I remember well when the first GPS systems appeared for use in cargo vehicles and automobiles. Based on satellite geolocation, they were not very precise and did not even dare to try to estimate the duration of the displacement. They only helped a lot in locating stolen vehicles and cargo, but they weren't very useful for travel management.
As road mapping and geolocation mechanisms were being perfected, vehicle location accuracy increased and GPS began to estimate travel time, still based on the permitted speed of the roads, which resulted in huge errors in forecasting. of the duration of the journey.
Companies that offered GPS systems to the market, over time, accumulated huge databases with records of trips made, which allowed to improve and more accurately estimate the location and travel time, which then began to be based on the “historical” speed of each road at each time of day. Accuracy improved a lot, but big errors still occurred, as it was impossible to anticipate accidents or any other traffic complications.
Finally, systems like Waze, based not only on geolocation, but also on user information, in a collaborative information network, is offering ever-increasing accuracy. Crossing historical speed data with real-time information from the vehicles in front of you, it is possible to estimate the journey time with incredible precision and offer alternative routes in real time.
Reflecting on this evolution, it is possible to draw a parallel with the concept of Demand Driven Supply Chain, which proposes a chain “pulled” by real demand, as opposed to “pushed” management, based on sales forecasts, which we find in most companies.
In an increasingly dynamic and uncertain environment, the predictive capacity based on historical sales is decreasing, which leads to large forecast errors and high costs with excess inventory and/or stockouts. Companies try to use a “GPS” to decide which path to choose, but the number of “accidents” and other problems leads to significant errors in these choices.
With the advent of CPFR tools, Big Data Analytics and the Internet of Things, we can anticipate demand planning based not only on history, but on real-time events and even on the anticipation of some events. It's a new paradigm for sure, but real! We are close to “WAZE” which will make the chain more Demand Driven!
Leonardo Julianelli
https://ilos.com.brExecutive Partner of ILOS. Graduated in Production Engineering from EE/UFRJ, Master in Business Administration from COPPEAD/UFRJ with extension at EM Lyon, France, and PhD in Production Engineering from COPPE/UFRJ. He has several articles published in periodicals and specialized magazines, being one of the authors of the book: “Sales Forecast: Organizational Processes & Qualitative and Quantitative Methods”. His research areas are: Demand Planning, Customer Service in the Logistics Process and Operations Planning. He worked for 8 years at CEL-COPPEAD / UFRJ, helping to organize the Logistics Teaching area. In consultancy, he carried out several projects in the logistics area, such as Diagnosis and Master Plan, Sales Forecast, Inventory Management, Demand Planning and Training Plan in companies such as Abbott, Braskem, Nitriflex, Petrobras, Promon IP, Vale, Natura, Jequití, among others. As a professor, he taught classes at companies such as Coca-Cola, Souza Cruz, ThyssenKrupp, Votorantim, Carrefour, Petrobras, Vale, Via Varejo, Furukawa, Monsanto, Natura, Ambev, BR Distribuidora, ABM, International Paper, Pepsico, Boehringer, Metrô Rio , Novelis, Sony, GVT, SBF, Silimed, Bettanin, Caramuru, CSN, Libra, Schlumberger, Schneider, FCA, Boticário, Usiminas, Bayer, ESG, Kimberly Clark and Transpetro, among others.
