expects to improve customer service, remove operational bottlenecks and save cash. (The proposed optimal network configuration projects a reduction of 23.4 percent in distribution costs, among other savings.)

The Supply-chain Network Optimization Workbench is delivered through IBM Global Business Services. For more information on putting this powerful supply-chain tool to work for you, contact IBM Research Services.