Logistics optimization is one of the biggest tasks that companies face on a daily basis. It is not easy, nor cheap, nor is it done in a short time, but requires a significant investment, extensive training and constant work and revision. And to know how to do it, we wanted to have an authorized voice. With the permission of the author, Donald Ratliff, we reproduce his article ’10 rules for the optimization of the supply chain and logistics’. Ratliff is professor emeritus and co-executive director of the Center for Logistics Research and Innovation at the Georgia Institute of Technology.
10 rules for supply chain and logistics optimization
“Businesses have made tremendous strides in automating transaction processing and capturing data related to supply chain and logistics operations. And while these innovations have reduced costs by reducing manual efforts, their greatest impact is yet to come. These processes are the essential enablers for supply chain optimization and logistics decisions. Supply chain and logistics optimization is neither easy nor cheap, but it is the best opportunity for most companies to significantly reduce their costs and improve their performance. For most supply chain and logistics operations there is an opportunity to reduce cost by 10-40% through better decision making. After more than 30 years developing and implementing supply chain and logistics technology, I have found that the following 10 rules are essential requirements for success.
1.- Objectives: they must be quantifiable and measurable
Goals are the way we specify what we want to achieve with logistics optimization. This, when the time comes, is the way in which the computer determines if one solution is better than another and the management of the company decides if the optimization process is giving an acceptable return on investment. For example, a distribution operation can define that the objective is to minimize the sum of the daily fixed cost of the assets, or the cost of fuel per kilometer and maintenance, or the cost of labor per hour. These costs are both quantifiable and reasonably easy to measure.
2.- Models: they must faithfully represent the required logistics processes
Models are the way we translate operational requirements and constraints into something a computer can understand and use through algorithms. For example, we need models to represent how shipments can be combined into truckloads. A very simple model such as total weight/volume of shipments will accurately represent some of the cargo requirements (for example, in bulk liquids). However, if we use a total weight/volume model to load new vehicles onto a car transporter, many of the loads that the computer thinks will fit cannot actually be loaded, while some of the loads that the computer discards because it thinks they they do not really fit, they do fit and are better than those selected. Therefore, in the latter case, the model does not faithfully represent the process and the loads developed by an optimization algorithm are probably unrealizable or suboptimal.
3.- Variability: must be expressly taken into account
Variability occurs in almost all logistics and supply chain processes (for example, transit time varies from trip to trip, the number of items that have to be picked up at a distribution center differs from day to day). and the loading time of a truck varies from truck to truck). Many of the models associated with supply chain and logistics optimization either assume there is no variability or assume that using mean values is appropriate. This often leads to errors in model output and poor supply chain and logistics decisions. Ignoring variability is often a recipe for failure. Variability needs to be: o Explicitly accounted for in models o Supply chain and logistics professionals need to be able to explicitly consider variability when interpreting model results.
4.- Data: they must be accurate, on time, and complete
Data is what drives supply chain and logistics optimization. If the data is not accurate and/or is not received in time to be included in the optimization, the resulting solutions will obviously be suspect. For process-focused optimization, the data must also be complete. For example, having the weight of each shipment is not enough if some of the loads are limited by the volume of the truck.
5.- Integration: it must allow a completely automated data transfer
Integration is important due to the large amount of data that must be taken into account by logistics automation. For example, optimizing daily store deliveries from a warehouse requires data about orders, customers, trucks, drivers, and roads. Manually entering anything but the least amount of data possible is both time-consuming and error-prone.
6.- Compliance: it must give results in a way that facilitates execution, management and control
The solutions provided by logistics and supply chain optimization models are not successful unless the people on the ground can execute the optimized plan and management can be confident that the expected return on investment (ROI) is achieved. is getting. The requirements for the job should be simple, unambiguous guidelines that are easily understood and executed. The directive requires more information about the plans and their performance against key performance indicators both over time and across different teams and facilities. Web interfaces are becoming the preferred tool for both management and operators.
7.- Algorithms: they must intelligently take advantage of the particular problems of the system
One of the biggest differentiators between logistics and supply chain optimization technologies are algorithms. An irrefutable fact regarding supply chain and logistics problems is that each one has particular characteristics that optimization algorithms must take advantage of to provide optimal solutions in reasonable times. Therefore, it is essential that (1) this specific structure is recognized and understood by the analyst who sets up the optimization system; and (2) that the optimization algorithms that are employed have the flexibility to allow them to be “tuned” to take advantage of this particular structure. Since logistics optimization problems have an enormous number of possible solutions (for example, for 40 groupage shipments there are 1,000,000,000,000 possible combinations), not taking advantage of the particular structure of the problems means either that the algorithm will take a solution based on some general rule or that the computation time will be extremely long.
8.- Personnel: they must have the domain and the necessary knowledge to work with the models, the data and the optimization engines
Optimization technology is very knowledge-intensive and it is unreasonable to expect it to work well over time without at least a few people who are able to understand it and ensure that the data and models are correct and that the technology is working the way it is intended. the one that was designed. You cannot expect a complex set of performance data, models, and algorithms to be operated and supported without considerable effort by people with the requisite knowledge, technique, and experience.
9.- Processes: they must support optimization and have the ability to continuously improve
Logistics and supply chain optimization requires a continuous and great effort. Changes in logistics problems are constantly going to occur. This change requires systematic monitoring of data, models, and performance algorithms to not only react to change, but initiate change when opportunity arises. Not being able to establish processes that support and continually improve logistics optimization consistently leads to poor use, or neglect, of optimization technology.
10: ROI: It must be demonstrable taking into account the total cost of technology, people and operations
The optimization of logistics and the supply chain is not free. It requires significant expenditures on technology and personnel. Demonstrating ROI (Return On Investment) requires two things: (1) an honest assessment of the total cost of the optimization and (2) a fair comparison of the solutions produced by the optimization against other alternatives on the market. .
There is a strong tendency to underestimate the recurring expense of using logistics optimization technologies. If the cost of the logistics technology decreases after the first year, it is likely that the quality of the solution will also decrease proportionally. It is rare that the annual recurring cost of efficiently using logistics optimization technologies is less than the initial cost of the technology. Determining the impact of logistics optimization requires (1) conducting market benchmarks against key performance indicators before implementing the technology, (2) comparing optimization results to market benchmarks, and (3) regularly conducting audits on optimization performance. Few companies today know how well their logistics optimization is actually working and how to determine their most important opportunities for improvement. This is both the biggest challenge and the biggest opportunity for the next generation of logistics and supply chain optimization technology.”
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