Omnichannel Order Management: How AI Drives Retail Growth?

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Discover how AI-powered omnichannel retail order management system connects retailer ordering, inventory and distribution to improve availability, sales productivity and profitable retail growth.

Gaurav singh

20 mins read

14 Oct 2026

SFA

The biggest threat to retail growth is not always a lack of demand. It is the inability to convert existing demand into profitable, fulfilled orders.

A retailer wants to place an order, but the sales representative is unavailable. The retailer app shows a product that the distributor cannot supply. A promotion exists, but the retailer does not know about it. Meanwhile, sales teams continue to chase orders manually, distributors reconcile conflicting records, and leadership reviews reports that explain yesterday's performance rather than today's missed opportunities.

A retail order management system typically supports several core functions:

Order capture: Recording orders across digital and assisted channels.

Order validation: Checking product availability, pricing, credit limits and applicable trade schemes.

Inventory visibility: Connecting order decisions with available stock and replenishment requirements.

Order orchestration: Directing orders through the appropriate processing and fulfilment workflows.

Order tracking: Making order status, exceptions and delivery progress visible.

Performance intelligence: Analysing order patterns, service levels, missed demand and commercial outcomes.

For FMCG businesses, Omnichannel Order Management is not just an ordering interface. It is the operating capability that connects demand capture with the business processes required to fulfil that demand.

Why Is Omnichannel Order Management Important for Retail Growth?

Retail growth depends on more than generating demand. It depends on whether a business can capture that demand, supply the right products, execute commercial programmes and serve retailers consistently.

Disconnected order management creates friction at each stage.

A retailer who cannot place an order outside a sales visit may postpone a purchase. A distributor working with inaccurate stock information may reject an order or substitute products. A brand that cannot connect retailer behaviour with order history may miss a cross-selling opportunity.

These failures affect revenue, working capital, sales productivity and customer retention simultaneously.

Omnichannel Order Management helps address five commercial priorities.

1 Capture demand beyond the sales visit

Traditional sales-led ordering depends heavily on the frequency and quality of field visits. Digital ordering channels allow retailers to place orders when they need stock, rather than waiting for the next scheduled interaction.

The business benefit is greater ordering accessibility and the potential to increase order frequency, provided retailers adopt the channels and products remain available.

2 Improve product availability and order fulfilment

An order channel is useful only when its information reflects operational reality. Connecting ordering with distributor inventory and order-processing workflows helps reduce avoidable order rejections, improve fulfilment decisions and expose stock-related constraints earlier.

3 Increase revenue per retailer

Order history, purchase frequency and SKU-level demand can reveal opportunities to recommend complementary products, improve assortment and promote relevant schemes.

The objective is not to push more products indiscriminately. It is to increase the proportion of relevant demand that the business captures while protecting margins and retailer trust.

4 Improve salesforce productivity

When retailers can handle routine orders digitally, field representatives can devote more time to outlet expansion, merchandising, assortment development, collections and relationship management.

The commercial value comes from reallocating sales capacity towards activities that require human judgement, not simply reducing the number of visits.

5 Give leadership a connected view of commercial performance

A connected retail order management system can bring together order capture, fulfilment, retailer engagement and sales execution data.

This helps leadership investigate questions such as:

Which territories generate demand but fail to convert it into fulfilled sales?

Which products are frequently unavailable when retailers order?

Which retailers are becoming less active, and why?

Which promotions generate incremental, profitable demand?

Where does distributor service performance constrain market growth?

The CXO business case for Omnichannel Order Management

Executive stakeholder	Business priority	What Omnichannel Order Management should improve	Strategic outcome
CEO / Managing Director	Profitable, sustainable growth	Visibility into demand capture, fulfilment and revenue leakage	Better capital allocation and growth execution
Chief Growth Officer (CGO)	Revenue expansion	Retailer activation, order frequency, assortment and conversion	Higher quality of growth
Chief Sales Officer (CSO)	Sales productivity and coverage	Digital order capture, outlet reach and opportunity visibility	More productive selling capacity
Chief Financial Officer (CFO)	Margin, cash flow and working capital	Order accuracy, inventory efficiency, discounts and cost-to-serve	Stronger contribution margins and cash discipline
Chief Operating Officer (COO)	Service reliability	Inventory accuracy, order processing and fulfilment exceptions	More consistent service levels
Chief Information Officer (CIO) / CTO	Scalable technology	System interoperability, data quality, security and governance	Lower integration risk and a more adaptable architecture

For the CEO, the value is commercial control. For the CGO, it is the ability to turn more demand into profitable growth. For the CFO, it is ensuring that additional sales do not come at the expense of margin, working capital or cost-to-serve.

