July 30, 2013 By H. Donald Ratliff, Ph.D.Companies have made tremendous strides in automating transaction processing and data capture related to supply chain and logistics operations.While these innovations have reduced cost by reducing manual effort, their greatest impact is yet to come. They are the essential enablers for optimizing supply chain and logistics decisions.Supply chain and logistics optimization is neither easy nor cheap but it is the biggest opportunity for most companies to significantly reduce their cost and improve their performance. For most supply chain and logistics operations there is an opportunity to reduce cost by 10% to 40% by making better decisions.Over more than 30 years of developing and implementing supply chain and logistics technology, I have found the following 10 rules to be essential requirements for success:1. Objectives - must be quantified and measurableObjectives are the way that we specify what we want to accomplish with logistics optimization.

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This in turn is how the computer determines whether one solution is better than another and management determines if the optimization process is providing acceptable ROI.For example a delivery operation might define the objective to be - minimize the sum of the daily fixed cost of assets, the per mile cost of fuel and maintenance, and the per hour cost of labor. These costs are both quantified and reasonably easy to measure.2.

Models - must faithfully represent required logistics processesModels are the way we translate operational requirements and constraints into something the computer can understand and use in algorithms. For example, we need models to represent how shipments can be combined into loads for a truck. A very simple model such as the total weight/volume of the shipments will faithfully represent some loading requirements (e.g., bulk liquids).However, if we use a total weight/volume model for loading new cars on a car hauling truck, many of the loads that the computer thinks will fit cannot actually be loaded while loads that the computer discards because it thinks that they will not fit may actually fit and be better than the ones selected. Hence, in the latter case the model does not faithfully represent the loading process and the loads developed by an optimization algorithm are likely to be either infeasible or suboptimum.3. Variability - must be explicitly consideredVariability occurs in almost all supply chain and logistics processes (e.g., travel time varies from trip to trip, the number of items to be picked at a DC differs from day to day, the time to load a truck varies from truck to truck). Many of the models associated with supply chain and logistics optimization either assume that there is no variability or assume that using average values are adequate.This often leads to errors in model results and poor supply chain and logistics decisions. Ignoring variability is generally a receipt for failure.

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Logistics Best Practices

Variability must either be explicitly considered in the models or the supply chain and logistics practitioners must have the expertise to explicitly consider variability in interpreting model results.4. Data - must be accurate, timely, and comprehensiveData is what drives supply chain and logistics optimization. If the data is not accurate and/or it is not received in time to include it in the optimization, the resulting solutions will obviously be suspect. For optimization that focuses on execution, the data must also be comprehensive. For example, having the weight of each shipment is not sufficient if some loads are limited by volume of the truck.5. Integration - must support fully automated data transferIntegration is important because of the large amount of data that must be considered by logistics optimization.

For example optimizing deliveries from a warehouse to stores each day requires data regarding the orders, customers, trucks, drivers, and roads. Manually entering anything other than very minor amounts of data is both too time consuming and too error prone to support optimization.6. Delivery - must provide results in a form that facilitates execution, management and controlSolutions provided by supply chain and logistics optimization models are not successful unless people in the field can execute the optimized plan and management can be assured that the expected ROI is being achieved.The field requirements are for simple, unambiguous directions that are easily understood and executed. Management requires more aggregate information regarding the plans and their performance against key performance benchmarks over time and across facilities and assets. Web based interfaces are becoming the medium of choice for both management and execution.7. Algorithms - must intelligently exploit individual problem structureOne of the biggest differentiators among supply chain and logistics optimization technologies is the algorithms.

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