Inventory to Impact: SKU Discipline and Working‑Capital Resilience at Titan Watches

2025 Kiruthika G, Dr. Shakeela Banu C 20 © International Academic Research Journal of Business and Management ISSN:2227-1287 Volume 13, Issue No:2, December 2025 DOI: https://doi.org/10.5281/zenodo.17695687 Inventory to Impact: SKU Discipline and Working‑Capital Resilience at Titan Watches Kiruthika G MBA Students, Faculty of Management Studies, CMS Business School, Jain (Deemed-to-be University), Bengaluru. Dr Shakeela Banu C Adjunct Faculty, Faculty of Management Studies, CMS Business School, Jain (Deemed-to-be University), Bengaluru. Abstract Titan's Watches and Wearables division works in a rapidly growing Indian market where a lot of choices draw customers, but slow-moving items can tie up cash. An internship study of FY21 FY25 FP A data surfaced SKU proliferation production sales mismatches and ageing inventory tying up working capital especially across 10K 10 25K and 25K price bands 10K 10 25K and 25K price bands Even though the flags for aging and excess production got better by FY25, there are still risks because stock-to-sales ratios are still high. The problem is how to make portfolio and flow discipline a part of the company's culture so that breadth doesn't hurt margins or the brand. Exhibits include the conversion of opening stock into aging buckets, production sold in the same year, and stock to sales by band. Options include SKU rationalization, production aligned with demand, dynamic liquidation ladders, cross-functional dashboards, and a rolling AI-assisted forecast. The goals of learning are to find the root causes, design governance, decide whether to stop or continue, redesign, and figure out the trade-offs between working capital. Keywords: Inventory optimisation, SKU rationalisation, Working capital, Demand forecasting, Smartwatch strategy CASE INTRODUCTION India's organized watch market combines fashion trends with long-lasting use, making it hard to build a portfolio. Not enough variety makes things less relevant, while too much slows down sales and clutters shelves. In Titan's Watches and Wearables division, FY21 FY25 FP A signals show that there is too much SKU proliferation in certain price ranges and old stock that is tying up working capital. By FY25, corrective actions were evident: aging diminished, excess production indicators declined, and opening stock conversion improved across the 10K, 1025K, and 25K segments. However, stockto-sales imbalances continued for a protracted list of items. Research on inventory shows that productivity depends on both turns and gross margin. Companies that align their assortment and replenishment do better than others. Gaur Fisher Raman Two thousand five Netessine Rumyantsev 2007 Studies on retail execution show that incorrect records and process drift can make decisions less accurate and increase the risk of markdowns. DeHoratius Raman 2008 Forecasting literature also says that demand that is sensitive to fashion and comes and goes needs simple models and strict rules instead of complicated overfitting. Syntetos Boylan 2005 This case puts students in Titan's operating seat with exhibits on how old buckets affect band opening stock, how much stock is sold within a year, and how much stock is sold compared to sales. They need to figure out what causes variety to turn into working capital drag and then come up with a way for the institution to respond by redesigning rules for people who aren't doing well. SKUs need production slots that are in line with demand, dynamic liquidation ladders that protect the brand while freeing up cash, and a rolling forecast ritual that ties design FP. A supply and a store It is common for managers in lifestyle retail to 21 International Academic Research Journal of Business and Management December feel stressed about how to keep design flexibility and variety while enforcing flow discipline. The best way to use inventory is to treat it like a strategy that is visible. This means creating a cross-functional cadence where dashboards show age, profitability, and sell-through experiments test forecast and allocation hypotheses, and governance stops people from going back to bad habits. Students will use basic math and sensitivity bounds to figure out how a five-point increase in opening stock sellthrough affects cash conversion and markdown expense over two quarters. The ultimate objective is to create a playbook that converts analytics into operational decisions amid uncertainty, in alignment with evidence regarding assortment discipline, forecast parsimony, and execution quality (Gaur et al. 2005; DeHoratius & Raman 2008). About the Watches and Wearables industry Watches and wearables for business India The market is worth between 13,000 and 15,000 crore, and it is growing at a rate of between 1% and 2% per year. It includes analog, digital, hybrid, and smartwatches that are getting faster. Urbanization, income effects, gift-giving spikes, and fashion cycles all affect demand. In the entry to mid-range segments, smartphones are replacing utility. Swiss and Japanese brands, as well as tech companies and nimble Indian newcomers, are all competing on a global scale. This raises the bar for speed of design and execution in channels. Operationally, expanding the variety of products is difficult because of uncertain demand and long supply cycles. The literature shows that having a lot of different styles in fashion can lead to more mismatches and markdowns, unless quick response and disciplined restocking are used to control it. Cachon Swinney 2011 Inventory productivity depends on balanced turns and margin. Gaps between production and sell-through