With the emergence of new digital technologies such as Mobility, Big Data, Cloud, Business Intelligence/AI, the IoT, etc., new and more abundant sources of data are available to improve forecasting and demand management. Companies that embrace digital and transform their demand management processes will gain significant competitive advantage. While the traditional statistical forecast may serve a baseline, demand analyses must extend across functions (silos) and include many different sources of causal data. -Rich Sherman, Senior Fellow, Supply Chain Centre of Excellence, Tata Consultancy Services
For the past two or even three decades, the immutable truths have been that the forecast is always wrong and the intra- and inter-organizational “silos” are to blame for it. In the Connected Age, everyone and everything is becoming connected. We are collecting data in real time about everyone and everything. Sensors and monitoring devices that automate the collection of data are being placed at every point in the demand-supply network that data can be collected. We are even injecting monitoring devices into our bodies. For years, I’ve spoken that the perfect logistics system would be just when I want a beer, it would be beamed to me like Star Trek. In the not too distant future, the breweries will sense my cravings and deliver the beer before I even know I want one! The moment of truth and the path to purchase are becoming Demand 4.0.
These new sources of data and real-time communications, supplemented with BI and machine learning make transforming to Digital Demand Management a competitive imperative. If you’re not developing and piloting new demand management processes today, it may be too late. Machine learning for predictive and prescriptive analytics takes time and new histories of new sources of data. It takes sophisticated algorithms and harmonized master data to connect the different sources of data that are used to create and fulfill demand within the organizational silos and across the demand-supply network. If you’re not building those connections and building the data foundation required for machine-learning-based applications, you may never be able to catch up!
You can’t break down the silos, but you can connect them. Think of statistical process control. We install sensors and monitors along every critical control point in the manufacturing process flow. We determine an optimal flow and specify a range of upper and lower control limits of variability that we can tolerate. As the manufacturing process flows, based on patterns and historical flow data, we can predict when the process is likely to move into an out-of-tolerance state, operators are immediately alerted to bring the process back into tolerance. Based on collecting a history of what corrective actions were taken we can provide prescriptive analytics to accelerate the operator’s decisions and actions.
With digital data increasingly available throughout the flow of the demand-supply network, companies are developing smart digital supply network strategies to apply horizontal process control techniques and applications that enable more accurate planning and scheduling to support efficient execution by vertically oriented functions and organizations to enable agile and rapid response to variability in the demand-supply network.
In 2018, expect to see market leaders deploying new digital demand management processes and technologies that align and enable vertical functional silos with horizontal process flows that create new demand and fulfill demand. Competition in the 21st Century is time – time for machines to learn to support decisions eliminating time delay and amplification. Agile companies will rapidly respond to demand variability reducing their operating costs by more than 50 percent of their competitors.
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