Intelligent enterprise fueling the supply chain of the future

An intelligent enterprise will require the following components—an insights and decision platform, a digital organization, and a digital operating model.

1. An insights and decision platform

To make intelligent decisions in large, complex supply chains, operators should understand how their actions anywhere will impact the enterprise everywhere. Disruptions in one area could mean delayed or canceled orders in another. Accurate decisions that impact the production, sourcing, or routing of products must be based on data that is timely, accurate, pertinent, and holistic. Many of the business rules and algorithms will likely lead to process automation.

Process automation enables managers to make decisions based on the rapid analysis of potential decision points and how those will impact the entire organization. To do this, the insights and decision platform must include the technology and tools necessary to access both internal and external data sources, captured in real time and organized into a single data model, made available to supply chain operators through a rules-driven, cloud-based layer that spans the enterprise. AI/ML capabilities will subsequently be used to conduct in-the-moment analysis. The findings can then be used not only to respond to supply chain exceptions, but also to guide the identification and automation of manual processes and non–value-add decisions.

2. A digital organization

To bring the organizational model up to speed with the capabilities of advanced technologies, the organization needs to operate from one integrated data layer, pulling data into one rationalized dataset, accessible to the entire enterprise. AI and other sophisticated analytics solutions, meanwhile, will expedite problem-solving. The result: What would have previously taken days to perform manually within one department will now be executed in a matter of minutes using data from across the organization.

For example, the aforementioned apparel manufacturer accelerated its go-to-market process by utilizing 3D applications to render virtual representations of planned products. Previously, it had used a process that required shipping product prototypes back and forth for approval, which was slow and cumbersome. The new 3D prototypes can be pilot-tested internally and with customers. The resulting digital product development process will get products to market faster, while also speeding product life cycles in markets where consumer preferences change or require regional adjustment.

Liberated from legacy systems, organizations can construct a governance, technology, data, and operational model to support an agile business. The company mission becomes customer-centric, and the business is restructured to provide the resources to nimbly pursue that mission.

Moreover, organizations can be structured to prevent and address service failures. Execution-focused roles in business functions would deliver customer-centric solutions using systems and analytics from cross-functional pods. Blending functional roles, such as planning and execution, can improve collaboration and responsiveness when facing near-term market fluctuations and disruptions. For example, positions such as a Master Planner (historically focused on monthly/weekly supply plan in an APS system), an Inventory Analyst (focused on inventory management), and a Replenisher (focused on fulfilling near-term demand in an ERP or execution system) will be combined into one role. The consolidated function will utilize the same data, business rules, and policies to respond to demand changes and iteratively replan with the latest input from the market. Reducing the number of functional roles and systems involved increases the organization’s agility and decision-making speed.

3. A digital operating model

The digital operating model will become operational through a series of codified business rules that will support the new customer-centric, rapid-response approach. The goal of the operating model is to help identify and address the root cause of problems. In effect, this model assumes that supply chain disruption is an eventuality, rather than an exception.  

The design of the operating model will assign decision-making authority based on information and analytics. Where formerly the governance model supported legacy system–dependent business silos, management can now replace those with policies, procedures, and incentives based on the integrated business planning process (i.e., the enterprise business planning process).

The technology layer provides a common, integrated data repository (single version of the truth) that will be accessed by roles across the enterprise to help ensure enterprisewide impact. In this model, performance measurement and incentives will be associated through a common, holistic set of metrics. For example, the aforementioned global apparel manufacturer is transitioning its assortment planning, allocation, and retail planning to a digital platform. When completed, this platform will help provide a single view of end-to-end supply chain operations across its global network.

As the company transforms, so does the work. New digital technologies will help automate mundane, repetitive, transactional, and non–value-added work (e.g., manually manipulating spreadsheets to connect datasets, or extracting data from siloed legacy systems). New roles will be focused on capability (e.g., data science economists responsible for weighing strategic and financial trade-offs and managing the business policies and logic in the digital layer) rather than functional expertise. The work will now be more strategic since it is focused on data analysis, running scenario modeling, and debating the merits of various strategic choices amplified by new digital tools.

Moving forward

Responding deliberately to disruptions requires interconnectedness of all enterprisewide variables. These include commercial strategies, production capabilities, financial impacts, inventory and supplier visibility, and order management capabilities (as the interface to the customer). Unfortunately, no single system does this today. Meanwhile, the speed to evaluate and make decisions is not sufficient to respond to ever-changing customer needs. The existing operational model thus does not allow for necessary transformation.

But like any transformation, this one too starts by acknowledging that the status quo is no longer tenable. Then, objectives that will get the organization to cultivate a new customer-centric, extended enterprise–aware culture can be outlined. What kind of organization do you want to be? What is your identity? Who should be driving the final decisions?

To answer these questions, companies must figure out how human talent and digital technologies can work together (augmentation). Machines will do more of the work in the future, which can yield returns in the short run (reduction in head count and fewer legacy systems to manage) and provide significant benefits into the future (improved decision-making driving enterprise priorities, improved agility/resilience).

Once organizations have set their objectives, the next step will be driving toward a single system to collect data from all functions across the enterprise and consolidating it into one data source (the single version of truth). This will require a system layer that connects internal and external data sources, likely stored in the cloud.

Once the single data layer has been established, organizations will be in position to use advanced digital technologies to optimize decision-making. AI/ML capabilities can automate low-impact or time-consuming decision-making by learning from the impact of previous decisions, while simultaneously refining the algorithms. Staffing can be reorganized to leverage analytics that will focus the enterprise on customer-centricity rather than functional silos.

Clearly, organizational change on this scale is not going to be easy. Nor will it happen overnight. There’s no single right answer for how to reach the intelligent-enterprise end state. Many organizations have embarked on this transformative journey, but few, if any, have mastered it. In fact, most leaders are currently focused on laying the foundations.

For example, the aforementioned global retailer has already launched its intelligent-enterprise efforts by developing a new vision for better decision-making in an uncertain environment. The first step in that vision is modeling the decisions made across the entire enterprise—from the factory floor to the store. That goal is close to realization, as the retailer has made data connections across the many functions of the enterprise, which will provide a holistic visibility into the supply chain. This resulting data will be used to build decision models, which will ultimately be automated.

The next step is evolving the company culture in alignment with its technological and operational capabilities. Governance models will need to establish clearly defined rules and roles to facilitate in-time decision-making. Skill sets will need to be upgraded, and operations managers will need to perform data-driven analysis for customer-centric decisions (with enterprisewide impact) from a single version of the truth while building and analyzing simulated decision scenarios.

In closing, it is increasingly clear that for supply chains globally more black swan events and disruption lay ahead.⁵ This accentuates the need for organizations the world over to transform into intelligent enterprises. Many companies with whom we work in the retail and consumer goods manufacturing spaces have already begun laying foundational elements. The race is on to improve operations, deliver better services, and offer improved value with agile and resilient supply chains.