Background & Opportunity
Kinga Mathe’s growth story began with a product and a point of view rather than with a technology plan. In 2007, founder Kinga Mathe designed a Dirndl for a traditional-clothing ball in Kitzbühel. That design became the origin of a premium Trachten brand. Over time, the business expanded beyond a single product identity. The official brand presentation describes the Kinga Mathe label, the Alissa by Kinga Mathe and Maxim by Kinga Mathe lines, three company-owned locations, and the official Tracht-à-Porter online store. The business also served B2B customers and created customized uniforms for hotels and catering businesses. Its main markets included southern Germany, Austria, and Switzerland. This model created meaningful commercial variety. A direct online order did not behave like a store transaction. A B2B customer might order larger quantities, while a hotel or catering uniform could involve customization. The online store required accurate availability for individual sizes and colors. Stores required point-of-sale coordination. Returns required a reliable status change, not merely a note in a separate file. Accounting required transactions to arrive in a usable form. The company was therefore managing a network of connected activities, even when its software did not yet connect them. At the beginning, the old approach was understandable. Kinga Mathe had only a small number of weekly orders, and Excel combined with manual picking could support that level of activity. Human memory and direct checking can compensate for weak systems when the assortment is narrow and exceptions are limited. The operating model becomes fragile when a brand adds variants, channels, and customers faster than it adds reliable data controls. That inflection point appeared after Alissa by Kinga Mathe launched in 2019. The range attracted rapidly increasing demand, including customers who were more price-sensitive than the earlier core audience according to the case description. The team had to continue producing during the selling period. Orders and SKUs increased, while the old process remained dependent on spreadsheets, paper orders, and manual warehouse work. What had once been a flexible process became a source of delay and error. The first problem was inventory confidence. Products could remain visible as available after they had sold out. In a multi-channel business, that error is more serious than a spreadsheet inconvenience. A customer can place an order for a product that is not actually available. A store can commit stock needed for an online order. A B2B reservation can be overlooked because it sits outside the consumer order process. The more variants the company carries, the more opportunities there are for a size or color mismatch. The second problem was fulfillment effort. Orders had to be printed, picked manually, and handled through paper-based records. The case reports that one order required about 20 minutes of processing in the earlier process, while the later comparison describes fulfillment processing falling from 30 minutes to 10 minutes after implementation. The precise comparison comes from the supplier case and uses its own process definition, but both figures point to the same operational issue: too much staff time was spent moving information and correcting exceptions instead of completing customer-facing work. The third problem was unreliable returns information. Software used during 2021 and 2022 could not fully support quotations, barcodes, logistics tracking, and the return of cancelled orders. It displayed a return rate of 70%, even though the warehouse had not received the corresponding returns. This must not be interpreted as a verified 70% consumer return rate. It was a system-reported anomaly, effectively a ghost-return problem. Still, the discrepancy made a critical point. If a company cannot reconcile what the system says with what the warehouse physically receives, it cannot confidently plan stock, cash, customer service, or financial processing. The fourth problem was fragmentation across channels. Shopify, company-owned stores, B2B orders, logistics, returns, and accounting each had information that the others needed. When systems do not synchronize, employees become the integration layer. They export spreadsheets, print documents, re-enter data, ask colleagues to confirm stock, and correct mistakes after an order has already moved. That work is difficult to scale because every additional product, employee, location, and channel adds more possible handoffs. The management decision was therefore framed around operational requirements. In December 2022, Kinga Mathe tested Xentral and decided in early 2023 to use it as the central operating platform. The requested capabilities were reliable two-way Shopify synchronization; SKU, size, and color variant management; cloud access; automatic backups; shared inventory for B2C and B2B; and automated returns. These criteria directly matched the failure modes. The objective was not simply to replace Excel with a more modern screen. It was to create a consistent flow from order to stock movement, fulfillment, return, and financial processing. BDG Solutions was responsible for implementation and customization. The case says the system went live in about 30 days. Speed reduced the period in which the company would have to maintain both old and new processes. It also reflected the need to improve operations while commercial activity continued. The implementation brought Shopify, POS, inventory, returns, logistics, and accounting data into a shared structure. A customized POS module and a returns-scanning process were added so that the system could fit the company’s actual operating needs. The distinction between integration and standardization is important. The business did not need every order to follow an identical path. A B2C Shopify order, a store sale, a B2B package, and a customized uniform order can legitimately require different information and handling. What they needed in common was dependable master data and visible stock movement. A sale should reduce the right inventory. A cancellation should return the right status. A physical return should be scanned and recorded. A fulfillment event should be traceable. A financial transaction should not need to be recreated from paper. The reported results indicate that this operating foundation removed substantial friction. According to Xentral’s customer case, revenue grew about threefold between 2023 and 2024. The company did not increase fulfillment capacity during that growth, according to the case. Fulfillment time dropped from 30 minutes per order to 10 minutes. The case gives a practical comparison: three employees could complete 150 orders in one day, while the previous process required two employees for approximately one week. The comparison is useful for understanding the operational change, but it should not be presented as independent productivity research. The main quantitative source is the solution provider. The scale of assortment also changed the significance of process reliability. After implementation, the company could manage about 10,000 product variants. That number includes the complexity of sizes, colors, and other product combinations; it is not a claim about 10,000 entirely separate designs. During the busy season, the company could process up to 1,000 B2B packages per month. This is a stated peak or busy-season capacity, not an annual average. Together, the figures show a business capable of supporting a wide product structure and meaningful B2B volume without returning to the same manual coordination burden. Financial administration improved as well. The case reports a 20% reduction