When travelling, I often run into a frustrating, yet common, problem: delays due to weather conditions. Often, the bad weather is not at the airports I am traveling to or from; but, instead, where the inbound aircraft is sitting. FlightAware, a handy and useful app for any frequent traveler, keeps track of the aircraft in our skies. According to FlightAware, in the past year, there were an average of 9,728 planes carrying 1,270,406 people in the sky at any given time. These flights traveled between more than 17,000 commercial airports around the world. The U.S. Department of Transportation’s Bureau of Transportation Statistics reports that 76 percent of all flights are on time. Considering the complexity of this multi-echelon distribution network, that sounds pretty impressive — unless you are waiting for one of the other 24 percent.
There is a lot of coordination in air traffic control aiming to reduce congestion, synchronize connecting flights, ensure safety and improve on-time performance. But imagine if that weren’t the case. What if operations at each of the 17,000 airports were optimized independently from what was happening at other airports? I would venture to guess that air safety would deteriorate, congestion and hold-times would increase, and on-time flights would significantly decrease.
Now consider your multi-echelon distribution network. Do you optimize inventory at each location separately, or are you adjusting total inventory across the entire network, taking into consideration the interdependencies among stocking locations? Single-echelon inventory optimization fine-tunes the inventory stocked at a single location or distribution echelon independently of others. While this does provide the ability to streamline operations, maintain service levels and free up working capital, in today’s hypercompetitive world, the benefits of optimizing inventory at each facility independently will not provide a sustainable competitive advantage.
On the other hand, multi-echelon inventory optimization (MEIO) significantly extends the potential benefits achieved from the traditional method. MEIO enhances the mix of stock keeping units (SKUs) at each location and time period, optimizes buffer locations of SKUs throughout the network and the resulting commitments among echelons. MEIO also can improve the mix of different types of inventory, as well as how much and where each should be held by different companies in an extended supply chain, in order to most effectively serve the customer. As a supply chain management professional, you need to understand the difference between single-echelon inventory optimization and MEIO in order to determine which is best for your company.
To do this, consider the following strategies from Nucleus Research. Its “IO Value Matrix 2018” reports that there are a few core solution capabilities to focus on when considering different inventory optimization solutions. These factors include
- ease of use to help visualize and communicate how the optimization engines are generating their recommendations
- transparency of calculations to help users understand why a change is being recommended
- the option for cloud deployment to speed implementation and improve return on investment
- a single data model across a company’s planning and optimization solutions
- machine learning capabilities to automate model switching and account for variability in actual demand across the supply chain
- robust what-if scenario engines to enable supply chain planners to run simulations and determine the volumetric and financial effect of changes in inventory policies.
Finally, ask yourself two key questions: What type of inventory optimization capability does your company need? And what short- and long-term goals will make it possible to optimize your end-to-end inventory?
Henry Canitz is director of product marketing and business development at Logility. He has more than 25 years of experience building high-performance supply chains. Canitz may be contacted at firstname.lastname@example.org.