Home Insights Supply chain resilience starts with data
3D orange cubes and a night city

Supply chain resilience starts with data

Achieving supply chain resilience, especially since disruptions have become an inevitable part of business operations, is an overarching priority for any manufacturing organization today. Reaching this goal, however, is not straightforward, and is full of risks. To prevent these risks, manufacturers need to gather and analyze the right data, so that the organization can base the decisions on an objective balance of efforts and costs.

This article expands on the ‘Robust data foundation’ element of the modular supply chain resilience framework, and discusses:

  • Why resilience in supply chain management should start with data
  • What manufacturers should focus on to build a viable data foundation
  • What the key actions are to take to start building resilience

Resilience in supply chain starts with actionable data

Even though COVID-19 is no longer a public health emergency, the list of other disruptors is not getting any smaller. The most evident include geopolitical tensions, cyber threats, climate change, inflation, and the economic recession. On top of that, according to KMPG, 71% of companies that operate globally rate the cost of raw materials as the #1 threat to the supply chain in 2023.

A company’s ability to succeed depends on achieving cost-effective operational excellence that is guarded against operational risks. Manufacturers need to carefully rank priorities for different scenarios that may or may not occur. This effectively translates to an array of important decisions to make at a given point of time, including:

  • What short-term and quick decisions would bring the most benefit for gaining competitive advantage?
  • Is our inventory management and supplier network cost-effective for stable quarters?
  • How can we minimize excess supply while keeping inventory buffers and supplier contracts resilient enough to sustain disruptions in the short term?
  • Do we have insight into critical supplier systems and risk management?
  • Is our diversification of suppliers beyond tier one sufficient for mitigating or, at least, minimizing risk for future supply chain disruptions?

Apart from the precautions and stability measures, manufacturers that aim to be resilient companies, need to weigh lean manufacturing principles into the decision-making process. Moreover, the list of measures to take into account would span several pages and steadily become longer. All of these measures are important, however, it is important to note that not all of them are equally important at a particular point in time.

This brings us to the value of data. Data are the ultimate goldmine, which, when interpreted correctly, enable visibility into the end-to-end state of the system, environment, and workforce. The benefits of such insights to supply chain resilience, among other things, cannot be underestimated.

From data to intelligence: The role of analytics and advanced technologies

What is supply chain resilience and how can it be built into supply chains? Unsurprisingly, the answer varies from company to company, since the ecosystems and supply chains differ. A crucial aspect to understand in this context is that disruptions might, and often do, occur outside the directly controlled supply chain. That is why being informed about key third parties—including suppliers, logistics companies, and downstream consumers such as retailers or end customers—is crucial to have an objective view of the landscape.

To have a truly holistic view of what is going on in every segment of the value chain, companies need to:

  1. Accumulate data from each segment of their direct supply chain operation as well as from the supplier network.
  2. Consolidate the data into knowledge, and manage its volume, velocity, accuracy, and enrichment (in a secure and compliant way).
  3. Turn this knowledge into actionable insights with artificial intelligence (AI) and machine learning (ML).

Data intelligence provides the ability to be agile and proactive, thus enabling companies to alter their plans quickly and efficiently to mitigate disruptions. That is why laying a robust data foundation is the first step in the framework for achieving end-to-end supply chain resilience.

What areas to focus on to create data-business acumen

Data alone does not provide competitive advantage. That is, until data are applied for and analyzed to solve a specific business goal. In that case, it accelerates awareness and justifies decisions and actions that build sustainable value. For instance, targeted intelligence, derived from data, helps with risk and demand forecasting as well as prioritizing product and go-to-market initiatives. These abilities enable quick and beneficial pivots that, in turn, deliver both operational and strategic benefits and boost resilience in supply chain management.

So, in order for data to serve as a strong foundation to build supply chain resilience, organizations need to bring data from all silos together as an ideal scenario. If that is unfeasible, then the next best thing is implementing a data mesh that allows easy access across disparate data sources. These include internal, such as supply chain management apps, ERP, shop floor, control systems, and so on, and external, such as data from your suppliers, logistics partners, e-commerce systems, etc.

Which brings us to a conundrum: how to manage and actually get value from these disparate data? Our research shows that proper design, orchestration, collection, monitoring, and validation streamlines and increases productivity and, thus, increases the value data brings to the business.

