Between record-breaking inflation, interest rates and gas prices, many are concerned about the negative consequences of a recession. Further, this fluctuating economy has been a major contributor to a plethora of hiccups in the global supply chain, resulting in delayed orders, canceled shipments and product shortages.
As a result of these uncertainties and existing supply chain issues, many companies are struggling to keep up. Even worse, PWC found that only 23% of companies have the necessary digital skills to meet future goals for their supply chain.
Despite the bleak outlook, companies still have an opportunity to get their supply chains into shape and emerge stronger by making data-driven decisions that provide the best outcomes for the business.
Solving for the analytics gap
Historically, only aggressive adopters of analytics have benefitted from downturns. Not only do they have the technology in place, but they also have the right talent to execute as they are more likely to take advantage of the slow hiring market and an increased supply of skilled data workers.
However, it’s not realistic to buy expertise off the shelf to accelerate analytics maturity and expect to immediately reach parity with aggressive adopters. Between data sources and business outcomes lies a series of steps in a process that typically requires highly skilled data and analytics workers: a literal analytics supply chain. Few purpose-built visual software tools reflecting this process are available. This leaves most organizations to deal with some combination of suboptimal spreadsheets, manual coding, and a chasm between the business and IT too large to accommodate the speed and specificity required to make better decisions.
There are cloud-based software solutions tailor made for the conditions that facilitate faster and better execution of data-driven work. These are analytics automation tools that abstract complexity so that both everyday data workers and those in the business can independently develop their own paths to better decisions.
Using analytics to adapt to change
Analytics automation enables workers with varying skills to address any element of the analytics lifecycle. In practice, this allows everyone to spend less time building and maintaining one-off analytics by capturing work once and automating it in an auditable and governed manner. This approach also supports analytic asset reusability, which speeds time to value for successive use cases and supports the upskilling of others.
Analytics automation unlocks the potential of influences hiding in external data such as weather forecasts, economic indicators, geopolitical events and more, to discern important changes in customer demand and potential disruptions in supply lines. More timely insight based on these signals leads to an experience that customers recognize as in-tune with their needs during lingering uncertainty.
With this in-depth information, companies can better discern essential changes in customer demand, disruptions in supply chain management, and any gaps within their own systems. As a result, they can better navigate turbulent economies and make more informed decisions based on what’s happening in the real world.
Having this well-analyzed additional data saves time and resources as it helps business leaders make quick, informed improvements to day-to-day decisions, ultimately driving business growth and success. When supply chain decisions are made more quickly and more workers are privy to the data that informs these decisions, companies can survive and even thrive during turbulent economic times.
Future-proofing the supply chain
Most organizations don’t have time or resources to completely re-engineer supply chains. However, any organization can implement automated analytics and improve their supply chains by starting small. The first step is to identify the low hanging fruit by focusing on the highest value and most quickly executed use cases. There are innumerable decisions across the supply chain just like this characterized by:
· Time sapping manual efforts requiring oversight and maintenance by overburdened staff.
· Too much time spent assembling the right data to support the targeted decision versus charting out a path to additional use cases and business value.
· Rearward looking, historical reporting versus looking ahead to predict future outcomes and develop confident prescriptions to realize them.
· Limited analytics involvement by business stakeholders with the most domain expertise but depend on the insights to do their jobs.
· Latency, or too much time, between the creation of an insight and ability to inform the best decision possible.
· Inwardly focused thinking, from the data sources to the way decisions impact the end customer. Outside-in thinking needs to prevail.
While businesses can operate under these circumstances, the probability of success in this new world goes up appreciably when they are addressed proactively in a structured manner. When analytics automation is applied relative to an organization’s priorities and analytics maturity, they can make better data-driven decisions that result in better innovation and improved processes. And for many, the value of better supply chain decisions made more quickly – by more workers – makes analytics automation essential to survive and emerge stronger regardless of the state of the economy.