Some 40 years ago, supply chains were domestic or local, and they presented a pretty simple process. The globalization paired with the technological boom added new moving parts to supply chains, making them complex. Ultimately, big data as a user-friendly and significant asset arrived to change supply chains once and for all. But what was the most valuable thing that big data brought to the industry? The answer is — an ability to create a wealth of knowledge to be shared.
The ‘Data as an asset’ report powered by KPMG revealed that 17% of responding companies claim their effectiveness in maximizing value from available data. A tiny 18% of surveyed organizations state they successfully sustain an enterprise-wide strategy of data management. 67% of CEOs think their businesses could enhance the understanding of their clients. As per the Harvey Nash infographic, 91% of surveyed CIOs believe they could do much more to build customer trust.
According to the Mordor Intelligence report, the supply chain BDA market value was worth $3.55 billion in 2020 and is expected to reach $9.28 by 2026. Given these projections, businesses involved in the supply chain must be well aware of the advantages of big data analytics and areas of implementation.
The key challenges of supply chain
I will outline five major challenges companies face in their path to sustainability and growth. While these problems existed before COVID-19 broke out, they are dramatically exacerbated by the pandemic.
The lack of agility. Difficulty in differentiating customer offerings on-demand due to lack of flexibility in manufacturing and supply chain.
Weak design of the ecosystem. Failure to identify the relevant partners to innovate, create and deliver value on demand.
Insufficient architecture. While the framework must ensure seamless collaboration between multiple stakeholders, some tech architectures fail to foster co-creation and innovation.
Lack of visibility. The inability to provide real-time visibility across the entire journey hinders the establishment of trusted relationships.
Big data: the capabilities
The current uncertainty has pushed businesses to find new agile approaches, reinvent themselves and get on their feet again. Whatever the circumstances are, a company either adapts or ceases its existence. Over time, artificial intelligence has become more accessible. In the context of supply chain and logistics, the industry is network-based. Therefore, its origin opens doors to various applications of AI as well as the opportunities of scaling it.
As far as the conventional SCM methods are concerned, every element in the chain faced barriers: it was limited to its own corporate tracking. The approach demanded manual work concerning the status updates and communication across departments, etc. Regardless of how swiftly the message travels from one office to another, a human factor generated a certain gap which was inevitable. Things could get much worse when big data consolidation takes place between the companies. One mistake and the whole dataset or several of them might go to waste.
The main capability of tech is that it consolidates and glues together the supply chain data, provides the framework for data maintenance, storage, and then turns it into specific optimization measures. When implementing cloud-based analytics, a company can unite and base metrics on a real-time updated source of truth.
Once a company gets a hold of in-depth analytical tools, the available insights optimize the supply chain in many ways:
- The problems and gaps become visible thanks to the real-time alerting system.
- Partners with numerous issues are detected.
- An organization can eliminate materials that are prone to systematic quality challenges.
- A business rationalizes the operations by removing the ones that appear unnecessary.
Let’s take Amazon as an example. One of the biggest names in the e-commerce world, Amazon uses big data analytics to address client demand. The company takes into account buying patterns, preferences, and search queries. Based on this quintessential data, Amazon makes highly relevant recommendations. By providing a personalized recommendation system, the corporation managed to boost its sales. The initiative, known as One-Day Delivery, was rooted in tight cooperation with manufacturers, resulting in cost reduction between 10 and 40%.
Now let’s proceed to certain processes where big data bears advantages.
Areas of implementation
Maintaining high-quality standards. Many industries implying time sensitivity (food, chemicals, agriculture, etc.), are obliged to ensure due monitoring and control of their goods. One tiny fluctuation in temperature can damage the product and turn it into waste. It is common knowledge that around 30% of products can get spoiled long before they arrive at their destination. This question is specifically relevant at times of Covid when vaccines are being actively transported. Variable data can be of help when it comes to potential weather-related issues.
Inventory management. The ability to make sales predictions and define trends relies greatly on predictive analytics. For instance, the pandemic could hardly be predicted by an algorithm or a person. However, having BDA systems in place could lend a helping hand in dealing with supply and demand. When demand growth comes with delivery delays, a company must be able to scale its production. By combining past trends with predictive analytics, the technology sheds light on different expectations. Inventory managers acquire information on how to cut costs, balance the stocks and reduce waste.
“We’re focused on better forecasting, better replenishment, and working with our suppliers to optimize that side of the supply chain. Right now (due to COVID-19), we’re testing those systems to their limit because we’re seeing demand that we’ve never seen before. We’re seeing customer buying patterns that we’ve never seen before, but that’s giving us better forecast insight than we’ve ever had. If we’re to have something again like this, we’ll be able to utilize that type of data to better forecast what we can do to get in front of it.” — General Manager/Senior Director of Supply Chain, Walmart.
Around-the-clock tracking and order execution. Real-time monitoring is crucial for a company’s productivity and for super customer experience. Constantly updated information helps cut costs by tweaking routes, schedules, goods location. As the package covers its long path, special sensors capture data on it. BDA systems can optimize routes and cut gasoline expenditure and as a result, help a company to cut costs significantly.
Machinery support. Due to improper maintenance, setbacks, and worn-off machines, a manufacturer, for instance, can suffer billion-dollar losses. The combo of big data and solutions based on the internet of things can provide timely alerts if anything goes wrong and predict issues before they happen. Again, big data acts out as a means to cut costs by reducing repair expenses and maintaining effective production.
To sum up, data per se is not a business strategy; it is the enabler. Before diving into transformation and adopting data initiatives, a business must have a well-articulated value proposition encompassing a vast variety of things — current challenges, gaps, and conjectures big data can tackle. It takes to have a clear-cut picture of supplementary capabilities necessary apart from data sourcing. It takes to perceive data as an asset. More than that, embracing a data-driven approach is a fundamental cultural change that implies robust management and open communication.