Here are the key areas for manufacturers to assess and focus on to build a data foundation that powers a holistic supply chain resilience strategy.

Comprehensive data strategy

The value of data insight lies in an undistorted and focused view of what matters. That is why, to get to that point, a strategy of how to govern the data needs to be established. Manufacturers have to assess data models, ordering, and implementation, and have a view of all parts of the existing data ecosystem:

  1. What structured data sources are in use?
  2. What unstructured data are gathered?
  3. What technologies (open source, licensed, in-house) are used for data management?

This helps bringdisparate parts of the ecosystem together.

After gaining clarity on the existing state, manufacturers can assess and uncover any duplications, gaps, and opportunities for improvement. This helps identify and upgrade the enterprise-wide data strategy on the basis of comprehensive data.

Framework for data usage

With a strategy in mind, manufacturers can move to developing a framework that supports combining the data to perform a variety of tasks. Since resilience in supply chain comprises many underlying operations, the framework must bring together:

  • Data orchestration that connects data from the whole ecosystem.
  • Data management for internal and external applications and users across the supply chain (for example, in- and outbound partners intelligence).
  • Timely delivery.

Value capturing: Policies and people

To make sure the data helps achieve the supply chain resilience improvement goals, while complying with laws and regulations, manufacturers need to fast-track data management policies (DMP) and capabilities. The policies have to be clear and account for third-party access, local requirements for data usage and privacy, and others specific to the business guidelines.

To leverage the benefits of advanced technology investment to its fullest, upskilling teams andshifting to a DataOps model is key.

Key technical actions and their business impact

While the previous section focuses on what priorities should be laid out for data initiatives, this one dives into the technical initiatives needed to achieve them.

Distributed data architecture

To ingest, organize, and operationalize data that comes from a variety of sources, a distributed architecture is needed. Such architecture must incorporate:

  1. Collection and ingestion of raw data.
  2. Integration and transformation to make it coherent and actionable.
  3. Purpose-based storage and consumption for cost-effective usage.
  4. DMP-based centralized governance and security.
  5. Interoperability standardization across all links of the chain.
  6. Robust analytics.

Ideally, the implementation should be either a cloud-native analytics platform, or data mesh.

Power-up for growth and changes

Another technical aspect of how to improve supply chain resilience in the light of transportation and raw materials suppliers, is proactive preparation for diversification within the chain.

To account for needed changes to the global supply chain, invest in adapters and integrations designed for rapid inclusion of new suppliers and locations. Adding the proven and capability-based integrations also ensures the required data privacy requirements of new regions are respected.

Governance, automated

When talking about data across the supply chain, we are talking about big data. The raw data are stored and initially transformed in a data lake, then analyzed. For it to be truly valuable, however, it must be accurate and uncorrupted—trustworthy.

That is why proper data governance policiesand tools must be implemented. With well-designed policies, manufacturers can ensure automated data quality monitoring and anomaly detection, as well as data pipeline orchestration and management.

Conclusion: Data strategy to power a comprehensive resilience framework

Data, undoubtedly, can uncover competitive advantages, and impact supply chain performance via increasing resilience and innovation. Grid Dynamics partners with companies to bring the power of data to action. For example, see case studies of how a data observability solution helped to reduce speed-to-market and how an analytics platform speeds up anomaly detection and brings business value. As the Loss prevention with AI-powered IoT analytics platform on AWS case demonstrates, short-term investments into intelligent innovation can bring significant benefits to business. Especially, when they’re based on AI accelerators that are built to transform raw data into insights drastically faster.

This article starts the series that expands on a comprehensive supply chain resilience framework. The framework suggests a modular, mutually exclusive and collectively exhaustive approach to bringing resilience into the supply chain, so that manufacturers have full visibility and control over each step. The next article will discuss cyber security, because cyber risks get more complex as the operations evolve.

Get in touch

We'd love to hear from you. Please provide us with your preferred contact method so we can be sure to reach you.

    Supply chain resilience starts with data

    Thank you for getting in touch with Grid Dynamics!

    Your inquiry will be directed to the appropriate team and we will get back to you as soon as possible.

    check

    Something went wrong...

    There are possible difficulties with connection or other issues.
    Please try again after some time.

    Retry