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Northwest Selling Solutions: Amazon Seller Central Automation for NetSuite Accounting and Inventory Management 

NSS is a grow­ing Ama­zon sell­er man­ag­ing of trans­ac­tions each month. While Ama­zon pro­vides exten­sive sales and set­tle­ment reports, rec­on­cil­ing those reports into accu­rate finan­cial state­ments in Net­Suite is noto­ri­ous­ly com­plex. Addi­tion­al­ly the man­age­ment of the flow of inven­to­ry from pur­chase through sale and returns pre­sent­ed a com­plex chal­lenge giv­en the dynam­ic nature of FBA logis­tics. GRAYBOX worked with NSS to define a solu­tion that sup­port­ed the flow of data from Ama­zon to Net­Suite, includ­ing auto­mat­ed inges­tion of dis­parate data and the cre­ation of all trans­ac­tions in Net­Suite to cre­ate a com­plete end-to-end pic­ture of Ama­zon activity. 

Outcome

We cre­at­ed an auto­mat­ed solu­tion that mapped com­plex and opaque Ama­zon reports into clear, accu­rate, and time­ly Net­Suite finan­cial and inven­to­ry data. The GRAYBOX solu­tion digests mul­ti­ple com­plex sources of data from Ama­zon, trans­lates it into the cor­re­spond­ing finan­cial records in Net­Suite, and pro­vides gran­u­lar detail for finan­cial and inven­to­ry reporting. 

Results

GRAYBOX built a solu­tion that allowed the NSS team to very quick­ly scale their oper­a­tions on Ama­zon with­out hir­ing addi­tion­al staff to man­age the increas­ing flood of trans­ac­tion­al data. Simul­ta­ne­ous­ly, the solu­tion pro­vid­ed greater and more gran­u­lar vis­i­bil­i­ty into per­for­mance, allow­ing the NSS team to make oper­a­tional deci­sions on prod­uct sets, pric­ing, and adver­tis­ing based on sales per­for­mance on Amazon. 

Amazon and NetSuite are two complicated end points from a data management standpoint.  In approaching a solution that would help NSS scale its operations, there were a number of challenges they were looking to overcome, including: 

  • Granular data complexity:  Amazon settlements include sales, refunds, promotions, fees, and reserve holds — often netted together, which obscures the true financial picture of performance.

  • Inventory alignment:  Inventory tracking from PO through Returns, across Amazon’s FBA warehouses and NetSuite’s perpetual inventory system, involves many moving pieces and sources of data. 

  • General Ledger (GL) accuracy:  Without the right transaction mapping, fees and offsets risked being buried or misclassified, leading to incorrect P&L reporting. 

  • Profitability insights:  Beyond just accurate accounting, NSS needed clear visibility into gross profit across product lines. This meant being able to quickly distinguish between profitable and unprofitable products, and use that data to adjust marketing investments, pricing strategies, and even which channels a product should be included in.

  • Scalability:  Manual journal entries or partial imports would not scale as NSS expanded its product catalog and sales velocity.

  • Maintainability:  Amazon frequently changes the structure and source of the data in its reports, which means any solution has to be sufficiently flexible to adapt as the data landscape changes.  

NSS needed a repeatable process that provided accurate, auditable accounting in NetSuite while also being efficient enough to handle Amazon’s transaction volume.

Per Asin Profitability
Key Performance Indicators
Trend Graph - Sales vs. Settlments

GRAYBOX designed and implemented a transaction-driven automation framework inside NetSuite that transformed Amazon’s settlement data into clean, reconciled financials.

1. Data Intake and Reports

We identified the most reliable Amazon data sources for financial reconciliation, including: Settlement Reports (bi-weekly) for definitive financial postings, Inventory & Fee Reports for reconciliation against NetSuite item and expense accounts, and Refund and Chargeback Data to ensure customer liabilities were properly recorded.  Our ability to automate the inventory and accounting processes can only be as effective as the data we have to work with, so this step was an incredibly foundational component in our solution.  Beyond simply identifying that the necessary data is present, the Graybox team also worked to map and correlate the data between the various reports to the NetSuite counterpart objects and fields, so that we could begin the mapping process for the movement of that data into our accounting workflow. 

2. NetSuite Transaction Types

Rather than relying on manual journal entries, GRAYBOX built a transaction structure that mirrored the underlying transactional events taking place on Amazon. This meant that every type of Amazon activity had a corresponding NetSuite transaction type designed to capture its specific impact.

For sales, we implemented a flow that created Sales Orders and Cash Sales at the SKU level, ensuring that revenue was recognized at the point of customer purchase. Refunds, returns, and chargebacks were not lumped together as a net reduction but instead represented as Credit Memos, preserving visibility into customer activity. 

When mapping Amazon transaction data to NetSuite there are different schools of thought on the level of detail to store in your ERP.  The Graybox perspective is that more detail provides better flexibility for future needs.  As such, all of the transactional activity is managed on a per-order basis, allowing us to trace back any NetSuite accounting transaction to the individual order and activity that happened on the Amazon side, providing us with incredibly granular audit capabilities.  

Amazon fees were mapped into Vendor Bills and Expense transactions, which allowed them to be categorized with much more granularity than a single catch-all “Amazon Fees” account. This approach made it possible to break out fulfillment fees, referral commissions, storage costs, and advertising spend as separate lines in NetSuite’s financial reports.

Inventory movements — such as when stock was transferred to or from Amazon FBA fulfillment centers — were represented as Inventory Transfers and Adjustments, maintaining accuracy between NetSuite’s perpetual inventory records and Amazon’s operational reality. We created a number of inventory Locations to help represent the various physical and conceptual locations Inventory transits through in its Amazon FBA lifecycle.  Through leveraging the Inventory reports from Amazon and automating the creation of transactions to reflect those activities, we always have a clear picture of inventory status and value across the supply chain.  Finally, we used Journal Entries sparingly, primarily to manage settlement timing differences like reserves and holds, which Amazon retains for potential chargebacks before releasing funds.