in financial-processing workload. Paper costs declined by about €2,000 per month. The Shopify Connector also removed an intermediate logistics service provider and produced an additional saving of about €500 per month. These savings matter because they affect recurring operating expense, not only one-time implementation effort. They also demonstrate that digital transformation can create value in administrative processes even when the primary business objective is faster and more accurate fulfillment. Returns scanning reportedly reduced the error rate to zero. The claim is most useful when connected to the original problem. The issue was not simply that returns took too long. The issue was that system status and warehouse reality had diverged. A scanning workflow created a more direct link between physical receipt and digital record. That connection can reduce ambiguity for inventory, customer service, and accounting. It does not mean that every future exception becomes impossible. It means the process has a clearer control point. The revenue result requires the most careful interpretation. The case states that revenue grew about threefold from 2023 to 2024, but public information does not provide absolute revenue, profit, or complete audited financial statements from Kinga Mathe. Source notes also identify different wording across Xentral versions: the English page presents threefold growth from 2023 to 2024, while a German version uses relative descriptions involving 2023 and 2024. This master uses the clearer English formulation and preserves the uncertainty. It would be inaccurate to say that the ERP independently caused every euro of growth. Product demand, brand development, store activity, B2B business, marketing, seasonal conditions, and management decisions may also have mattered. The same caution applies to productivity claims. A fall from 30 to 10 minutes is a strong reported operational improvement, and the reduction in financial-processing workload is measurable evidence. However, the published figures come mainly from a vendor customer case. They should be treated as management or supplier-case evidence rather than audited causal conclusions. A rigorous internal evaluation would compare the same process definitions before and after implementation, separate order types, measure seasonal effects, and track error rates over time. The available case does not provide that complete independent evaluation. Even with those limitations, the strategic lesson is clear. The company identified recurring points where growth created risk: stock synchronization, returns, fulfillment, POS, and accounting. It selected an ERP against those requirements and used an implementation partner to adapt the platform. Buying software without mapping the process could have reproduced bad data in a new system. Mapping the process without implementing shared controls would have left the company dependent on manual discipline. The value came from combining platform capability, integration, and workflow change. The transformation also altered the meaning of capacity. Before Xentral, additional sales created additional administrative work and exception risk. After implementation, the same sales increase could be processed with less time per order. That does not mean the business became independent of people. Staff still needed to maintain product data, handle physical goods, resolve unusual returns, serve B2B customers, and manage seasonal demand. Automation changed where their effort was spent. Less time went into copying information and searching for the correct status; more capacity became available for work requiring judgment and service. The company’s channel mix made this shift especially valuable. D2C, stores, B2B, and customized uniforms can complement one another, but only if the business can see commitments across them. A shared B2C and B2B inventory view helps reduce the risk that one channel promises stock already allocated to another. POS integration connects a store transaction to the same product data used online. Returns integration helps ensure that stock is not treated as available before the physical item is received and checked. Accounting integration reduces the cost of reconstructing transactions after the fact. There are continuing risks. A high-variant fashion assortment requires disciplined master-data management. Sizes, colors, barcodes, product status, and stock movements must remain accurate. ERP automation does not automatically correct an incorrect product record. Seasonal demand can still create peak workload and cash-flow pressure. Returns can still affect availability and customer satisfaction. The business also has dependence on Shopify, Xentral, DHL, and its implementation partner. A service interruption, integration failure, policy change, or price increase could affect the operating model. Dependence is not a reason to reject integration, but it is a reason to maintain contingency procedures and monitor service quality. Channel dependence should be watched as well. The online store is described as a major revenue channel, while company-owned stores and B2B activity provide additional routes to market. Diversification can improve resilience, but it can also increase coordination requirements. If a brand expands stores or B2B volume before its data and fulfillment processes are ready, it may recreate the original bottleneck in a different form. The correct sequence is to understand the capacity of the shared operating foundation before adding new commitments. Kinga Mathe’s case is therefore less about a software brand than about timing. Excel was not inherently wrong. Manual picking was not inherently wrong. Paper records were not inherently wrong at a very small order volume. They became wrong for the scale, assortment, and channel complexity that followed the 2019 product launch. The decision to change came when the cost of informal coordination exceeded the cost and effort of integration. For other fashion businesses, the first step should be an operational map. Identify every place where an order is created, stock is reserved, goods are picked, packages are shipped, returns are received, cancellations are recorded, and financial data is processed. Then identify which events need to be visible in every channel. The second step should be measurement: fulfillment minutes, inventory errors, return mismatches, paper cost, administrative workload, and peak B2B volume. The third step should be platform selection against those requirements rather than against a generic digital-transformation checklist. The final lesson is to separate growth evidence from growth attribution. Kinga Mathe’s reported results are substantial: about threefold revenue growth from 2023 to 2024, fulfillment falling from 30 to 10 minutes, 20% less financial-processing workload, approximately €2,000 less paper cost per month, the ability to manage about 10,000 product variants, and busy-season capacity of up to 1,000 B2B packages per month. These figures show the scale of the reported outcome. They do not prove that Xentral alone caused the revenue increase. A credible case study can recognize operational improvement while stating the evidence limitation plainly. The transformation’s durable contribution was a shared operating rhythm. Shopify, stores, B2B customers, warehouse activity, returns, logistics, and accounting no longer had to be coordinated primarily through spreadsheets, paper, and memory. That rhythm gave a premium Trachten brand more room to handle demand without multiplying exceptions. When product and channel complexity grow faster than manual controls, integration becomes a condition for scalable service, not an optional technology upgrade.
