Turning Data Chaos Into Clarity

TeamCentral’s Andy Park explains how using automation to integrate systems unites the modern distributor and paves the way for AI implementation.

Every distributor knows that closing a sale is only the beginning of the customer relationship. A lot can go wrong between order placement, delivery and a satisfied buyer. One of the most common culprits is the handoff of order information between the salesperson, billing team, warehouse staff, and transportation provider. In many cases, each stage must reenter order information into their specific databases and platforms, a time-draining process that causes delays and compounds the opportunity for human error. Add in the potential for a salesperson to sell an item they didn’t know was out of stock, and the frustrations caused by fragmented data become clear.

Fortunately, advancements in automation are setting free data that was previously confined to individual departments — making it more visible and useful across the entire company while eliminating repetitive data entry. In the following Q&A, Andy Park, co-founder and COO at TeamCentral — an AI-powered system integration and data automation platform and a FEDA Data Portal + Quoting Partner — explains how automation connects data silos and helps distributors move away from manual management.

What are some of the problems businesses run into when they manually manage their data?
When production, sales and inventory data are entered manually into separate systems (or spreadsheets), information becomes disconnected or “siloed.” Teams work from different versions of data “truth,” leading to mismatched part numbers, pricing errors or out-of-date stock levels. We work with a customer whose sales team reserves inventory out of concern that it won’t be available when customers place orders. Because the company’s ERP system is updated only once a day — manually, from paperwork on the dock — the team lacks real-time visibility into current inventory levels. As a result, they worry about selling items they can’t deliver.

How are leading distributors using automation to better manage their data?
Top distributors are using automation to keep data clean, current and useful — without adding people or spreadsheets. A big one is order-to-cash automation. This uses automation to auto-validate orders (stock keeping units, pricing, customer data), push orders from customer relationship management (CRM) systems to ERP and confirm inventory available to promise. From there, it checks the shipment and delivery commitment, releases the order to the warehouse based on ship dates, generates advanced ship notices from warehouse management system (WMS), invoices upon shipment, and applies cash received back into the ERP. This requires integration between systems, which integration-platform-as-a-service (iPaaS) solutions are built to do with little to no custom code development.

How does data automation improve the supply chain experience for manufacturers, dealers and end-users?
For many manufacturers, managing the supply chain used to mean constantly reacting — waiting for yesterday’s production reports, manually reconciling what was shipped or calling suppliers to confirm what was really on the way. Information gaps caused inefficiencies that rippled across the entire operation. With data automation, that all changes. Every scan on the shop floor now updates the ERP and WMS instantly. As soon as raw materials are consumed, production orders backflush automatically. Likewise, as soon as finished goods roll off the line, inventory adjusts across every warehouse. Purchase orders, advanced ship notices and invoices flow between systems without human intervention. For example, a parts manufacturer saw a significant drop in production variances and also improvement in on-time, in-full performance after connecting their manufacturing execution system, ERP and supplier electronic data interchange (EDI) through automated workflows.

Distributors once lived in spreadsheet chaos — manually rekeying sales orders, chasing down available-to-promise numbers, updating dealer products and pricing lists, and reconciling data between ERP, CRM and e-commerce systems. Every handoff created opportunities for delay or error. Data automation turns that chaos into orchestration. Pricing updates can now flow instantly from ERP into configure, price, quote (CPQ) and e-commerce portals. Available-to-promise and estimated time of arrival data sync in real time, so every dealer and manufacturers’ rep can provide quotes with confidence in the accuracy of the underlying data. Third-party logistics providers, carriers and drop-ship vendors are connected via automated EDI, enabling orders, confirmations, advanced ship notices, and invoices to be exchanged seamlessly.

Should distributors prioritize automating certain data workflows first? If so, which ones typically deliver the quickest and most impactful wins?
Automation can touch dozens of processes, but not all deliver the same speed-to-value. The smartest distributors start with high-volume, high-error and high-visibility workflows where even small gains make a measurable difference in customer experience, operational efficiency and profitability. Take the order-to-cash process. When a dealer sets a price quote in the CPQ, the CRM captures that order immediately. Once the customer confirms, it automatically makes a record in the sales system. From there, the WMS can pull the data to fulfill the order, even sharing critical information with transportation partners to track status and delivery through a no-code iPaaS. When properly set up, automation allows all that information to happen without human intervention. Orders that once took hours to process now flow automatically, freeing the sales team to focus on upselling rather than fixing errors.

