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    Home » How a furniture retailer automated order confirmation processing
    AI Technology

    How a furniture retailer automated order confirmation processing

    ProfitlyAIBy ProfitlyAIApril 24, 2025No Comments9 Mins Read
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    Picture by Jessie McCall / Unsplash

    Promoting custom-made furnishings on this age of mass manufacturing is just not straightforward. However this mid-sized, Europe-focused furnishings retailer was making it work. Their secret? Letting prospects select all the pieces — from material decisions to couch leg types, even right down to the colour of ornamental nails.

    Nevertheless, as gross sales grew, the made-to-order created a significant drawback. Every order was distinctive — totally different materials, {custom} options, and particular necessities. The staff needed to rigorously deal with every customization, create detailed specs for suppliers, and guarantee each particular request was appropriately manufactured. When suppliers despatched again order confirmations, the true problem started.

    With an 8-week order cycle, processing delays meant prospects waited with out updates. Order backlogs grew, unfavourable evaluations elevated, and 20-30% of all orders have been experiencing some type of error or concern. They wanted a method to course of these paperwork precisely with out hiring extra employees.


    The true price of handbook provider order administration

    The provider paperwork arrived as complicated PDFs — some as much as 16 pages lengthy, in a number of languages, and with totally different technical notations. Their seven-person operations staff spent 10-15 hours per week per particular person processing these paperwork. That is 70-105 hours weekly simply matching codes and verifying particulars.

    At a mean hourly price of 180 SEK for operations specialists in Sweden, the handbook processing was costing them roughly 655,200-982,800 SEK (€59,600-€89,400) yearly in direct labor prices.

    On prime of that, the handbook course of resulted in 20-40 order errors throughout 100-150 month-to-month orders. It meant both the shopper needed to be compensated or the inaccurate merchandise needed to be bought off at a loss. The potential losses as a result of incorrect order might find yourself costing €12,000 month-to-month.

    To sum up, the processing inefficiencies had main downstream results:

    • Clients left ready for updates about their orders
    • Incapacity to offer correct supply timelines
    • Rising unfavourable evaluations particularly mentioning poor communication
    • Time spent correcting errors managing and promoting off returns
    • Alternative price of expert staff members doing handbook work
    • Further hiring wants as order volumes grew

    Automation appeared like the plain answer. Nevertheless, their distinctive processes and excessive degree of customization meant they needed to discover a system that would deal with their particular wants. They wanted one thing that would not solely course of complicated PDFs precisely but in addition adapt to their cautious, detailed verification course of whereas working seamlessly with their present programs.


    Why conventional order affirmation processing failed the retailer

    Let’s check out how this retailer’s order dealing with workflow seemed like.

    • Buyer locations order on their web site
    • Order flows by their e-commerce system, Crystallize, into their ERP, Enterprise Central, as a gross sales order
    • Staff manually collects these orders 2-3 instances weekly and makes use of inside filters to pick the suitable provider for the order
    • Creates Excel recordsdata for suppliers detailing what must be manufactured
    • Suppliers ship again order confirmations confirming what they’ll make and after they can ship
    • Order affirmation information is manually extracted and matched to gross sales orders
    • Order affirmation quantity and promised supply dates are added to Enterprise Central for matched objects
    • When all objects in an order are matched, prospects are knowledgeable the tentative supply information
    • As soon as the product is in manufacturing and transit, suppliers ship packing lists
    • Staff verifies these towards gross sales orders to make sure every merchandise is being readied
    • Continia is used to create POs from these packing lists
    • These assist mark gross sales orders for supply and schedule product launch of their warehouse administration system

    This complicated course of created a wierd workflow the place buy orders have been created after receiving packing lists slightly than earlier than putting orders with suppliers. This uncommon method was vital as a result of they collected web site orders on particular days earlier than sending them to suppliers. Moreover, not all objects in a buyer order would go to the identical provider, which means components of a single order would possibly arrive at totally different instances.

    Moreover, three extra components made this course of notably tough to automate:

    Variations in product listings

    Since customization was on the core of their enterprise, they wanted to trace every part of a {custom} order individually. So a {custom} couch received’t be recorded as a single merchandise in Enterprise Central however as separate line objects — one for the couch mannequin, one other for the material alternative, and extra for particular options like bronze nails.

    Nevertheless, the provider usually lists all these particulars as a single merchandise of their order affirmation. For instance, if a provider confirmed ‘Valen three-seater in Blue material with bronze nails‘, the staff must match this single entry to a few separate traces in Enterprise Central. This complicated construction made processing order confirmations notably difficult.

    Language and notation variations

    The furnishings firm’s suppliers used totally different languages and technical notations of their paperwork. One provider used English with German technical notations, whereas one other blended Swedish and English phrases. This made matching with gross sales orders notably difficult as a result of Enterprise Central wanted clear, standardized product codes.

    So, even one thing easy like metal nails might seem in a number of methods — as a technical code in a single doc, in plain English in one other, or as a German notation in a 3rd. The staff needed to manually interpret and translate these variations throughout information entry to make sure correct matching. 