These outcomes are connected. A retailer who receives the right products on time is more likely to continue ordering. A sales representative who can see outlet-level demand can prioritize the right intervention. A distributor who receives more accurate orders can plan inventory and fulfilment with greater confidence.

The advantage comes from improving the entire order lifecycle, rather than optimizing one isolated activity.

How Does AI Improve Omnichannel Order Management?

Traditional order management systems primarily record transactions and execute predefined business rules. AI Order Management adds the ability to analyse patterns, predict likely outcomes, recommend decisions and, where authorized, automate selected actions.

The distinction is important. Digitization makes a process faster to execute. AI can help determine which action should happen next.

1. From order history to demand prediction

Historical orders reveal what retailers purchased. When combined with relevant signals such as seasonality, promotion calendars, SKU movement and outlet characteristics, AI models can estimate what retailers are likely to need next.

This can support more relevant reorder recommendations, demand planning and distributor inventory decisions.

Forecasts remain estimates, not guarantees. Their usefulness depends on data quality, model performance and how quickly the business incorporates changes in actual demand.

2. From generic promotions to commercially relevant recommendations

A uniform promotion treats every retailer as though they have the same assortment, purchasing capacity and demand profile.

AI can help segment retailers according to purchase patterns, product preferences, order frequency and other permitted business signals. Brands can provide tailored product recommendations and scheme to specific retailer needs.

The objective is to increase incremental sales without unnecessarily discounting products that retailers would have purchased anyway.

3. From exception reporting to proactive intervention

Conventional reporting often identifies a problem after it has affected performance. AI can monitor order patterns, inventory signals and service indicators to identify emerging exceptions.

For example, a sudden decline in orders from a previously active retailer may warrant investigation. A recurring mismatch between ordered quantities and available distributor stock may indicate a planning or inventory issue.

The system can flag the exception, recommend an intervention and route it to the appropriate team. Automated action should remain subject to business rules, permissions and financial controls.

4. From manual ordering to intelligent assistance

AI-powered conversational interfaces can simplify product discovery, ordering and reorder decisions. Retailers may be able to request products in natural language, explore relevant SKUs and receive contextual recommendations without navigating complex catalogues.

This is particularly useful where retailers have limited time, prefer local languages or are less comfortable with conventional application interfaces.

5. From fragmented metrics to commercial decisions

The strongest AI Order Management model connects demand signals with downstream business outcomes.

A recommendation to increase an order quantity should consider more than predicted demand. It should account for available stock, delivery constraints, commercial terms, product economics and the retailer's likely ability to sell through the inventory.

That is the difference between generating an intelligent suggestion and making a commercially useful decision.

McKinsey's research on retail AI identifies demand forecasting, pricing optimization, customer segmentation and supply-chain optimization as important applications. Its work on agentic AI also highlights the potential to move beyond analysis towards systems that execute defined tasks. Realizing that potential requires integration, usable data and redesigned workflows, not simply adding an AI interface to an existing process. (Ref: McKinsey: Rewiring retail in Europe; McKinsey: Agentic AI in retail merchandising)

The strategic role of AI in Omnichannel Order Management is to reduce the gap between detecting a commercial opportunity and acting on it. The business benefit depends on whether those actions improve conversion, availability, service or profitability.

What Are the Key AI Use Cases in Omnichannel Order Management?

AI use cases should be selected according to the commercial constraint they address. A sophisticated model has little value if it solves a low-impact problem while a larger source of revenue leakage remains untouched.

The following 15 use cases illustrate where AI can make a measurable difference across the retail order lifecycle.

