Distribution and Manufacturing: Pricing Capability Improvement & Automation to Recapture Margin

Client situation

This mid-sized distributor of industrial products for MRO (maintenance, repair, and operations) provides its manufacturing customers with spare parts, equipment, and supplies. They urgently needed to tackle longstanding process problems with pricing—both external and internal.

External issues included late supplier notices of price increases. Annoying as these late vendor notices were, they destroyed confidence in the internal price schedules, resulting in factors that eroded margin:

• Highly inconsistent prices quoted to customers (3x variance for identical products)
• Excessive sales-rep-initiated “price overrides”
• Requests for quotes from customers that got “lost in the shuffle”
• Failure to file claims on suppliers with excessive late notices

The CEO was facing a host of additional issues. Competitors attacked both ends of the distributor’s offering spectrum. At the top end, where sophisticated “solutions” were sold through consultative methods, overseas global giants poached national accounts. At the bottom end, where basic products were sold locally, “no-frills” competitors—even Amazon—offered lower costs for less service. The distributor was getting “stuck in the middle” with few advantages for customers. In addition, tenured staff were retiring, taking the business’s tribal knowledge with them.

New replacements, overwhelmed by the steep learning curve, rarely stayed more than six months. Against this backdrop, late supplier price increases combined with price overrides offered by the distributor’s own salespeople were wreaking havoc on gross margins—but no one had any time to calculate the amount.

The executive team was frustrated. Their internal team’s conventional improvement strategies were no match for these concurrent challenges. They needed a different approach. They needed it to stick. They wanted the changes automated. Yet their most recent ERP upgrades weren’t yielding results.

Collectively, they decided to contact The Lab. The CEO knew that the executive team needed a win, so he decided to focus exclusively on margin management—and pricing in particular.

Project scope: Pricing Automation for Improved Margin Management

As always, time was of the essence. The project scope was quickly defined in terms of the major segments of the end-to-end process flow that include pricing. There was one “inbound” segment from the supplier that warranted scrutiny and rapid response. Once an error or delay worked its way into the process here, it was nearly impossible to correct it or reestablish credibility over the subsequent process segments:

Inbound from Supplier

• Supplier Pricing Notifications

Outbound to Customer

• Prospect-to-Quote
• Quote-to-Conversion
• Order-to-Cash

The Lab’s proposal outlined the major objectives of the initiative. These were easy to say and list; the hard part was identifying the activities needed to achieve these goals. Fortunately, The Lab’s IP-based “templates” helped (more about these in “Analysis & Design,” below). 

Phase I: Analysis & Design

The improvement and automation initiative began with a rapid six-week analysis of the entire organization. This is where The Lab has an advantage versus the conventional “start with a clean sheet” approach. Over the course of The Lab’s 30 years of experience, we’ve documented the most proven, high-value, rapid-payback sources of improvement for supply-chain executives—as well as the roadblocks that could hinder implementation. And we’ve compiled these best practices into “templates” for improvement. For example, our best practice business process-map “templates” detail the likely errors in data intake and provide suggestions for how to fix them. The maps also detail precisely where robotic process automation (RPA) and AI could be installed to automate aspects of the business process.

This approach focuses the organization’s attention on what the process could be, avoiding the common problem of false precision and “analysis paralysis” that typically plague such efforts. The same holds true for our data-standardization models, our automation use-cases, and our predictive analytics for reporting. These are all designed to plug into a standardized, future-state map. 

Because we implement RPA for automation, no new core technology is required. And benefits are
always guaranteed.

Phase II: Implementation

Together, The Lab and management set about standardizing business processes and implementing a suite of RPA bots to update prices based on supplier notifications and reconcile prices across all distributor systems. Additional bots were deployed to incorporate predictive analytics and AI to enable preemptive reporting that highlighted likely margin “leakage” on every order and SKU.

This leakage existed in two major processes that required attention. The first included supplier-to distributor, the second involved distributor-to-customer, or essentially everything else downstream. While both processes required standardization and automation, the second process also represented a major increase in executive oversight and business measurement, a significant change for this distributor’s freewheeling, sales-driven culture.

• The first task involved capturing, standardizing, and automating the supplier-to-distributor price notifications. This distributor’s front-line staff and mid-level managers had previously determined these to be “un-automate-able,” even with all the most recent ERP updates available.
• The second task involved building an automated framework to monitor the status of distributor-to-customer quote requests. These “quote-to-conversion” bots managed a centralized, automated, and performance-measured pathway between the three major systems: ERP, CRM, and the WMS.
• Once this was complete, management had visibility of all the issues in their distribution supply chain. The remaining improvements (scores) were also addressed with RPA bots, real-time predictive analytics, and AI.

Within the first six months, the engagement had paid for itself, and all the bots were operational. From that point on, it was simply a matter of managing out the excess variance in customer pricing, putting the squeeze on sales reps who continually issued overrides, and pressuring late suppliers to do better on pricing notifications.

