Manufacturing organizations have traditionally relied on conventional forecasting methods to track market trends—essentially, a process that boils down to collecting data and trying to read the tea leaves. Although these legacy methods have served manufacturers reasonably well for generations, 81 percent of manufacturers say that established methods for planning and forecasting demand are no longer working, according to 2020 Salesforce industry research. There’s too much uncertainty and risk inherent in these conventional methods, and too much important data is being siloed within a single team or group within the organization—if the data is even being gathered at all. What manufacturers are increasingly recognizing is that they need dynamic data flows and dynamic data connectivity to improve their business intelligence. Under a dynamic data operating model, manufacturers are continuously collecting, analyzing, and sharing multiple types of data—and ultimately converting it into actionable, insightful information that drives decision-making and performance optimization across the organization.
When manufacturers make the decision to invest in dynamic data workflows, there’s not a magic-bullet solution. Rather, manufacturers must commit to making strategic investments in a number of areas to incrementally build capacity to collect and use data effectively. On the demand side, they must begin gathering multiple types of data, including on customer behaviors, purchase history, and demographics. On the supply side, they must begin collecting even more types of data, including on inventory, equipment, production, transportation, and staffing. Finally, manufacturers must build the capacity to reliably analyze and extract actionable market insights from this stream of data.
Dynamic data workflows enable manufacturers to detect changes, trends, and risks—reliably and within the timeframes that manufacturers need to take appropriate actions in response. Let’s explore four key reasons every manufacturer needs dynamic data flows and dynamic data connectivity to optimize their core planning and forecasting capabilities:
You cannot rely on gut-feel decision-making
Many manufacturers’ planning and forecasting capabilities are built at least in part on gut instinct. Although manufacturers almost universally collect some customer and operational data, they still infuse the data with their own intuitions and personal experiences—and don’t think twice about how much risk and uncertainty this practice is introducing into their decision-making processes. Gut-feel decision-making can be effective in some circumstances, of course, but it’s…
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