that last for a long time put a strain on working capital. Gaur Fisher Raman 2005 Execution errors, ranging from inaccurate records to inadequate shelf discipline, compromise signals employed for allocation and forecasting, resulting in self-reinforcing distortions. DeHoratius Raman 2008 In forecasting, fashion-sensitive items frequently demonstrate intermittent or bursty demand. Empirical studies advocate for straightforward, bias-aware methodologies that ensure stability over time, supplemented by governance rather than solely relying on model complexity. Syntetos Boylan 2005 When liquidation is necessary, markdown science indicates that timing and depth must be adjusted to safeguard the brand while eliminating obsolete inventory. Smith Achabal 1998 Finally, portfolio renewal, which is the rate at which SKUs that aren't selling well are retired or redesigned, is becoming a strategic tool to keep things relevant without tying up cash. Problems in Watches and Wearables industry Sector frictions are caused by the conflict between unpredictable demand and long replenishment cycles. SKU proliferation increases the likelihood of slow-moving items that tie up working capital, while smartphone substitution reduces demand for mid-range analog watches. Gaps in execution mistakes in inventory records Inconsistent allocation and channel spillovers further distort true sell-through, which weakens forecast baselines. DeHoratius Raman 2008 Inventory productivity studies indicate that retailers with consistent discrepancies between production receipts and sellthrough rates encounter diminishing turnover and increasing reliance on markdowns. Gaur Fisher Raman 2005 Rumyantsev Netessine 2007 Forecasting 2025 Kiruthika G, Dr. Shakeela Banu C 22 is made more difficult by intermittent and seasonal changes in fashion. Using rich models without governance often overfits noise and makes bias worse compared to simpler methods. Syntetos Boylan 2005 Finally, liquidation practices that don't take brand diversity into account hurt equity, while cash markdown design must think about timing, depth, and channel to avoid sending the message that quality is going down. Smith Achabal, 1998 In short, the industry's biggest problem is not getting tools but following institutional discipline rules that turn analytics into production slots for different types of products and liquidation for different channels, all while keeping a credible renewal rhythm for people who aren't doing well. gives the right context. About Titan Company Limited Titan Company Limited is a Tata TIDCO venture based in Bengaluru. It helped India switch to branded quartz watches and now leads the organized watch market with its own brands, such as Fastrack, Sonata, Raga, and Edge. It is also expanding into jewelry, eyewear, fragrances, and ethnic wear. Within Watches and Wearables, growth has been fueled by good design, a wide range of prices, and a presence in many channels, such as company-owned stores, largeformat retail, and e-commerce partners. The internship led FP An analysis of FY21 to FY25 shows that too many SKUs and mismatched production and sales contributed to inventory aging and working capital being tied up. Management actions reduced aging, lowered excess production flags, and improved opening stock conversion across the 10K, 10K, 25K, and 25K bands. Production sold within the year also rose for premium SKUs. There were still a lot of items with high stock-to-sales ratios, which suggests that there is still a mismatch between the breadth and speed of design. The main question is how to make discipline a part of the organization so that improvements last, and which SKUs to stop, keep, or redesign. how to connect production slots to demand signals, which liquidation ladders keep the brand while freeing up cash, and how to run a rolling forecast ritual that ties design FP A supply and a store The case establishes a quarterly governance rhythm based on dashboards that reveal age profitability and sales performance, accompanied by clear regulations for retirement and redesign, as well as a gradual transition into hybrids and smartwatches, where speed and profit margins justify investment. Problems faced by Titan Company Limited Baseline FP A signals showed that older stock was concentrated in certain price ranges, that opening inventory was slow to sell, and that there were gaps between production and in-year sales, especially for premium launches. More SKUs made things more complicated and less focused, while stock-to-sales ratios showed that working capital was stuck in the long tail. Execution problems, inaccurate records, inconsistent allocation across channels, and uneven liquidation practices made forecasts more noisy and delayed corrective actions. Even though FY25 metrics got better, with less aging, less excess production, and stronger opening stock conversion, there were still some areas of overhang where design breadth was faster than demand velocity. Without institutionalized rules, improvements are at risk of going backward, underperformers stay, production keeps going based on old assumptions, and markdowns pile up late. What we need right now is an operating system that is backed by governance and sets stop, continue, and redesign thresholds by band, links production slots to rolling demand signals, sets channelspecific liquidation ladders, and runs a 23 International Academic Research Journal of Business and Management December weekly review that combines data with action. Success will be clear in faster sales, less aging, better cash flow, and stable margins during portfolio renewal cycles. Key strategies The strategy brings together portfolio