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To be future-proof in the new normal volatility, supply chains need to address greater complexity quicker than ever before. Technology is a key enabler to solve this challenge, but companies need to select technology solutions that enable them to make faster decisions and break down silos, yet still be flexible enough to adapt to company-specific requirements and address changing business situations. Knowledge graph modeling can do just that and bring a decisive advantage to supply chains.

Volatility, complexity, and speed of decision-making are key features of the new normal.

Volatility is the new normal and requires speed in decision-making across strategic, planning, and execution horizons. At the same time, supply chains are becoming more complex than ever. The COVID crisis is adding to this, from new multi-sourcing approaches to increase resilience to the accelerated move to online. Further complexity is driven by an increasing number of partnerships and mergers.

For example, we’re observing an ongoing wave of partnerships and mergers in the auto industry in order to reach scale to enable the massive investment required to compete in emerging markets for Electric and Connected Vehicles. However, to achieve the expected scale benefits (e.g., combining purchasing power, manufacturing, and R&D capacity), the auto industry will need to integrate supply chain management across formerly independent supply chains that are now serving more brands and platforms.

Speed and quality of supply chain planning in increasingly complex value chains is critical to building future-proof supply chains with a competitive edge. Speed, quality, and complexity are competing needs and companies tend to prioritize and compromise. However, future-proofing a supply chain requires superiority in all three areas that goes beyond improving current supply chain planning processes and systems.

Technology enables fast decision making in a complex and volatile environment

To achieve the required decision-making speed, companies are starting to rely on capabilities like machine learning to quickly turn big data into actionable insights and prescriptive actions that enable automated planning and execution processes resulting in supply chain planning man and machine workflows to run at higher clock speed and support faster, more collaborative decision making.

A prerequisite to this is building an effective digital representation of the increasingly complex end-to-end value chains to provide relevant data and knowledge with required granularity and latency to these higher clock speed supply chain planning workflows.

Increasingly complex value chains lead to increasing complexity of digital representations. This is exacerbated by ever increasing data volumes and complexity (more external data, disparate data sources, IoT, etc.).

Not all technologies are equal in managing complexity

A critical success factor in building a future-proof supply chain is the ability to represent value chain knowledge and information in a way it can be used for fast and collaborative decision-making seamlessly across different planning horizons. If technology solutions translate increasing supply chain complexity into a similar or even disproportional increase of data complexity and volumes to be processed, then this puts speed and flexibility at risk.

Traditional data storage and digital representation paradigms (RDMS​, OLAP cubes) often store data at the lowest level, causing artificial data explosion with increasing supply chain complexity. This results in scalability and speed challenges and eventually leads to simplified models to keep a satisfying level of performance, but compromises quality as this model can’t use today’s data abundance to the fullest to obtain actionable insights.

Enter KGs – representing Value Chain complexity without exploding complexity of VC representation.

Knowledge Graphs represent knowledge not just by aggregating data of multiple underlying transactional systems, (for example ERP systems of pre-merger companies), but also by considering relationships across multiple entities (products, parts, brands, product platforms, markets, customers, plants, suppliers, etc.).

This provides the right level of complexity for each horizon and functional stakeholder.  This fundamentally breaks down functional data silos because it provides a ‘function-specific view of one truth’, yet is consistent with the real-time propagation of each change. Moreover, it can do this at speed and is flexible and extensible to adjust to changing business rules and business configurations, new data, etc.

KGs break the functional data silos by providing ‘function relevant views’ that are consistent.

KG-based supply chain planning can be particularly impactful where different functional views of product increase the risk of functional silos, which is the case for most configurable products.

For example in the automotive industry, a car has different functional descriptions: customers buy sales features (like models and options) so this is sales-speak, whereas supply-speak would be about parts, manufacturing capacities, and supplier capacities to produce these parts. These different views are typically linked with a set of usage statements describing relationships between these terms.

This is useful, as selling features such as park pilot or in-car entertainment do not require the sales representative to know how these are connected to different wiring harnesses, are installed, or the capacities for hundreds of other different parts related to this and many other features. Similarly, the manufacturing plant does not need to know which channel or market a car is sold, as its focus is to build on time and meet quality standards. While both sides need their own view—without redundant details—these views must be consistent.

Consider the current semiconductor shortage requiring companies to find optimal capacity-compliant production planning. Optimal with respect to business objectives which can be dynamic and differentiated. For example, objectives like margin, revenue, market share, serving strategic customers can have different weights for brands in the same group. A decision that is taken in sales-speak (i.e., what to build for what customers) may be subject to restrictions in supply-speak (i.e., available capacities).

