Editor’s note: This article is the first in a five-part series.

Market volatility in the face of inflation, tariffs, and other geopolitical and economic shocks has disrupted global supply chains, compressed margins and exposed the limits of traditional analytics systems. The increase in these events in both frequency and magnitude has caused distributors to respond with urgency. However, few act with structured discipline.

This five-part article series outlines how leading distributors are transforming uncertainty into decisive action through highly customized analytics frameworks. These frameworks integrate transactional data, SKU-level dynamics, supplier conditions¹ and customer-specific details into a rules-based financial model built for real-world complexity.

By institutionalizing a repeatable process within this framework, distributors improve decision-making precision and move with speed and confidence to protect and grow margins in an increasingly unpredictable market.

To drive the highest adoption of these decisions, we examine how to present model outputs in a prescriptive, contextualized, actionable and collaborative format.²

We illustrate our case for a purpose-built analytics framework with a distributor who was able to preserve over $1 billion in margin during the inflation disruptions of 2020-2022.

While technology is essential, success ultimately depends on people. Applying this framework requires technically fluent, creative problem-solvers who can design, build and adapt systems. We refer to these professionals as data engineers — individuals who translate complexity into clarity that is efficiently actioned.

This series challenges distribution leaders to rethink analytics — not as dashboards or rearview reports, but as a purpose-built capability that empowers decisive, forward-looking action. Becoming the disruptor in a disruptive market requires more than tools; it demands intentional investment in the frameworks, processes and people that transform data into competitive advantage. The analytics imperative isn’t about surviving the next disruption. It’s about building the muscle to lead through whatever comes next.³

The Analytics Imperative — When Survival Demands an Analytics Revolution

Why Tariff Volatility Exposes Fatal Flaws in Traditional Distribution Analytics

Disruptions like tariffs expose fundamental weaknesses in traditional distribution analytics. This section introduces the concept of modeling “doing nothing vs. doing something” and helps distributors assess whether the investment in purpose-built analytics is justified by their scale and complexity.

“In disruptive markets, there’s no neutral ground. You’re either shaping the market or reacting to it.”

Why the Current Analytics Framework Fails

In today’s environment, distributors face relentless pressure from unpredictable cost variability. Inflation, supply chain constraints, regulatory shifts, and tariffs all contribute to compressed margins and expose the limitations of traditional analytics and pricing systems. These systems were not designed to handle complex, high-frequency changes across thousands of SKUs.

Tariffs, for example, come in multiple forms, such as ad valorem (percentage-based) and specific duties (fixed per unit). Each type affects price execution differently. To make matters worse, suppliers communicate tariff changes inconsistently. Sometimes the cost is embedded in the product price. Other times, it appears as a global surcharge. In many cases, impact estimates arrive before any official cost notification, creating confusion and delays in decision-making.

Tariffs are one example of a broader pattern of disruptive cost variability, alongside inflation, supplier shortages and shifting trade regulations. Most distributors lack the systems and processes to capture, standardize and respond to these changes in a structured way. Manual workarounds might work temporarily, but they don’t scale and don’t deliver accuracy or speed when they’re needed most.

Pricing is where this challenge hits hardest. It is a core function of distribution and the most critical driver of margin. Yet, when disruptions occur, pricing often enters crisis mode. Inaccurate or delayed inputs lead to poor pricing decisions, unnecessary friction with suppliers and customers and ultimately, significant financial loss.

In an ideal world, suppliers would provide SKU-level cost changes in standardized, digital formats. But that’s not reality. Like distributors, suppliers are often constrained by legacy systems and inconsistent communication methods. The result is fragmented data scattered across emails, spreadsheets, PDFs, and ERP notes — none of which offer clarity or precision.

This lack of clarity causes real harm. Pricing teams are forced to make high-stakes decisions with incomplete or imprecise data. That creates internal confusion, customer frustration and margin erosion.

Distributors today face two primary challenges:

1. Data Volume and Complexity — The pace and unpredictability of change, and how those changes impact specific customers, places overwhelming stress on systems and people.
2. Inadequate Frameworks — Most existing systems cannot produce reliable, repeatable, and actionable outputs that align stakeholders across the enterprise.

Solving this problem requires more than strategy. What’s needed is a tactical and scalable framework: one that models disruption in detail, reflects real-world complexity and produces clear, actionable outputs for finance, sales and operations.

Distributors that rely on generalizations or reactive responses risk losing margin, market share, and credibility.⁴ Those that succeed are the ones who invest in purpose-built analytics frameworks, empower cross-functional teams and turn uncertainty into a competitive advantage.