Inbound from Supplier: Process Improvement Solutions Implemented

Data Standardization, Robotic Process Automation, KPI Analytics

Solution 1: Automating supplier price changes and implementing a data integrity team

This distributor faced a constant barrage of supplier-cost increases that needed to be promptly passed on to customers in the form of higher product prices. But the sheer volume of these updates overwhelmed the pricing teams. Most of the delays were the avoidable result of a lack of standardization and automation. For example:

• Supplier-cost files arrived in inconsistent formats: Excel, PDF, typed out in emails, and called in over the phone.
• These data were then uploaded in bulk—and sometimes manually typed, SKU-by-SKU—into an outdated Excel template, then imported into the ERP system, generating a significant human error rate (14% rework). Nobody wanted to perform this work and so it was handled by the Finance department as a stopgap, last resort.
• To make matters worse, nearly a third of supplier cost increases arrived after the effective date—and that figure was growing. However, resource constraints at this distributor prevented proactive management, i.e., negotiation or filing a claim.

First, responsibility for managing all supplier-to distributor price notifications was moved from Finance back to procurement as a Data Integrity Team (DIT) capability was being implemented. Next, the intake process was standardized as much as possible and electronic price change forms were implemented for use by suppliers. Finally, a first wave of automation, in the form of RPA bots, was added to automate this process. Later, once a rhythm was established, Intelligent Document Processing (IDP) was added to the suite of bots to attempt to interpret the “nonstandard” inbound notifications.

Even with these automations, this intake process was still human-supervised, since getting these notifications right at the first point of contact was essential to maintaining downstream accuracy. The new DIT capability was paramount for this task and future data innovation projects for the distributor.

Solution 2: Supplier late-price change-notice reporting & Super KPI analytics

In conjunction with the development of bots, centralized, real-time automated analytics “Super KPIs” dashboards were developed to monitor the effectiveness of the effort and to provide management oversight. These analytics dashboards provided “at-a-glance” updates for the status of all price notifications. A basic reporting module was established to begin. Later, additional modules were added to enhance the features of this dashboard.

This distributor now had data-supported automated processes for responding to late price notifications. Late-reporting suppliers were proactively managed and warned in writing by a bot. Continuing offenders (i.e., suppliers) had the implications of their late prices quantified and provided back to them. Notification of substitute products were also provided by automation to the sales team on a daily basis. Finally, if all else failed, claims documentation was compiled as support for supplier negotiations and where necessary, as proof of a claim.

AI-analytics-powered bots reported on the performance of all suppliers, issuing a report that flagged those which issued price notifications late. It monitored their response to warnings on late price notifications. And it calculated the cost to the distributor’s margin due to late price-change submissions and made suggestions for replacement-supplier sourcing.

Solution 3: Automated late supplier price-change claims documentation compilation

Additional bots were implemented to document the case for a claim. Although this distributor expected to file few claims, this documentation proactively reduced the cycle time leading to a claim: warning letters, and past documented examples for “repeat offenders.” 

Distributor-to-Customer: Automation Solutions Implemented 

Quote-to-conversion bots & centralized automated pricing capability

As this engagement unfolded, management realized the need for a centralized point of visibility of all outstanding orders, with margins visible at the individual order level and customer-decision dates. Although they had three systems (ERP, CRM, and WMS), the data quality and measurements varied by each. This new central viewpoint would deliver the ability to manage the entire portfolio of orders, customers, and producers.

The first step in building this pricing management capability involved un-blocking the manual “logjam” that typically occurs at the quote-to-conversion step—or between the ERP, CRM, and WMS systems. Human workers could not keep up with the daily volumes of orders and large amounts of data that they had to work with across these three systems.

Given the high weekly volume of customer requested quotes—often spanning hundreds of line items, including products that require external sourcing and pricing—the distributor’s pricing and sales teams struggled just to keep up with this demand for quote preparation. That meant that after the quote was delivered, there wasn’t enough human capacity to update the company’s ERP, CRM, and WMS systems in a timely manner.
The first task in developing this capability was to reconcile and standardize data across all three platforms.

After this was complete, The Lab designed and documented routes for the data to travel. Next, these data flows were automated by bots that process internal ERP, WMS, and CRM workflows—transmitting data just like the “swivel chair” human work that nobody had time to keep up with. And finally, The Lab installed bots that report
out, to a central dashboard, the status of quotes.

These bots alleviated the burden of sales-team members overwhelmed by requested quotes. Acting as real-time, virtual “upstream managers,” they gathered pertinent input from the ERP and entered it into relevant fields of the CRM. Finally, the bots prepared “to-do” lists for individual sales reps. They gave them timely prompts and data to help track, and convert, each quote request, even flagging potential negative-margin quotes.