    Particular case dealing with

    Some orders required particular dealing with guidelines. As an example, when an order affirmation was marked as ‘Showroom’ as an alternative of getting a buyer reference, it wanted totally different processing because it wasn’t tied to a buyer order. 

    The staff needed to first spot these distinctive instances, then apply totally different verification guidelines — including extra steps to their handbook processing. This meant always switching between totally different procedures relying on the kind of order they have been dealing with.

    Break up orders

    Clients might order objects that got here from totally different suppliers. For instance, a buyer would possibly order a settee from one provider and a footstool from one other. 

    So when order confirmations arrived, the staff needed to rigorously match every to the fitting components of the shopper’s order in Enterprise Central. Since confirmations got here individually from totally different suppliers, they wanted to trace which objects have been confirmed and which have been nonetheless pending — all whereas making certain they have been updating the right line objects for every product.

    They tried varied instruments, together with Continia, however they could not successfully deal with these complicated paperwork whereas sustaining the accuracy their course of demanded. They wanted a versatile answer that would precisely extract and interpret inflexible, prolonged PDFs whereas adapting to their particular workflow wants.

    That is after they approached us at Nanonets.


    How we automated the retailer’s order affirmation processing workflow

    Taking a look at their complicated order dealing with course of, we knew automation wanted to occur step-by-step. We began with order confirmations. We constructed a versatile workflow that would automate the method from receiving provider paperwork to updating Enterprise Central with supply dates. The concept was to make use of this as a basis for different potential workflows, like packing listing processing.

    This is a fast overview of how the automated workflow labored:

    • Staff forwards order confirmations to a devoted e-mail handle or uploads them to a Dropbox folder
    • Our system identifies the provider format and applies related processing guidelines
    • For every doc, our mannequin:
      • Extracts order references, product particulars, and supply dates
      • Finds corresponding gross sales orders in Enterprise Central
      • Maps provider product descriptions to appropriate Enterprise Central codes
      • Identifies particular instances like showroom orders
    • Nanonets flags objects needing evaluate:
      • Amount mismatches
      • Product code discrepancies
      • Unmatched objects
    • Staff evaluations flagged objects by easy interface
    • System learns from corrections
    • As soon as verified, updates Enterprise Central with supply dates

    Right here’s how we went about fixing totally different challenges of their doc processing workflow:

    1. Automated doc consumption:

    We established dependable doc consumption channels by configuring e-mail forwarding guidelines and establishing Dropbox folder monitoring. This eradicated the handbook downloading, sorting, and classifying of order confirmations. The system routinely detects new confirmations and routes them for processing.

    2. Product matching:

    The most important problem was matching provider product descriptions to a number of Enterprise Central line objects. 

    So, we constructed an identical system that:

    • Begins with actual description matching
    • Falls again to fuzzy matching when wanted
    • Filters outcomes primarily based on further standards like upholstery codes
    • Handles the “one-to-many” drawback (one provider merchandise to a number of Enterprise Central traces)

    When a provider lists “Valen three-seater in Blue material with bronze nails” as a single merchandise, our system can now routinely determine and replace the corresponding couch mannequin, material, and particular function traces in Enterprise Central.

    3. Provider-specific guidelines

    Every provider’s paperwork required {custom} dealing with:

    • For provider A: The system extracts article numbers and variant codes from product descriptions, checking “Choices” fields for particular options like ornamental nails
    • For provider B: The system handles blended Swedish-English phrases and matches primarily based on product descriptions and portions
    • For each: Further verification steps for upholstery codes, types, and particular notes

    4. Managing exceptions

    To deal with their particular instances, we constructed particular detection and processing guidelines:

    • System identifies showroom orders routinely
    • Handles cut up orders by monitoring a number of confirmations
    • Processes particular product codes with particular guidelines
    • Flags exceptions that want human evaluate

    The interface lets the retailer evaluate these exceptions effectively. After they make corrections, the system learns from these modifications — bettering future extraction and matching accuracy.


    The ROI of automated provider order administration

    Inside 3-4 months, the automated system delivered measurable outcomes throughout 4 important areas:

    • Processing time lower from 70-105 to 40-50 hours weekly
    • Full elimination of order backlogs
    • Potential to deal with rising order volumes with out further employees
    • Built-in with Klaviyo for automated buyer communications
    • Proactive order updates all through the 8-week order cycle
    • Fewer unfavourable evaluations and buyer inquiries
    • Early detection of product mismatches earlier than manufacturing
    • Month-to-month financial savings of €12,000 from error prevention
    • Operations staff shifted to value-creating actions
    • Enlargement into the German market with the identical workflow

    What moved the needle most for the retailer? Our system’s means to precisely course of complicated PDFs – it’s one thing they did not count on could possibly be carried out successfully. Even 16-page paperwork with blended languages and technical notations are actually processed precisely.

    They’re additionally planning to increase the automated workflow to their Germany-region operations because the course of would stay the identical, kind of. The one distinction can be the language – one thing that Nanonets would be capable to deal with seamlessly.



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