By aligning transaction types this way, NSS gained both accounting accuracy and business intelligence, since NetSuite now reflected not just cash flow but the operational drivers behind it.

3. GL Impact

Each transaction type was configured to have a specific and intentional impact on the general ledger. Sales flowed directly into product revenue accounts at the SKU level, enabling gross revenue reporting by product line. Cost of Goods Sold (COGS) was triggered automatically by NetSuite’s costing engine at the moment of fulfillment, ensuring that profitability could be measured in real time.

We routed Amazon fees into a series of detailed expense accounts. This separation allowed NSS to see, for example, how much gross margin was eroded by fulfillment fees versus advertising spend, giving leadership clear visibility into where operational improvements might yield the biggest impact.

Another tricky aspect to financial reporting are the various timing delays and discrepancies that exist.  Not all reports represent data across the same time frame, and some financial impacts occur on a delay based on the type of activity.  To manage timing differences, we established clearing and settlement accounts. These served as temporary holding accounts for funds that Amazon had not yet disbursed, as well as for reserves Amazon held back. By doing so, the balance sheet reflected both current assets and contingent liabilities with precision, supporting audit readiness and better cash forecasting.

4. Automation Scripts

The backbone of this solution, and how we facilitated NSS’s ability to scale, is a series of SuiteScript automations that parse Amazon settlement files and other reports and translate each report line into the correct NetSuite transaction(s). The scripts performe intelligent mapping, so a single settlement could generate hundreds of NetSuite transactions — all structured consistently and tied back to the source data.  This automation is the tangible output of the extensive data mapping exercise we used to start this process.  

These automations also manage reconciliation logic, validating that the sum of NetSuite entries equal the net amount disbursed by Amazon. The system flags any anomalies, such as missing SKU mappings or unrecognized fee types, before final posting. This additional series of checks ensure that accounting staff are spending their time reviewing exceptions rather than manually keying in every line of data.

The automation became the backbone of NSS’s Amazon–NetSuite integration: scalable, rules-driven, and reliable enough to process thousands of transactions without human intervention.

5. Audit & Controls

Accuracy was further reinforced with a layer of audit controls built directly into the process. Every settlement was required to tie out — the total recorded in NetSuite had to match Amazon’s disbursement down to the penny. In addition, we created variance reports that compared SKU-level sales in Amazon against inventory movements in NetSuite, highlighting discrepancies in near real time.

We additionally deployed NetSuite dashboards to help manage the process. These dashboards provided inventory, finance, and operations leaders with up-to-date insights into sales performance, ordering needs, fee burdens, and reserve balances. By embedding controls and transparency into the workflow, NSS gained not just accuracy but confidence — knowing the system would surface issues automatically rather than relying on manual spot checks.

The process of pulling reports, cre­at­ing trans­ac­tion CSV imports, and doing the inven­to­ry rec­on­cil­i­a­tion was daunt­ing. The automa­tion the GRAYBOX team put in place for our Net­Suite account saves hours and hours a week of man­u­al effort, and gives us a time­line and real­ly accu­rate pic­ture of what’s hap­pen­ing with inven­to­ry and sales across our business.

Dan Ryerson, CFO, NSS

Our Solutions

This engagement leveraged a number of GRAYBOX skill sets and solutions.

Final Results

The automation framework delivered by GRAYBOX fundamentally changed how NSS accounted for and analyzed its Amazon seller central business. Where previously settlements were a black box of complicated reports and netted financial figures, NSS now had transaction-level clarity in NetSuite to reflect their performance. Every sale, refund, fee, and adjustment flowed through NetSuite in a way that mirrored the underlying economics of Amazon’s marketplace, ensuring that financial statements were both accurate and auditable.

As important, the ability of NSS to keep tight controls in inventory was greatly improved.  Through frequently updated visibility into what is on order, what is moving through Amazon’s warehouse system, what items have been purchased, and what returns are in various states of progress, the business is able to easily idenfity where they are over or under stocked on product. This ensures they can keep just the right amount of inventory in the FBA system, helping ensure buy-box while reducing unnecessary warehousing fees.  

One of the most significant outcomes was improved profitability visibility. With gross profit accurately reflected at the SKU and product line level, NSS could quickly see which products were driving margin and which were underperforming. This enabled faster, more informed decisions about where to adjust pricing, how to allocate advertising spend, and when to shift products in or out of certain sales channels. Instead of waiting until quarter-end to diagnose profitability issues, leadership could pivot strategy week by week.

On the operational side, the automation eliminated hours of manual transaction entry and reconciliation work with every settlement cycle. What once required painstaking effort from accounting staff was now handled automatically, freeing the team to focus on higher-value analysis rather than repetitive data entry. The process scaled seamlessly as NSS’s sales volume grew — thousands of transactions were processed without adding headcount or compromising accuracy.

The system also reinforced audit readiness. Every Amazon settlement tied out exactly to the corresponding entries in NetSuite, with dashboards and variance reports surfacing any anomalies immediately. This level of precision not only reduced the risk of misstatements but also gave stakeholders — from executives to auditors — confidence in the integrity of the numbers.

Perhaps most importantly, NSS gained trust in its data. With accurate revenue, expense, and inventory alignment, the finance and operations teams could work from a single source of truth. That trust unlocked the ability to operate more strategically: whether planning cash flow, modeling new product launches, or evaluating the ROI of marketing campaigns, the business now had reliable insights to guide its decisions.