The AI-powered quote-to-conversion bot implemented by The Lab performs all the “swivel chair” intra- and intersystem data-movement activities in the ERP, CRM, and WMS, just like a human worker—except it performs at 20x speed, and makes no data errors. It solves these challenges with an efficient multi-step process:

• The bot logs into the company’s year-to-date quote-list data set, where it identifies the most recent quotes stored in the ERP. In a single pass, it memorizes all the quotes it had searched for: customer, quote amount, line items, due date, and other characteristics.
• Next, the bot next logs into the CRM, where it sets up a new “opportunity” for each quote, and auto-populates all required fields: Opportunity Name, Organization Name, Dollar Amount, Opportunity Type, Start Date, Expected Close Date, Next Step, Sales Stage, Next Step Due Date, Probability, and Assigned Sales Rep.
• It assigns the tasks to the appropriate sales reps and selects the interval it will use to remind the rep of the pending due date and relevant next step to convert the pending order.
• To assist the reps, the bot generates a draft email for them to send to the customer, even attaching PDFs of the appropriate product catalog cut sheet(s) to give them the information needed to help close the sale.
• Using AI, the bot works to protect the margin on each quote. Each quote’s email comes with a summary sheet that outlines the margin of that quote. Copies are forwarded to the rep, the rep’s manager, and the centralized management box.

This end-to-end, real-time, automated capability laid the foundation for centralized pricing and quote management. The process was resilient: It could not be undone by employee turnover or tribal knowledge leaving the company. It enabled executive oversight to manage both the overall distribution margins as well as the individual drivers of margins, such as individual producers and customers. Over time, this distributor would be able to add features that would provide more management control to launch new offerings: vendor-managed inventories, collaborative forecasting, and others. However, in the short term, they were focused on eliminating the massive number of price overrides—waived price increases.

Distributor-to-Customer: Data Driven Insights Solutions Implemented

Gaining visibility into overrides and preempting them with data

One more direct consequence of lagging supplier price updates was out-of-control price overrides by front-line sales staff. The Lab helped this distributor discover and quantify that nearly half of all orders include a price override.

These overrides resulted from operational shortfalls. For example, there were few clear business rules and thresholds to determine when and how an override should be reviewed. It was not clear who was even authorized to override a price. This distributor had no time to create those operational rules. Instead, the focus was on the more immediate problem, which was the pricing chaos resulting from the lack of discipline regarding supplier price notifications. Additionally, the sales staff had no trust in the ERP pricing, since they knew all the pricing updates were at least a month, but usually two months, behind schedule.

Without centralized visibility of orders, there were no pricing rules that couldn’t be broken without any consequences. Margin was not a top-of-mind concern for most sales staff. So when customers complained about price increases, sales staff were generally eager to provide an override. Some even proactively discounted prices—issued an override—to incentivize customer loyalty.

The result at this distributor was that over 90 percent of downward price overrides were approved, typically without any review. These overrides alone accounted for the “invisible” loss of 12 percent of gross margin.

To instill data-driven discipline, this distributor required automated measurement of overrides. Predictive analytics was added along with RPA bots to anticipate when an override was likely to be issued. Most cases simply involved bringing visibility to the transaction in a timely manner. In situations where an override policy was out of alignment with customer needs, an adjustment was easily made.

To reduce the frequency of high-dollar overrides by CSRs, robotic process automation was implemented across the ERP and already existing processes. This solution, combined with predictive analytics, automated the complex process of quantifying and qualifying price overrides by salespeople, including automatically analyzing all sales, margins, and override amounts, on a daily basis.

Business Results and Benefits

The distributor took action on The Lab’s recommendation to centralize notification intake and related data management. The culture change involved with centralized pricing and margin management oversight was challenging. However, with strong sponsorship, a relentlessly persistent, fact based, bi-weekly, “lather rinse repeat” process to manage pricing improvement was adopted. This work was removed from the finance team and assigned to a newly formed, dedicated team from operations. This new “Data Integrity Team, or DI Team” had full accountability for all pricing – and no responsibility for sales. These changes were essential because the distributor’s strategic growth plan required sales resources to expand its service-based offerings without adding headcount.

1. Supplier-to-distributor impact

After the bots were deployed, the chaos at the distributor that was attributed to pricing changes quieted down. As long as the salespeople were confident that the ERP contained the latest supplier pricing data there was no rationale for issuing overrides and these diminished quickly.

Suppliers got the message as well. Some suppliers were continuing to act as repeat offenders, but this turned out to be a very small group – less than 2 percent and dropping. Claims were filed in several cases, but the distributor found easier alternatives with other, more responsive suppliers.

2. Distributor-to-customer benefits

Once discipline and automation were established in the process for managing supplier price notifications, and the performance was reported on a regular basis, the credibility of management increased. The Data Integrity “DI” Team extended the scope of their efforts to include this next segment of the end-to-end business process – internal pricing operations. Centralization of overrides and margin management with data standardization, Microsoft Power BI analytics dashboards and bots reduced pricing variance and overrides within weeks.

The overrides maintained a steady retreat, the pricing variance narrowed, and individual order margin status was easily tracked and proactively managed by the distributor.

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