Insights Will Save Your Next Supply Chain Disruption

摘要： Global supply chain disruptions happen. What we learn from them matters greatly.

▲Credit: Pixabay(來源：informationweek.com)

Businesses have weathered a storm of recent high-profile supply chain disruptions, from a giant cargo ship choking access to the Suez Canal to a pandemic that upended business and life as we once knew them, for a year.

In the face of such upheaval, it is the rare company capable of turning chaos into competitive advantage that prevails. Thanks to the smart use of data and advanced analytics, some organizations are moving beyond basic visibility into inventory levels or customer order status to drive insights they can act on, helping to remediate supply chain complications.

Minimizing Revenue Risk

In March of 2020, a major multinational conglomerate was able to pinpoint exactly where they had material and manufacturing capacity despite the pandemic and subsequent lockdown. This insight allowed the organization to optimize production, ensuring intermediate inventory and finished goods were ready to ship the moment international borders and trade opened back up for business.

Another manufacturer was forced to change components and substitute materials, but they were able to spin data into intelligence to prioritize inventory and cater to high-yield customers.

Both companies substantially minimized their revenue risk related to the Suez Canal debacle.

Optimizing Supply Chain Strategies

While the above companies’ data and analytics initiatives are standouts, too many others are stuck spinning their wheels. Gartner estimates that between 60% to 85% of big data projects fail and it expects a paltry 20% of analytics insights to deliver business outcomes through 2022.

Leveraging data and analytics for supply chain optimization presents several challenges. To do this effectively goes beyond a historical accounting of what occurred to predictive analytics that identifies what-if scenarios and determines the best course of action.

The problem is the preponderance of companies still rely on spreadsheets to do supply chain planning -- 79% according to Ventana Research -- which is hardly predictive and severely limited for optimization strategies. Moreover, supply chain planning with spreadsheets makes for an error-prone process that is difficult to audit, not conducive to collaborative work, and reinforces data silos.

Ditching Data Silos

Data silos are one of the biggest hurdles to supply chain optimization. It is not that companies lack data -- many are drowning in it. It is that the data is heterogeneous, spread out across different systems that aren’t fully connected and don’t speak the same language. This lack of context creates gaps and translation hiccups that make it difficult to get the full picture for smarter data-decision making. According to NewVantage Partners’ 2021 Big Data and AI Executive Survey, only 24% of companies have created a data-driven organization with slightly less than half (48.5%) driving innovation using data and 41.2% competing on analytics.

There is much technical complexity associated with supply chain analytics. Many of these programs have been architected by data analytics experts and data scientists, not the actual business users who understand the challenges and know the questions that need to be asked and answered. The experts responsible for such programs may have moved on, causing them to languish. Without giving business users tools to unearth meaning behind masses of data, it is next to impossible to derive real value out of supply chain optimization tactics.

Future-Proofing the Supply Chain

What must change to make data and analytics an effective tool for optimizing supply chain operations and mitigating risk from the next-up disaster?

1. Empower decision makers.

Ensure the business context is communicated at the reporting stage. If a data scientist or analytics expert tasked with finding insights focuses on the wrong business problem or inaccurately interprets the data, it can have a compounding effect throughout the organization while preventing individual users from making informed decisions. Ignoring this problem, results in data science exercises that don’t deliver any measurable business value.

2. Take a holistic view.

Due to supply chain complexity, looking at one process or task at a time to figure out what’s going to happen next doesn’t tend to work well. A cross-functional, cross-process view is imperative to surfacing the necessary insights. Sales, distribution, manufacturing, and procurement departments must not work in isolation.

3. Look beyond the data.

Too many companies are obsessed with finding ways to grow their data reservoirs, but the real opportunity is not in bigger and better data but capitalizing on insights. Companies need to focus on the questions they want answered or the business problems solved and then backtrack with what data is needed to address the challenge.

The goal should be to achieve more informed decision making. In pursuit of this via an agile and optimized supply chain, the key to effective decision-making lies in insights.

轉貼自Source： informationweek.com

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