AI use case	Business pain point	What AI can do	Potential contribution to growth
1. Predictive reorder recommendations	Retailers order too late or forget fast-moving SKUs	Estimate likely replenishment needs using order history, SKU movement and seasonality	More repeat orders and fewer missed sales
2. SKU-level demand forecasting	Forecasts fail to reflect local demand variation	Predict demand by product, territory, distributor or outlet where data permits	Better product availability and inventory allocation
3. Intelligent cross-selling	Retailers buy a narrow assortment despite relevant demand	Recommend complementary products based on purchasing patterns and product relationships	Higher average order value and assortment penetration
4. AI-led upselling	Sales teams lack visibility into suitable higher-value options	Identify relevant pack sizes, product tiers or quantities based on retailer needs	Higher revenue per order when the recommendation is profitable
5. Personalized trade promotions	Uniform schemes create low engagement or unnecessary discounting	Match eligible retailers with relevant schemes and product offers	Better promotion conversion and incremental sales
6. Retailer churn prediction	Declining retailer engagement is detected too late	Identify unusual drops in ordering frequency, value or SKU breadth	Higher retailer retention and recovered sales
7. Dormant outlet reactivation	Previously active retailers stop ordering	Prioritize inactive outlets and recommend tailored re-engagement actions	Recovered revenue and stronger outlet productivity
8. Conversational order capture	Complex catalogues and interfaces discourage digital ordering	Interpret natural-language requests and guide retailers through product selection	Lower ordering friction and broader channel adoption
9. Multilingual ordering assistance	Language barriers limit access to digital channels	Support conversational interactions in supported local languages	Greater accessibility and potential expansion into underserved markets
11. Intelligent stock allocation	Available inventory is distributed without sufficient demand context	Recommend allocation based on demand, service priorities and stock constraints	Better sell-through and reduced lost-sales exposure
12. Promotion demand sensing	Campaign demand differs from initial forecasts	Combine promotion schedules with sales and ordering signals to identify changing demand	Better campaign availability and lower excess stock
13. Order anomaly detection	Unusual quantities, duplicate orders or inconsistent patterns go unnoticed	Flag deviations for validation against business rules and historical patterns	Lower operational leakage and fewer preventable errors
14. Delivery and fulfilment exception prediction	Delays or service failures are discovered after retailers complain	Identify patterns associated with late fulfilment and route exceptions for intervention	Better service reliability and retailer retention
15. Next-best-action recommendations	Sales and distribution teams receive data but lack clear priorities	Recommend the next relevant action, such as replenishment, retailer follow-up or assortment correction	Faster execution and higher productivity per outlet

How Does AI Connect Retailer Ordering, Inventory and Distribution?

The commercial value of Omnichannel Order Management becomes clearer when the complete process is considered.

An order begins with retailer demand. It then moves through validation, inventory checks, distributor processing, fulfilment and performance measurement. Each stage generates information that can improve subsequent decisions.

AI can strengthen this process by connecting signals that would otherwise remain separated across sales, distribution and retailer systems.

The connected order lifecycle looks like this:

Step 1: Capture demand across relevant channels

Retailers place orders through an application, web interface, messaging channel or sales representative. A connected ordering layer records the transaction and preserves the relevant retailer, product and order information.

Step 2: Validate the order against business conditions

The order is checked against product availability, applicable prices, schemes, credit rules and other relevant constraints. Where integrated inventory information is available, the system can identify potential stock conflicts before confirmation.

Step 3: Apply intelligence to the order

AI can recommend relevant SKUs, suggest reorder quantities, identify unusual order patterns or flag potential stock shortages. The recommendations should reflect the business's commercial policies and the information available at that point.

Step 4: Route the order into distribution workflows

The validated order moves into the appropriate distributor or order-processing workflow. A Distribution Management System (DMS) can support downstream activities such as order processing, billing and inventory management.

The exact workflow depends on the system architecture and integrations. An OMS does not automatically guarantee real-time inventory accuracy or seamless fulfilment; those capabilities depend on the underlying data and system connections.

Step 5: Monitor fulfilment and exceptions

Order status, billing outcomes, stock discrepancies and delivery exceptions provide visibility into whether the original demand was fulfilled. Exceptions can be assigned to the appropriate team for resolution.

Step 6: Feed outcomes back into planning

Actual orders, fulfilment results and SKU movement can inform subsequent demand forecasts, retailer recommendations and inventory decisions. Over time, this feedback loop helps the business assess which interventions produce better outcomes.

What competitive advantage does this create?

The market advantage is not merely faster order entry. It is a shorter decision cycle between retailer demand, operational response and commercial learning.

A brand with connected ordering and distribution information can identify unmet demand earlier, make better-informed replenishment decisions and focus sales effort on the retailers and products that need attention.