Original Problem / Model
Kinga Mathe’s problem was not a lack of demand. The 2019 Alissa launch increased demand, product variety, and the need to replenish while selling. Excel, paper orders, manual picking, and disconnected systems then produced inventory errors and a 70% system-reported return anomaly. Products could remain displayed as available after selling out, while cancellations, logistics updates, and physical returns were not reliably reflected across channels. Because the business combined Shopify, company-owned stores, B2B orders, and customized hotel and catering uniforms, each unshared stock or order update could affect another channel. Employees became the integration layer, printing documents, reconciling spreadsheets, checking stock manually, and correcting exceptions after orders moved. The operational question became how to increase volume without increasing errors, administrative workload, and fulfillment time at the same rate. The reported return figure was not a verified consumer return rate, but a system anomaly showing that digital records and warehouse reality had diverged.
Turning Point
The company selected Xentral after testing it in December 2022 and deciding in early 2023. The requirements directly matched the operating failures: reliable two-way Shopify synchronization, SKU, size, and color variant management, cloud access, automatic backups, shared B2C and B2B inventory, and automated returns. BDG Solutions implemented the platform in about 30 days, allowing the business to change its operating foundation while commercial activity continued. Shopify, POS, inventory, returns, logistics, and accounting were connected in a shared structure. Customized POS and returns-scanning workflows were added for the company’s actual processes, linking physical stock movements and returned goods with digital records. The objective was not simply to install another financial system, but to replace paper and spreadsheet coordination with dependable order, inventory, fulfillment, return, and accounting workflows.
Core Decision
The implementation created a shared operating base rather than separate channel records. Stock changes, cancellations, returns, fulfillment, and financial processing could follow connected workflows. This reduced dependence on printed documents, spreadsheet reconciliation, and employee memory while preserving different handling for D2C, store, B2B, and customized-uniform orders.
Execution / Model / Acquisition Changes
The reported outcome was substantial. Revenue grew about threefold between 2023 and 2024, according to Xentral’s customer case. Fulfillment time declined from 30 minutes to 10 minutes per order. Three employees could complete 150 orders in one day, compared with two employees needing approximately one week previously. The company could manage about 10,000 product variants and process up to 1,000 B2B packages in a busy month. Financial-processing workload fell 20%, monthly paper cost declined about €2,000, and removing an intermediate logistics service reportedly saved another €500 per month. Returns-scanning errors reportedly fell to zero. These figures are supplier-case evidence, not independently audited outcomes.
Measurable Results
The case supports a strong operational conclusion but a cautious financial conclusion. Integration clearly addressed the reported problems of inventory visibility, returns reconciliation, manual fulfillment, and paper administration. However, public materials do not disclose absolute revenue, profit, or audited before-and-after statements. Xentral is also the primary source for the quantitative results, and different language versions describe revenue development somewhat differently. Product demand, brand activity, stores, B2B sales, seasonality, and management decisions may have influenced growth. The ERP should therefore be credited as an enabling operating foundation, not presented as the sole proven cause of revenue growth.
Lessons
The transferable lesson is to digitize when manual coordination becomes the growth constraint. Map all channels and physical workflows, define the stock and return events that must synchronize, select technology against those requirements, and implement with measurable controls. Track fulfillment time, inventory accuracy, return mismatches, administrative workload, paper expense, and peak B2B volume after launch. Integration creates capacity, but disciplined product data, contingency planning, and independent measurement are necessary to preserve it.