Article written by Toolsgroup
Faced with the reality of the 4.0 Industry that we are currently experiencing, the field of logistics is also facing, in parallel, the new challenges involved in the process of digitalizing the industrial sector. Thus, 4.0 Logistics have to go through the transformation of conventional processes, methodologies and systems to new approaches that allow us to overcome the current limits and adapt to much more complex supply chains.
Big Data management, increased volatility and variability in demand, omnichannel... Given this current situation, it is not surprising that companies are increasingly aware of the importance of using technology to obtain supply chains that are increasingly agile, profitable, sustainable, automated, flexible, reactive and able to adapt to highly complex environments.
In this sense, technologies derived from Artificial Intelligence such as Machine Learning or Advanced Analytics techniques applied to the supply chain will allow us to optimize supply chains, making much finer demand forecasts (managing large volumes of data and taking into account unpredictable external factors so far as the impact of Social Media, among others), reducing security and storage stocks, and predicting and reacting much more efficiently to changes in demand.
But the question that assails us now is, where do we start? Which areas can we explore first and which ones will have larger impact on our supply chain management? Big Data management will undoubtedly be one of the fields that will have the greatest impact on our business network. In this sense, the Artificial Intelligence variations will allow us to automatically treat the own and external data and make better decisions in our supply chain. The data will then become a high-value asset for companies, which must invest to turn them into a source of profitability, orienting their organizations towards the information economy.
One of the technologies used for the Big Data management is Demand Sensing technology. Broadly speaking, we can define Demand Sensing as the technology that, using detailed short-term demand data, improves forecasts in the "near future". This new trend arises, then, to respond to the tremendous current complexity of demand, get ahead of it and enable us to make sound decisions in our supply chain. For this, Demand Sensing technology imports a large variety of data on daily demand, immediately detects changes in demand by comparing these data with a detailed statistical pattern and evaluates the statistical significance of this change. In summary: by analyzing real demand data in short periods, we can execute automatic adjustments in the short-term forecast using a probabilistic model recognition and predictive analysis system, and quickly identify and react to unexpected changes in customer demand. As an example of the use of this type of technology, Costa Coffee implemented a replacement and demand planning system that collects data from the point of sale of its 7,000 coffee machines every 15 minutes to forecast demand, optimize inventory and generate nigh of refueling proposals. The company managed to improve its logistics indexes in a spectacular way: a 20% reduction in stock and 30% reduction in annual logistics operating costs, among others.
The use of these new technologies will enable us to automate the supply chain to become much more efficient and productive. The trend towards automation is seen in some cases as a threat, but far from being the case, the reality is that the use of an appropriate automation technology can result in great competitive advantages. But the real added value of this automation is that, then, the organizations and all their members will be in an unbeatable position to make transformational changes in their supply chain.