It can also distinguish between demand-generation problems and fulfilment problems. A territory with weak sales may need better outlet activation—or it may already have sufficient demand but inadequate product availability. Those situations require different interventions.

Research from McKinsey on AI-enabled supply-chain planning describes a consumer-goods implementation that improved SKU-level forecast accuracy by 10–12%, reduced finished-goods inventory by 6–8% and increased order fill rates by 3–5%. These are results from a specific implementation, not universal outcomes or a guarantee of what an individual business will achieve.

The lesson for business leaders is straightforward: better demand intelligence creates value only when inventory, order processing and execution can respond to it.

What Are the Challenges of Implementing AI-Powered Order Management?

AI-powered Omnichannel Order Management is not primarily a model-selection problem. It is a business integration problem.

Organizations often have the necessary data somewhere in their technology estate, but the data may be inconsistent, delayed or disconnected from the workflows where decisions are made.

Challenge 1: Fragmented data across sales and distribution

Retailer records, product codes, order history and inventory information may be spread across SFA, DMS, ERP and other systems. Inconsistent identifiers or delayed synchronization can undermine both order accuracy and AI recommendations.

What businesses need: A reliable integration (iPaaS) layer, consistent master data, clear ownership of critical fields and controls for detecting synchronization failures.

Challenge 2: AI recommendations that cannot be executed

An AI system may identify an attractive replenishment opportunity, but the distributor may lack the stock, the retailer may have a credit constraint, or the relevant scheme may no longer be valid.

What businesses need: AI recommendations connected to operational rules, inventory signals and authorized actions. A recommendation should not be treated as executable simply because a model produced it.

Challenge 3: Low retailer adoption

A sophisticated retailer ordering app will underperform if it is difficult to use, requires excessive data entry or does not fit local purchasing habits.

This challenge is especially relevant in fragmented retail markets where digital maturity, connectivity, language preferences and retailer behaviour vary widely.

What businesses need: Accessible ordering channels, intuitive interfaces, relevant product catalogues, local-language support and a clear reason for retailers to use the platform repeatedly.

Challenge 4: Poor economics hidden by headline growth

A business may increase digital order volume while also increasing discounts, fulfilment costs, returns or inventory exposure. More orders do not automatically translate into more profitable growth.

What businesses need: Measurement across incremental gross margin, order fulfilment, cost-to-serve, repeat ordering and working capital—not app registrations or digital order share alone.

Challenge 5: Unclear accountability for AI decisions

Forecasting errors, incorrect recommendations and automated actions can create commercial or operational consequences. Without governance, teams may either distrust the system or follow its recommendations without sufficient scrutiny.

What businesses need: Defined decision rights, explainable recommendations where appropriate, approval thresholds, audit trails and human review for high-impact exceptions.

How FieldAssist's eB2B Retailer App addresses core operational challenges?

FieldAssist's approach is relevant to FMCG and CPG businesses because the retailer ordering experience is connected to the broader sales and distribution ecosystem rather than being treated solely as an independent ordering interface.

The eB2B retailer platform supports Android, Progressive Web App (PWA) and WhatsApp interfaces, multilingual access, offline functionality in low-connectivity zones, retailer engagement intelligence and integration with DMS, ERP and SFA.

These capabilities address several practical adoption and integration constraints.

Implementation challenge	Relevant FieldAssist capability	Business relevance
Retailers have different channel preferences	Android, PWA and WhatsApp ordering	Offers multiple ways to place orders
Language and connectivity create friction	Multilingual access and offline support	Helps accommodate diverse retail environments
Orders are disconnected from distribution operations	Integration with DMS, ERP and SFA	Connects retailer ordering with operational workflows
Retailers miss relevant schemes	Scheme visibility and retailer engagement features	Makes commercial programmes easier to access
Retailer inactivity is detected late	Retailer activity and order-pattern insights, including AI-led reactivation nudges	Helps teams identify and engage at-risk or dormant outlets
Retailer complaints require manual follow-up	Grievance and return management with status tracking	Improves issue visibility and accountability

These are capabilities, not a guarantee that every integration, workflow or AI use case will be available in every deployment. Actual outcomes depend on configuration, data readiness, operating processes and adoption.

The broader point is that AI-powered order management must work for the people and systems that execute the order. FieldAssist's integrated model addresses that requirement within FMCG sales and distribution.