Many features use semiconductors. For example, a park pilot feature uses a camera, which includes chips that are shared with displays used in entertainment features. Making optimal use of available semiconductor supply requires considering all relevant relationships.

Knowledge Graphs can connect data at different levels (e.g., considering revenues for all models in a certain market using a certain part connected to a specific supplier tool capacity, supporting rapidly building scenarios, and identify incompliance and enable prescriptive analytics proposing supply and demand actions) and show these to each function in their own language. Sales can be restricted to sales-speak while the supply chain works on a capacity level and the Knowledge Graph’s real-time propagation will keep them synchronized to enable collaboration.

Today, companies can take actions like closing assembly plants for days or weeks or strip certain models of specific features, or replacing them with analog solutions. These actions can be decided quickly without much analytics support. However, these are rather blunt measures that help manage the shortage but are unlikely to achieve business optimal use of a scarce semiconductor supply.

Right view also means a relevant level of granularity for each function and horizon.

This is enabled by telescopic capability of KG-based supply chain planning.

Let’s continue with the semiconductor shortage to illustrate this. Driven by volatility across industries, automotive requirements collapsed and then rebounded earlier than expected. Working from home also drove increased requirements for consumer electronics. An immediate solution is not in sight and demand will further increase driven by moves to electrical vehicles, infrastructure electrification, 5G, etc.

This makes capacity planning a challenge. Particularly for high investment and long lead time foundry capacity based on consolidated demand trends for the many products and industries sharing requirements for this capacity. Requiring consistency with other value chain steps like semiconductor assembly and package planning working on a more granular level (e.g., allocation of supply between automotive customers).

With its ‘telescoping capability’, Knowledge Graphs-based supply chain planning systems provide the ability for semiconductor players to have one single integrated plan across horizons for their supply planning, enabling companies to take decisions at the right level depending on the horizon but with consistency in the decisions. They will also allow companies to leverage relevant external information on trends in diverse end-customer requirements to create and evaluate different scenarios at speed.

Technology goes beyond KGs and future-proofing supply chains goes beyond Technology

Knowledge Graphs modeling of supply chains allows to quickly get from data to insights. However, increasing full organizational clock speed also requires speed in getting from insights to decision-making and then from decisions to actions. To achieve this, standalone Knowledge Graphs are not enough and should be embedded in the core of organizational capability leveraging technology, people, and process:

  • On the technology side, to ensure speed and consistency of decision making, Knowledge Graphs modeling should be the backbone of a planning platform integrating end-to-end processes from supply chain design (e.g., Capacity planning) over financial and commercial planning to S&OP and S&OE (e.g., Control Tower)
  • On the people and process side, new man and machine workflows will require people to change roles and reskill on all levels of the organization. Organizational formats will have to break functional organization silos to fully benefit from breaking knowledge silos.

There is no one-size-fits-all approach and each company needs its own discovery journey which should be a close partnership between business (providing an understanding of problems, processes, and people) and technology experts (knowing what technology can enable).

Connections between o9's knowledge models across the supply chain

o9 Solutions uses their proprietary Enterprise Knowledge Graph (EKG) that provides the richness of modeling and computations to power next-generation business applications.

The o9 EKG allows for:

  • Multi-dimensional models at appropriate levels of detail.
  • Fully linked models to rapidly propagate all changes.
  • Built-in support for lightning-fast aggregation and disaggregation along any number of hierarchies.
  • Ability to incorporate necessary structured and unstructured data to drive all analytics and decisions.

About the authors


Dirk Lembregts

Held senior supply chain leadership roles in multiple industries including for General Motors, Philips, and Marks&Spencer. He is now an independent consultant focussed on leveraging digital technology to future-proof supply chains.

Philippe wolff

Philippe Wolff

Director Industry Solutions at o9, bringing supply chain planning expertise to o9 Industrial Manufacturing sector in EMEA

:o9 Solutions

:o9 Solutions is the premier AI-powered platform for driving digital transformations of integrated planning and operations capabilities. Whether it is driving demand, aligning demand and supply, or managing P&L, any process can be made faster and smarter with :o9’s AI-powered digital solutions. Bringing together technology innovations—such as graph-based enterprise modelling, big data analytics, advanced algorithms for scenario planning, collaborative portals, easy-to-use interfaces and cloud-based delivery—into one platform.