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Organizations seeking to make their supply chains more efficient must weigh many factors that affect their business processes. In a global market environment, companies have many choices in supply strategy that can help deliver the final product more quickly or at lower cost, giving them a chance to garner a greater market share. A single change in vendors can have multiple impacts—on costs, delivery times, product quality and operational risk, to mention just a few. Inefficient asset utilization, seasonal spikes in demand, market fluctuations and complex international regulations are just few of the problems that companies face in an ever-changing global marketplace.

Streamlining the supply chain

To help clients avoid the potential pitfalls of supply chain restructuring, IBM Research has developed two tools that use advanced analytics to simulate complex supply chain changes and predict their effects. The IBM Supply Chain Process Modeler (SCPM) is an integrated workbench for end-to-end supply chain transformation. Focusing on the business process, SCPM can help provide comprehensive supply chain transformation solutions at the strategic, tactical and operational levels efficiently and accurately.

The IBM Supply Chain Network Optimization Workbench (SNOW) is designed to help in supply chain logistics, from raw materials procurement to manufacturing to finished goods delivery. SNOW helps organizations develop streamlined supply chains in part by evaluating existing supply-chain strategies and performing tactical transportation analyses using cutting-edge optimization, simulation and Geographic Information System technologies. SNOW’s analytics capability assists in determining facility locations and 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.

In one instance, a large third-party logistics firm in China sought assistance from GBS in revamping its supply-chain networks, which included 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. Using SNOW, GBS delivered an optimized network solution that addressed logistics strategy design, network configuration and inventory optimization. The SNOW tool was instrumental in checking the consistency of the company’s imported data, forecasting the client’s demand, optimizing its logistics network, performing “what if” analyses and recommending an optimal logistics network strategy. The streamlined supply chain strategy helped improve customer service, remove operational bottlenecks and save cash.

Automating supply chain management

To improve efficiency, companies increasingly are increasingly turning to software applications that assist in automating routine decision making. One such technology is the IBM Watson Implosion Technology (WIT), a software tool designed to solve a class of resource-constrained production planning problems. Among the variables WIT uses for production planning are market supply and demand, production capacity and raw materials availability. WIT uses fast and scalable algorithms for optimizing the allocation of resources throughout the network.

The IBM Supply Chain Capability Engine (SCE) is an application built on top of WIT, providing a Central Material Requirements Planning capability that “explodes” a demand statement to all levels within the supply chain when inventory or capacity is low. SCE has been used in IBM's internal Supply/Demand Planning processes for more than 10 years to add function for multi-site and multi-tiered enterprise planning, and is easily customizable to address specific problem domains. SCE has been deployed internally in IBM to address various parts of the planning process, as well as different needs of the product brands.

Improving inventory management

When a retailer runs out of a hot-selling item, the missed sales opportunities can result in short-term revenue loss and long-term erosion of brand loyalty and potentially market share. Research shows that despite efforts over the past years to improve processes and technology, out-of-stocks occur at a rate of approximately 10 to 15 percent for promotional items and 8 to 10 percent for regular merchandise.

To help retailers leverage the multiple data sources available for inventory management, researchers at IBM developed the IBM Demand Driven Replenishment (DDR) offering. DDR builds on the success of IBM’s Continuous Replenishment Service, using downstream demand data like retail point-of-sale (POS) quantities to help identify and correct supply chain and store execution problems at the store and SKU levels—not only when problems are imminent, but proactively in time to avert them. DDR improves supply chain efficiency by identifying current and predicted out-of-stock and overstock situations across all echelons of the supply chain and providing automated, actionable recommendations to the entity best able to mitigate the supply chain execution issues—be it the product manufacturer, a third-party merchandiser or the retailer. In addition, DDR has helped improve the effectiveness of sales promotions and new product introductions and rollouts. Working together with GBS, retail clients have used DDR to help drive sales improvements of 5 to 10 percent.

Planning an efficient disaster response

When disaster strikes, getting food, water and supplies to victims efficiently is paramount. The relief effort supply chain differs from a typical industry supply chain in that there is often a huge surge of demand with very short notice, the transportation conditions are less-than-ideal and the penalty for delay can be catastrophic. To help government agencies better address crisis situations, IBM Research has developed the IBM Disaster Response Simulation Modeler (i-DRuM)—a modeling framework for disaster response that provides a simulation model of disaster response that factors in relief supply chain, distribution operations at point of distribution (POD), dynamics of demand and progression of disaster.

Using i-DRuM, agencies can better match the volume of relief supplies, such as water and food, blankets, generators, tarps and medical supplies, to the number of people in need. The tool also factors in how many workers are needed and what machinery is required for distribution and medical care. The demand model helps predict when and where people will need relief the most and the disaster model describes the arrival and progression of the disaster with respect to time and location. By modeling and simulating disaster preparedness and response, i-DRuM can evaluate a wide range of disaster scenarios and help devise an effective plan for protecting people in the affected area.

Reducing risk and planning for uncertainty

Disasters are not the only risk factors that organizations face in a global economy. Issues such as raw materials and parts shortages, operational or transportation problems, labor strikes, currency fluctuations, and cross-border delays and closures can all cause significant headaches for manufacturers and retailers. In addition, there are many areas of emerging concern, such as reputational integrity and regulatory compliance. The IBM Supply Chain Risk Management (SCRM) solution can help companies identify and mitigate risk as part of their overall supply chain strategy.

In its own operations, IBM used SCRM for IBM System x® Server supply chain planning, using the SCRM risk quantification framework to model both catastrophic and everyday risks and develop a risk mitigation strategy based on likelihood and financial implications. SCRM uses an approach based on Bayesian network modeling—a graphical model that represents a set of variables and their probabilities—to identify and quantify risks and map them out for visual interpretation. The combined map of business processes provided a blueprint for a simulation model to compute the effects of disruptions and failures on supply chain performance, and helped IBM anticipate and rank potential problems and develop a game plan for meeting each challenge. The potential benefits include the elimination of unexpected costs, reduced disruptions and shorter time to recovery. And in today’s complex supply chain ecosystem, avoiding or at least confronting risks can help strengthen competitive advantage and even financial longevity.

More information on IBM resources for smarter supply chains

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