A close second is inventory synchronization because few things frustrate customers more than being promised items that aren’t actually in stock. Automated, real-time synchronization between ERP, WMS and e-commerce portals ensures every channel reflects true inventory availability. This not only reduces backorders and stock-out order cancellations, but also improves customer satisfaction and trust in the ordering and delivery process.

What are some examples of how automation can help distributors become AI ready?
Before AI can provide insights, make recommendations or take actions, it needs clean, connected and contextual data.

Most distributors aren’t lacking data; they’re drowning in it. Today’s companies are juggling so many business systems that store valuable information, but none of them speak the same language. Leading distributors depend on automation to bridge those gaps. By continuously synchronizing, normalizing and governing data, automation builds the information pipelines that AIs need to reason, predict and act. In doing so, automation positions AI to deliver its intelligence potential — but only if implemented correctly.

What are some steps businesses can take to move from manual data management to data automation?
1. Establish clear data standards and business rules for the data. Before automating anything, businesses need to define what “good data” looks like. This means standardizing how customers, products, orders, and locations are defined, named and formatted across systems.

2. Break down data silos between systems and groups within your organization. Sales, operations and finance often each have their own systems (CRM, ERP, WMS, etc.), none of which communicate automatically. Removing silos means connecting these systems so data flows seamlessly end-to-end. This is where iPaaS integration and data synchronization comes in.

3. Modernize and integrate legacy systems. Many organizations rely on outdated systems or custom databases that weren’t designed for integration. Modernizing (or wrapping them in an integration layer) allows these systems to participate in automated workflows.

4. Introduce real-time data monitoring and continuous improvement. Once automated workflows are live, businesses need visibility into data health —the ability to track flow performance, error rates and latency. Dashboards and alerts help teams continuously refine rules and expand automation safely. Automation is not “set it and forget it.” Real-time observability helps catch integration failures early and ensures every transaction, sync and data stream remains healthy. Over time, this foundation supports AI agents that can learn, explain and act on live data.

What are the most common integration/implementation challenges when connecting automation tools with existing ERP (or other) systems, and how can distributors prepare for them?
Legacy systems tend to have limited connectivity with modern tools. Many distributors run ERP systems that were built 10–20 years ago. These systems often lack modern application programming interfaces (APIs) or use flat files, open database connectivity connections or custom scripts. Integrating them with cloud-based automation tools can be slow, brittle or dependent on third-party connectors. Good iPaaS solutions can overcome this challenge by providing a variety of integration approaches.

The other major challenge is data quality and inconsistent data standards. Automation relies on clean, structured data, but many companies have inconsistent product codes, duplicate customer records and incomplete fields across systems. Integrating this data “as is” amplifies chaos. Poor data integrity causes failed syncs, inaccurate reports and AI models that aren’t trained well. Automation without quality governance moves bad data faster. To avoid these problems, companies should discuss data standards and clean their data accordingly. They can also program their systems to enforce some data entry validation rules.

What kind of employee skills should companies be looking for as they move toward greater data automation?
We typically recommend looking for people who are problem solvers with some experience using enterprise systems — those who understand how data flows through ERP, CRM and operational systems and can identify where automation creates the most value. Skills in process mapping and system integration concepts are critical, since these employees can translate manual workflows into digital ones using no-code or low-code automation tools.

Equally important are people who know how to structure, validate and maintain clean, consistent data that automation can trust. Employees with analytical and data visualization skills (e.g., Excel, Power BI, SQL basics) can interpret automated data outputs and turn them into actionable insights.

What are some tools that can help distributors automate their data management?
We’re seeing manufacturers and distributors automate in four main areas: system integration, logistics, product data, and payments. Tools such as iPaaS, EDI, product information management, and e-payment platforms keep everything connected and accurate — from orders to invoices and inventory — so teams can make faster, smarter decisions and prepare for AI-driven operations.

What are some key performance indicators or performance metrics that companies should use when evaluating the usefulness of such tools?
When evaluating automation tools, companies should look beyond simple cost savings and measure operational, financial and customer-focused outcomes. Key metrics include order accuracy, inventory record accuracy and reductions in manual touches or cycle time, each of which can show how well automation is improving efficiency and reliability. On the financial side, indicators such as days sales outstanding, invoice accuracy and payment reconciliation time reveal whether automated e-payment and billing processes are accelerating cash flow and reducing errors. Finally, customer-centric metrics such as on-time, in-full delivery rates and satisfaction scores demonstrate how better data flow directly enhances service quality and partner trust across the supply chain.