_{How Can FMCG Brands Measure the ROI of Omnichannel Order Management?}

For a CEO or CGO, the relevant question is not how many retailers downloaded an app. It is whether the investment creates incremental, profitable business value. A sound ROI framework connects the intervention to the financial or operational outcome it is expected to change.

KPI	What it measures?	Why leadership should care?
Incremental net sales	Additional sales attributable to the intervention	Establishes whether growth is genuinely incremental
Incremental gross profit	Additional gross profit after relevant product costs	Tests the quality of growth
Contribution margin	Profit after relevant variable costs	Prevents unprofitable order expansion
Order fill rate	Proportion of ordered demand fulfilled	Measures fulfilment effectiveness
Order rejection rate	Share of orders rejected under the defined measurement policy	Identifies ordering or supply constraints
Active ordering outlets	Retailers placing qualifying orders in a defined period	Measures productive channel adoption
Repeat-order rate	Share of eligible retailers ordering again within a defined period	Measures sustained engagement
Average order value	Average value per qualifying order	Helps assess basket expansion
SKU breadth per outlet	Number of distinct SKUs purchased per outlet	Indicates assortment penetration
Cost-to-serve per order	Relevant operating cost divided by qualifying orders	Measures process efficiency
Inventory days	Average inventory relative to the defined consumption or cost base	Assesses working-capital efficiency
Sales productivity	Commercial output per representative or productive selling hour	Measures the value of released field capacity

What Should Businesses Look for in an Omnichannel Order Management Solution?

Most solution evaluations cover integration, scalability and reporting. Those criteria matter, but they are not sufficient. Three less obvious priorities deserve equal attention.

Priority 1: Can the system distinguish demand from fulfilment failure?

A retailer who does not order may have no demand. Alternatively, the retailer may have experienced repeated stockouts, delayed deliveries or unresolved claims and stopped trying.

A conventional dashboard may show only declining order frequency. A stronger system connects ordering behaviour with availability, fulfilment and service history to help identify the likely cause.

What to evaluate: Can the solution connect retailer behaviour, order outcomes, stock availability and service exceptions at a useful level of detail? Can it help teams distinguish a demand-generation issue from a supply or service issue?

This matters because the wrong diagnosis leads to the wrong investment. More promotions will not solve a distributor fulfilment problem.

Priority 2: Does the system optimize for profitable fulfilment, not maximum order volume?

AI can recommend larger baskets, more frequent orders and additional products. But these recommendations may be commercially unsound if they increase discount dependency, create excess retailer inventory or push products that are unlikely to sell through.

The system should support decisions that balance demand, availability, retailer needs, service constraints and product economics.

What to evaluate: Can the platform measure outcomes beyond order value? Can recommendations account for relevant stock and commercial rules? Can the business assess whether growth is incremental and profitable?

This is the difference between an order-generation engine and a commercial growth system.

Priority 3: Can AI move from insight to action without losing control?

Many businesses already have dashboards that identify underperforming territories, inactive retailers and inventory discrepancies. The remaining gap is execution: who acts, what action is appropriate, and whether it worked.

AI Order Management should support a controlled progression from detection to recommendation, execution and feedback. Routine, low-risk tasks may be automated where authorized. Financially sensitive or ambiguous decisions should retain appropriate approval and oversight.

What to evaluate: Can recommendations enter existing workflows? Are decision rights, exceptions and auditability defined? Can leaders measure whether the intervention changed the intended KPI?

A system that produces recommendations without connecting them to operational action creates another layer of work. A system that acts without controls creates a different problem.

The most capable retail order management system is not necessarily the one with the largest feature list. It is the one that reliably connects commercial intent, operational execution and measurable business outcomes.

FieldAssist eB2B Retailer App: Connecting Omnichannel Ordering with Sales, Distribution and AI

For FMCG and CPG brands, Omnichannel Order Management has a specific operational requirement: retailer demand must connect with distributor execution and the field sales organization.

A standalone ordering interface may digitize order capture. It does not, by itself, ensure that orders are commercially relevant, inventory-aware, processed correctly or followed through to fulfilment.

FieldAssist addresses this requirement through its eB2B Retailer App, integrated with Sales Force Automation (SFA), Distribution Management System (DMS) and the AI-powered NOVA retail assistant.

