- Client :Common Sense Robotics
- Category :Agent-Based, Discrete Events
- Project Url :
- Date :April 27, 2019
The client’s business can be summarized as the transportation and management of fresh food from Distribution Centers (DC) to Fulfillment Centers (FC) as a B2B setup and from the Fulfillment Center to final customers as a B2C setup with the objetive of providing fresh food to customers in less than one hour guaranteed.
There were considerable requirements of expansion due to an increasing demand, which required the client to select new Distribution Centers and new Fulfillment Centers in a particular city to cover that demand while being able to provide the customers with the ordered fresh food in less than one hour. This also meant that there was a potential of expansion to new cities and new countries.
The client wanted to have a very flexible playground in which it would be possible to define the positions of the different FCs and DCs, the location of the potential customers, the distribution strategies, the demand distributions, the demand regions, etc. And use that information to analyze the consequences and implications of those decisions in terms of customer service, distribution costs, resource utilization, etc.
For Supply Chain and distribution problems, AnyLogic is one of the best tools out there with a powerful set of features such as GIS routing, easy configuration for flexible simulation models, easy to save and load complex scenarios on runtime and a wide set of analysis tools to easily generate conclusions based on the set of strategies defined.
A simulation model was developed using AnyLogic that would take all the client’s requirements into consideration with the possibility to manage Fulfillment Centers, Distribution Centers and demand areas either before the simulation or during runtime. The client was able to change the structure of the network at any given point to see the impact of adding, editing or removing an entity of the supply chain system. With a satisfactory setting, the client was able to save the Scenario and load it again in the future for additional testing.
Figure – Simulation User Interface on Runtime
Of course, it is important to add all the necessary analysis tools that would allow the client to understand the implications of changes or new network structures on the key performance indicators (KPIs) of the company. These KPIs were visualized in chunks of 4 hours, 1 day and 1 week to understand the short, mid and long term consequences of strategic decisions on the supply chain dynamics and the distribution characteristics.
Figure – 4 hours analysis vs 1 week analysis on service level indicators
Some of the strategic decisions would change the dynamics of how a final customer is reached. For that, expert-system algorithms were used, which would change manually or automatically depending on the user decision.
Figure – two of the Order Management strategies for the expert-system algorithm
The outcome of the model is a full functional Software that allows for flexible decision making in a simulation setting, having a toolbox of strategies, parameter definition, algorithms definitions, automation and other flexible features to account for all the possible scenarios that can come to mind to achieve the financial and service level goals committed by the client to its customers. The user of this Software can easily visualize a full palette of graphical and text data showing all the dimensions of the business with clear information on KPI implications based on initial or runtime changes in the structure of the supply chain. The Software was used to define real expansion decisions along with the best action plan for the expansion setting (leasing new locations, modifying the number of delivery vehicles, segmenting delivery destination zones, improving the processes within a fulfillment center, etc.).
- Industry: Supply Chain
- Model: Agent-Based + Discrete-Events
- Duration: 2 months