discipline, realistic demand, and control of working capital through a single operating cadence. First, Titan will streamline its assortment by making quarterly stop–continue–redesign calls based on ABC profitability, aging, and stock-to-sales signals. This will get rid of items that have been underperforming for a long time and redesign items that are close to missing the mark. Evidence shows that better inventory productivity is linked to tighter assortment-replenishment alignment (Gaur, Fisher, & Raman, 2005; Rumyantsev & Netessine, 2007). Second, production will be aligned with demand through a rolling, AI-assisted forecast that focuses on parsimony for intermittent items and is checked for stability. This is based on the idea that simple, bias-aware methods often work better than complex models in fashion contexts (Syntetos & Boylan, 2005). Third, a dynamic liquidation ladder will get rid of stock that has been sitting for more than a year through tiered channels and timed markdowns that are based on how sensitive the brand is (Smith & Achabal, 1998). Fourth, cross-functional dashboards will show design, FP&A, supply, and retail the age, sell-through, and expected margin-after-markdown at the SKU level. This will close execution gaps that have been shown to lead to bad decisions (DeHoratius & Raman, 2008). Lastly, a working-capital playbook will set up cash KPIs, supplier-term levers, and aging triggers. This will turn inventory into a governed flow instead of a static stock. These changes turn variety into speed while keeping brand and margin safe. Academic Learning Three portable ideas apply to more than just Titan. The Aging-to-Action Dashboard combines inventory age, sellthrough, and expected margin-aftermarkdown into one decision surface. This makes sure that every SKU is on a path— hold, remarket, redesign, or retire—and reduces record-inaccuracy distortions (DeHoratius & Raman, 2008). The Designfor-Velocity Playbook gathers features and launch heuristics from previous slow movers to lower the risk of mismatches. This is in line with research on assortment discipline and inventory productivity (Gaur, Fisher, & Raman, 2005; Rumyantsev & Netessine, 2007). The ForecastCollaboration OS connects design, FP&A, supply, and retail to a rolling, scenariobased forecasting process. It favors simple, bias-aware methods for intermittent demand and pairs them with governance to avoid overfitting (Syntetos & Boylan, 2005) while using markdown science to plan calibrated exits (Smith & Achabal, 1998). These ideas together show how analytics, governance, and cadence can change assortment breadth from a working-capital liability into a long-term advantage in times of uncertainty. CONCLUSION Between FY21 and FY25, Titan's aging and sell-through rates got better, but they need to be more disciplined in their structure to keep from going back to where they were. The company can free up cash, protect its margins, and invest in hybrids and smartwatches without hurting its brand by making rationalization thresholds, demand-linked production, calibrated liquidation, and cross-functional governance part of its normal operations. The win condition is not just lower inventory; it's also a faster portfolio renewal cycle that makes things more relevant and 2025 Kiruthika G, Dr. Shakeela Banu C 24 protects the P&L from changes in the market. CASE QUESTIONS 1. Which two metrics in the data pack most reliably predict a future “poor performer,” and how would you validate their thresholds across price bands? 2. Design a quarterly stop–continue– redesign routine. What evidence qualifies a SKU for retirement versus redesign, and who decides? 3. Build a rolling 12‑month forecast that links launch cadence to production slots. How will you quantify forecast error and adjust safety capacity? 4. Propose a dynamic liquidation ladder for >12‑month stock across offline/online channels. How do you protect brand while unlocking cash? 5. Create a dashboard that a design head, FP&A lead and retail lead would all trust. What are the five tiles and their drill‑downs? 6. Show how a 5‑point improvement in opening‑stock sell‑through propagates to cash conversion and markdown expense over two quarters Elaborated Teaching Notes (for instructors) Programme fit: MBA/Executive modules in Strategy, Operations, Retail & FMCG, Business Analytics, and Working‑Capital Management. Pedagogical objectives: Diagnose ageing and portfolio complexity; design rationalisation rules; construct a rolling forecast and channel plan; quantify working‑capital impact. In‑class flow (75–90 minutes): 0–10 context; 10–25 metric alignment; 25–50 team work on rationalisation and forecast; 50–75 plenary governance cadence; 75– 90 sensitivity on cash conversion. Assessment rubric: Logic and structure 40; evidence and calculations 40; clarity and persuasion 20. REFERENCES Cachon, G. P., & Swinney, R. (2011). The value of fast fashion: Quick response, enhanced design, and strategic consumer behavior. Management Science, 57(4), 778– 795. DeHoratius, N., & Raman, A. (2008). Inventory record inaccuracy: An empirical analysis. Management Science, 54(4), 627–641. Gaur, V., Fisher, M. L., & Raman, A. (2005). An econometric analysis of inventory turnover performance in retail services. Management Science, 51(2), 181–194. Rumyantsev, S., & Netessine, S. (2007). What can be learned from an analysis of inventory turnover performance of retailers? Manufacturing & Service Operations Management, 9(4), 414–435. Smith, S. A., & Achabal, D. D. (1998). Clearance pricing and inventory policies for retail chains. Management Science, 44(3), 285–300. Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303– 314.