The distinction is important. FieldAssist provides omnichannel B2B ordering and fulfilment-related capabilities within its sales and distribution ecosystem. This is a different proposition from claiming that it is a standalone, general-purpose OMS for every retail model, including all D2C, marketplace and point-of-sale scenarios.

eB2B Retailer App: Making ordering accessible

The retailer app supports ordering through Android, PWA and WhatsApp interfaces, with multilingual access and offline support in applicable workflows.

Retailers can place orders, access product and scheme information, review relevant engagement features and raise grievances or returns through supported functionality.

For brands, this creates additional opportunities to capture retailer demand outside scheduled field visits. The actual benefit depends on retailer adoption, product availability and the quality of the ordering experience.

SFA: Connecting retailer demand with field execution

SFA supports the field sales organization by connecting outlet interactions, order capture and sales execution workflows.

When retailer ordering and SFA operate as part of a connected ecosystem, sales representatives can use available retailer and order information to prioritize visits, identify assortment opportunities and focus on activities that require direct intervention.

The goal is not to eliminate field sales. It is to make field sales more productive by reducing dependence on manual order capture for routine transactions.

DMS: Connecting orders with distributor operations

DMS provides the distribution layer through which relevant order-processing, inventory and billing workflows are managed.

Integration between retailer ordering and DMS can connect order capture with distributor-side processing and inventory validation. This improves the potential for a more coherent view of what retailers request and what distributors can supply.

The exact sequence, stock visibility and fulfilment capabilities depend on the implementation and connected systems.

NOVA: Bringing AI into retailer engagement and ordering

FieldAssist's AI-powered conversational AI retail assistant adds an intelligence layer to the retailer and distribution experience. Relevant capabilities described across FieldAssist's product materials include conversational assistance, SKU recommendations, reorder intelligence and demand-related insights.

These capabilities can help reduce ordering friction and surface opportunities that might otherwise remain hidden in historical transactions or disconnected reports. Specific functions and automated actions should be validated against the intended deployment.

The combined business value:

Component	Primary role	Contribution to the connected model
eB2B Retailer App	Omnichannel retailer ordering and engagement	Makes ordering and relevant retailer interactions more accessible
SFA	Field sales execution	Connects outlet-level activity with sales opportunities
DMS	Distribution and order processing	Connects orders with distributor operations
NOVA	AI-powered retail assistance	Supports intelligent recommendations and more responsive retailer interactions

Together, these components support a connected model in which retailer demand can be captured across channels, processed through distribution workflows, informed by sales execution and improved through AI-driven intelligence.

Explore FieldAssist's eB2B Retailer App to understand how omnichannel ordering, sales execution, distribution management and AI-powered retail assistance can work together within an FMCG-focused ecosystem.

Frequently Asked Questions

1. What is Omnichannel Order Management?

Omnichannel Order Management coordinates orders placed through multiple channels and connects them with relevant order processing, inventory, fulfilment and tracking workflows. In FMCG, this can include retailer applications, web interfaces, messaging channels, sales representatives and distributor systems.

2. How does AI improve Omnichannel Order Management?

AI can forecast demand, recommend replenishment quantities, personalize SKU and promotion recommendations, identify unusual order patterns and help prioritize operational interventions. Its business value depends on data quality, integration and whether recommendations lead to measurable improvements in sales, fulfilment or profitability.

3. What is the difference between an OMS and a retail order management system?

An Order Management System (OMS) manages the lifecycle of orders, including validation, processing, routing and status visibility. A retail order management system applies these capabilities to retail-specific workflows. The scope varies by vendor: some systems coordinate enterprise orders across stores, warehouses and marketplaces, while FMCG-focused solutions may concentrate on B2B retailer ordering, distributor operations and field sales integration.

4. How can FMCG companies measure the ROI of AI Order Management?

FMCG companies should measure incremental contribution margin, order fill rate, stockout-related lost sales, active ordering outlets, repeat-order rate, cost-to-serve and inventory efficiency. A controlled pilot with a clear baseline and attribution method helps establish whether observed improvements are genuinely incremental.

5. Does FieldAssist provide Omnichannel Order Management?

FieldAssist supports omnichannel B2B retailer ordering through its eB2B Retailer App, with relevant sales and distribution workflows connected through SFA and DMS and AI-powered assistance through NOVA. This supports order capture and fulfilment-related processes within an FMCG-focused ecosystem. It should not be interpreted as a claim of universal OMS coverage across every D2C, marketplace and retail POS use case.

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