dynamic data

Why manufacturers need dynamic data flows and connectivity

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 largely akin to gambling—it can quickly send an organization into a downward spiral. The opposite of gut-feel decision-making is methodical, impartial decision-making informed by hard, diverse data streams. Not only can data-informed decision-making help you stay on course, but it also can give you the confidence to make consequential pivots and changes to your operations. Often, organizations need to make fundamental changes before they can successfully transition into new markets, significantly boost profit margins, and achieve new economies of scale.


Social listening is limited in its effectiveness

When manufacturers think about leveraging customer data to drive planning and forecasting capabilities, they often think of social listening. Social listening refers to gathering data online about what customers are saying, and it can take many forms, including customer reviews, trending topics on social media, and more traditional sources like market analysis and news coverage. While it’s important for manufacturers to always be listening to all these sources, social listening cannot on its own offer a comprehensive picture of market trends. Social listening is passive and can offer only a partial picture; it creates tunnel vision. What organizations need is to proactively engage in dynamic data gathering, where every data point feeds into sophisticated, AI-powered predictive algorithms and performance tracking systems that help manufacturers make strategic, data-informed decisions. Social listening should also include listening to your business partners because sometimes the most pressing demand is with distributors and dealers and not necessarily the manufacturer. 


You need to pick up on market trends earlier

As manufacturing becomes increasingly global and as competitors worm their way into previously untouched niches of the manufacturing marketplace, manufacturers can no longer afford to deprioritize tracking market trends. Today, if a manufacturer doesn’t react fast enough, they risk losing customers to the competition, and they risk becoming irrelevant. Dynamic data workflows are critical to keeping manufacturers in the know about how customer expectations and preferences are evolving, how supply chains are being reshaped, and what the competition is doing differently. For example, if there’s a sudden spike in the number of Google searches for “immune health,” a manufacturer in the health supplements industry absolutely needs to be aware of this fact—and pivot and adjust how it labels and markets its products to drive customers to products that promote immune health. In this scenario, if Manufacturer A hasn’t picked up on the spike in Google searches, it’s a safe bet Manufacturers B and C have—and are already positioning themselves to capture more market share from Manufacturer A. 


You need to be synthesizing data from multiple disparate sources

Modern data analytics platforms don’t pull data from a single source; they pull from many sources. And significantly, these multiple sources of data are automatically and continuously synthesized to produce far more insightful business intelligence than if they’re analyzed in siloes. The newest technologies take multiple disparate streams of data and—with the help of AI—convert them into a single set of synthesized business insights. For example, a single platform is capable today of synthesizing data from product reviews, competitor websites, business announcements, supply chain changes, and customer satisfaction surveys on the voice of the customer. Significantly, this technology utilizes natural language processing, which enables raw, cacophonous data points to be converted into standardized formats for analysis.

Every manufacturer deserves to be armed with best-in-class tools and technologies for forecasting and planning. Dynamic data flows are at the heart of this strategy, enabling manufacturers to remain competitive in a fast-changing industry. With dynamic data flows and interconnectivity, manufacturers can end reliance on gut-feel decision-making, rely less on social listening as a predominant source of business intelligence, pick up on market trends earlier and more reliably than the competition, and effectively synthesize data from disparate sources.

One of the most important investments any manufacturer can make is embracing a data-driven strategy powered by Salesforce. When you feed enterprise data into their Salesforce ecosystem, you immediately begin generating reliable, insightful, actionable intelligence that benefits your employees, your entire ecosystem of partners, and ultimately your end customers. To learn more about how Simplus uses Salesforce to create dynamic data flows for manufacturers, please reach out to us today.



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