Accenture on Operations: From ones and zeros to supply chain heroes

Today the lifeblood of business is data. How companies leverage data is what separates leaders from laggards.

This is especially true for supply chain and logistics functions. The key is knowing what data you need, and how it should be organized, stored and shared to transform it to drive insights and power the supply chain growth engine.

Resilience capabilities, growth strategies, cost optimization, and customer centricity in the supply chain all require the right approach to data—and when coupled with the right advanced capabilities, such as Artificial Intelligence (AI) and Machine Learning (ML), will provide the needed visibility and insights to achieve and exceed business objectives.

This is how supply chain and logistics teams can go from ones and zeros to supply chain heroes.

Data is one of the biggest challenges that executives face in building intelligent customer-centric supply chains. Many companies underestimate the value of a data strategy, and the legacy programs they’re still using are not fully capable of supporting a truly data-driven enterprise that drives timely insights.

Companies need to first understand what data they need to capture, how to ingest, transform and store it and then determine which advanced technologies can be utilized to pivot from descriptive (what happened) insights to predictive and
prescriptive (what will happen) insights.

The lack of collaboration between the business and IT on data standardization and integrity limits a company’s ability to drive meaningful reporting and analytics to support beyond the day-to-day tasks.

For example, many shippers today still collect transactional data at an aggregate level, lumping in total shipment costs with fuel surcharges, accessorial, and linehaul costs as one value.

As a result, they lack the discrete data elements which are critical to drive insights and make more informed decisions. To build the necessary data driven foundation, the business needs to partner with IT to make sure the right data is captured at the right time.

Get your head in the Cloud

Decisions on how to ingest, store and secure the data are crucial to setting up the right framework for success. Data needs to be captured in real-time—and analog/on-prem solutions create a latency issue that hampers critical decisioning.

The Cloud is the cure for such issues—creating the webbing that supports all the systems and data across all aspects and nodes of the complex, partner-based value chain—delivering value well beyond just infrastructure savings.

The Cloud is perhaps the most critical key enabler of innovation today, as it provides the foundation for affordable, unrestricted access to cutting edge technologies that might normally be out of a company’s reach. Accenture research shows that nearly all (93%) of supply chain executives expect 50% or more of their business to be in the Cloud in the next three years.

The true value of the Cloud goes beyond gaining economies of scale and cost savings through sharing public servers. Companies benefit from integrated systems across the supply chain function that unlocks visibility to the value chain resulting in faster, more-informed decision making.

For example, the Cloud helped a global auto manufacturer address supply chain challenges such as lack of data integrity across systems, non-integrated processes, and strenuous visibility of planning by enabling an architecture that supported analytics models with advanced algorithms.

From competent to cognitive

After the foundation and data strategy is in place, companies need to quickly identify those advanced technologies they need to utilize to drive the intelligent supply chain agenda. The most common ones—AI and ML—are all the rage today for good reason; they work. Data is the backbone of AI and ML.

Because AI feeds off the data it receives, it’s crucial to ensure that data quality and collection has been set up accurately. The data needs to be relevant to the business and in a structure and format that lends itself to AI models to accurately determine patterns, predict outcomes, and automate actions. With the right data, AI powers improved decision-making, drives efficiencies and accelerates business growth.

ML can not only sense patterns undetectable to the human brain, but also allows systems to infinitely learn and adapt to new information—feeding it back into the insights engine—constantly re-evaluating the emerging patterns and delivering real time insights to drive better decisioning for supply chain teams.

Take the example of a global fashion manufacturer who deployed an advanced forecasting solution leveraging machine learning so planners could determine specific requirements of local markets and customer purchasing behavior to make informed adjustments to their forecasts.

To boost the predictive modeling of forecasts, the solution connected the company’s warehouses and factories with distributors’ data, also looping in relevant external demand signals such as weather, photos, and trends—all in real time. This new unified view of demand forecasting improved forecast accuracy, overall management of the entire supply chain, and on-shelf availability of key products.

As a result, costs decreased 10% by optimizing and automating production scheduling for suppliers and finished goods flow to distribution centers.

In essence, ML and AI make the possibility and potential of predictive and prescriptive analytics real, creating a real-world crystal ball for supply chain.

Getting started

Developing an intelligent Cloud-based supply chain requires ownership from leadership and a strategic vision that guides the business and IT to follow a structured, disciplined approach. It’s imperative for companies to realize every journey is different, and implementing a Cloud-based supply chain is not a “lift-and-shift” activity. To get started, companies should follow basic principles to realize their speed-to-value through the following:

• Define your overall data and Cloud strategy, mission statement and desired future state outcomes to achieve at the onset.
• Identify key challenges faced within your supply chain and ideate on the data required to increase employee efficiency, leverage technology by reducing manual work arounds, and streamline processes.
• Co-create with the business and IT a roadmap identifying core capabilities required to unlock value in the Cloud over a specific time period.
• Engage partners with resources who possess relevant industry and technology knowledge to guide the journey while avoiding common pitfalls and risks.

It’s clear that the pace of digital transformation in the supply chain is accelerating, with the Cloud, AI and ML being among the top technologies currently being scaled up. With the right strategy in place, leaders will be able to fully leverage the full potential of data and put into place effective use of new technologies like AI and machine learning to increase resiliency, build customer-centricity and drive new value