This article series is a guide for those leaders — those ready to move beyond survival mode and reimagine analytics as a foundation for disruption.

Modeling the Impact of Doing Nothing and Something

When faced with disruption, the priority is to understand the financial consequences of inaction.

One of the primary uses of predictive analytics is to simulate the impact of inaction. This includes estimating margin erosion at the customer and SKU level if prices remain unchanged and the distributor absorbs the full cost increase. This understanding is essential to inform better decisions.

Many distributors overlook this step or approach it informally, estimating potential margin erosion with high-level, rough calculations. Imprecision introduces financial risk.

An effective financial impact model starts with isolating SKU-level cost changes to predict impact to margin.

Prediction alone is not enough.

The model should generate output that guides clear next steps and supports better outcomes.⁵ It must be rules-driven,⁶ repeatable⁷ and able to produce insights⁸ within hours. It should minimize⁹ manual steps (to minimize errors) and the methodology must be clearly understood by teams across pricing, category management, finance and sales.

The purpose is not just to react quickly. It is to support informed decisions. Let’s look at the questions that should be asked from the perspective of tariffs:

• Should we pass through the full increase of the product(s) affected as price increases to the customer?¹⁰ If so, how do we scale that decision across all customers and all affected SKUs, given the volume and customer agreement variation?¹¹
• Where can we blend margin protection with customer retention strategies?
• How can we blend our preferred supplier partner strategy as an alternative to proposed price recommendations to incumbent SKUs affected by tariffs?¹²
• Is there an opportunity to isolate key customers to seek temporary deviations to cost from the supplier for high impacted SKUs?¹³
• Which customers or products require tailored handling based on risk, value, or strategic importance?¹⁴

To make confident decisions in the face of disruption, you must first understand the cost of doing nothing. A structured risk assessment model provides that clarity and becomes the foundation for a more powerful prescriptive model.

For example, unlike traditional cost updates that occur annually or semi-annually, tariffs can change pricing fundamentals overnight. Teams must be prepared to run simulations frequently, and at scale. The ability to assess the precise details of risk quickly and accurately should be non-negotiable.

Response Timeline (using Tariffs as an example)

Here is a timeline your team should aspire to when a tariff notification is received:

• Impact simulation (within hours): Quantify risk if customer pricing is not adjusted
• Initial pricing model (within 24 hours): Present rules-based customer/SKU price scenarios for leadership review¹⁵
• Finalize rules, generating prescriptive output (within days): This output will be the specific (prescriptive) change recommendations, some of which will be updated automatically, others will need to be displayed in an actionable interface¹⁶

These timeframes are critical for aligning stakeholders and creating the appropriate sense of urgency across the organization.

In summary, a financial “do nothing” model transforms instinct into insight. It sets the stage for confident action by quantifying risk, aligning decision-makers, and exposing gaps in execution capacity. The most important outcome of the model is its prescriptive output, powered by structured, discrete data attribution.¹⁷

Is Return Worth the Investment?

A purpose-built analytics framework at the scale described in this paper is not suitable for every distributor. The complexity of such a system must be justified by the size of the opportunity and the risk of inaction.

To assess whether this investment makes sense, start with scale.

Consider an organization with 200 sales representatives, each managing 100 customer accounts, with every customer affected by 10 tariff-sensitive SKUs. That results in over 200,000 discrete pricing decisions that must be reviewed, communicated and justified.

Relying on a manual or semi-structured process invites inconsistency, confusion, and costly errors. The margin impact of even a few hundred mispriced items or customer miscommunications can be significant. And the organizational cost of eroded trust, sales hesitation and backtracking is even greater.

This is where the return on investment becomes clear.

For large distributors, the volume of activity justifies investment. The ability to model risk, attribute data, generate scenarios and present prescriptive output at scale is not optional. It is essential.

For smaller distributors, the answer may be different. If your company has fewer sales reps, fewer customers, or a product mix with limited exposure to external cost volatility, then the value of a large-scale pricing engine may be limited. In these cases, the investment should be proportionate to the complexity of the business. Smaller-scale frameworks, powered by structured logic but lighter in infrastructure, may deliver sufficient ROI.

When evaluating scenarios like tariffs or other cost shocks, here are a few considerations to keep in mind:

• If most of your products are sourced domestically or exempt from external cost drivers, this framework may not be necessary.
• If your pricing changes are infrequent or handled centrally, a manual approach may be sufficient.
• Still, if you manage pricing at scale, under pressure, with distributed decision-makers, a structured pricing system becomes essential.

The deciding factor is not cost alone. It is exposure. The more complexity your pricing environment carries, the greater your risk. The greater your risk, the more valuable a system becomes that brings speed, accuracy, transparency and control.

The Final Word

Pricing has moved from the back office to the front lines of competitive strategy. It is a strategic lever. And when mismanaged, it becomes a margin liability. This investment pays off when it transforms pricing from a vulnerability into a competitive advantage.

An often-overlooked benefit of a purpose-built analytics framework is that it expands how leaders think about using data across the business. That capability will provide enormous long-term benefits to the company.

The Distributors’ Analytics Imperative Series

• Part 1: When Survival Demands an Analytics Revolution
• Part 2: Attribution Breakthrough – Building on Bedrock
• Part 3: The Analytics Prescriptive Engine — From Static Reports to Strategic Recommendations
• Part 4: The Human Algorithm: Why Great Analytics Fail Without Great People
• Part 5: How to Turn Analytics into an Organizational Muscle

Footnotes

1. Such as robust SKU cross-referencing. ↩︎
2. Adoption is dependent on how to get consumers of information to easily and confidently act. ↩︎
3. We are at a pivotal moment: Your competitors will either figure this out, or not. And you will do the same. The “figuring-it-out-or-not” will determine winners and losers. Winners will preserve margin and continue to invest during times of uncertainty. And as history has shown, the winners will experience order of magnitude growth compared to the losers. (See: McKinsey & Co: How Distributors Can Build Momentum in a Slowdown, Sep 20, 2022) ↩︎
4. See McKinsey & Company, The Commercial Response to Cost Volatility: How to Protect Margins Against Inflation and Tariffs (June 2019). The report cites an example of a distributor that implemented broad-based price increases in response to rising costs. Within three months, the company lost double-digit market share, and the leadership team “limped away from the attempted increase with margins worse than ever.” ↩︎
5. This is the essence of prescriptive analytics. ↩︎
6. Rules must be parameterized, not hard-coded.  Too often financial models morph into an abyss of hidden conditions and calculations.  These conditions and calculations should be visible, tunable. ↩︎
7. You’ve now seen this word “repeated” many times. Core to success is creating a framework that is scalable. And that can only be achieved when the process to generate output is automated and hence, easily repeatable. ↩︎
8. This is the beginning of prescriptive analytics – recommendations that will help the company influence the future to their benefit. ↩︎
9. This is where most distributors stall during their journey to build a reliable model. Our advice is this: Don’t build the system to manage every situation. Build the model to automate the “80%” and manage the “20%.” Too often the personnel working on this effort to build this new analytics framework become obsessed with perfection. And while that is an ideal trait for this role (obsessing over the details), it often is a limiting factor to making progress. ↩︎
10. In a CNN article (Feb 27, 2025), Sean Kerins, president and CEO of Arrow Electronics, said that the $27.9 billion distributor has experience dealing with tariffs imposed during the first Trump administration: “We know what this looks like. Muscle memories are in place, and our posture will be to pass those [increased costs] on to customers as transparently as possible. Not to make money, but just to recover the uplift.” ↩︎
11. Caution: If you plan to give general direction to your sales team and hope they will successfully pass on the increase to their customers, prepare for devastating margin erosion. ↩︎
12. In parallel with financial modeling, companies should evaluate product substitution strategies. Identifying tariff-exempt alternate products early can limit disruption, preserve margin and provide differentiation with your relationship with both suppliers and customers. ↩︎
13. This strategy allows for sharing the burden with the supplier where they help you lessen the impact to your (and their) important customers. ↩︎
14. The most successful systems don’t manage every condition. In other words, it ok to manage unusually complex scenarios as exceptions. Too often we have seen distributors seek to solve all challenges systemically.  In our opinion, this introduces too much complexity and limits the likelihood of success of an analytics project such as the one we are discussing in this paper. ↩︎
15. The model must include the ability to run comparative scenarios and to generate output that quantifies estimates related to company margin erosion, customer price increase, decision count related to SKU price changes, alternate SKUs and deviation negotiation recommendations. This will allow leadership to strike the balance between these three constraints: a) return (margin preservation), b) risk (impact to customers) and c) administrative burden (decisions, and who makes those decisions and how those decisions are made). ↩︎
16. This interface includes many features, the most important of which are: data ontology and data lineage. This is the first but will not be the last time we reference the terms data ontology and data lineage.  Data ontology provides the consumer of information context. Data lineage allows that consumer to understand the source of the displayed metric – how that metric was calculated, derived, sourced – within one click. Both concepts provide understanding and confidence to adopt (act). ↩︎
17. Data attribution is the foundation for building a successful financial model. ↩︎