Rewiring the Engine While It Runs: Technology and Process Reengineering in Indian Supply Chains

3.4 D3–D5 Scoring: Digital Spend, Visibility, Vendor Maturity

D3, D4, and D5 use a qualitative-quantitative hybrid approach. D3 draws on disclosed IT and digitisation capex from sector-representative annual reports. D4 draws on regulatory mandates — pharmaceutical serialisation requirements, automotive RFID and FASTag integration — and DPIIT LEADS 2023 state-level digital logistics scores for states where each sector is predominantly concentrated. D5 draws on RBI TReDS aggregate adoption data, sector-specific supply chain finance reports, and documented vendor digitisation programmes from published annual reports.

3.5 Full SDRI Results

4. What the Data Shows

4.1 The 299-Day Divide

A leading Indian automotive manufacturer carries 23 inventory days. A leading Indian pharmaceutical company carries 348 days. That is a 15-fold difference between two large, well-managed, publicly listed companies operating in the same economy, subject to the same infrastructure constraints, and served by the same logistics networks. The 10-year trend shown in Figure 6 confirms this is structural, not cyclical.

The automotive sector's 23-day figure is not the product of a recent technology deployment. It reflects decades of Toyota Production System (TPS) discipline — a process framework adopted and adapted long before cloud ERP or AI-powered procurement tools entered the conversation. The process came first. Technology followed and amplified what was already working.

4.2 The FMCG Sector Negative CCC Argument

The leading FMCG sector company in this analysis carries a cash conversion cycle of negative 71 days, meaning it receives payment from trade partners before it pays its own suppliers. This is widely attributed in analyst commentary to distribution network scale and buyer market power. That reading is incomplete. The FMCG sector's negative CCC reflects a distribution redesign executed in the early 2000s — rural direct coverage models that removed intermediary layers and established direct accountability at the last mile. Digital tools were then built on top of an already redesigned process. The sequence matters. Technology did not create the efficiency. It scaled what the process redesign had already established.

4.3 The Pharmaceutical DPO Illusion

The leading pharmaceutical company in this analysis carries Days Payable Outstanding of 210 days — the highest in the dataset. Read in isolation, that appears to reflect sophisticated working capital management. Read alongside inventory days of 348 and a cash conversion cycle of 228, it tells a different story. The high payables are compensating for a slow, capital-intensive inventory cycle — not complementing an efficient one. A company that pays suppliers in 210 days because it takes 348 days to move inventory is not exercising financial discipline. It is deferring one problem to manage another. This is process debt wearing the mask of financial strategy — and it is the most important diagnostic the SDRI surfaces.

4.4 The Steel Sector Decade-Long Progress

The 10-year trend data shows the steel sector's debtor days falling 77% over the decade from FY2014 to FY2025 — a 30-point reduction achieved through sustained, incremental process discipline rather than a single technology implementation. Inventory days, however, remain above 170, suggesting the reengineering journey is incomplete and the next phase of process work is still ahead.

4.5 The Textiles Export Paradox

 The leading textiles company in this analysis exports to major global retailers who impose demanding supply chain standards as a condition of trade. Yet its cash conversion cycle stands at 130 days and inventory days at 150. Global customer demands have improved outbound supply chain visibility without reengineering the inbound procurement and inventory processes that sit behind it. The result is a recurring pattern in export-oriented Indian manufacturers: a digital and process upgrade at the customer-facing end, while the upstream processes continue to accumulate debt.

5. Why India's Context Makes This Harder

India has approximately 63 million MSMEs forming the vendor backbone of its supply chains. Their process maturity is, by definition, heterogeneous. A large anchor buyer can deploy a sophisticated supplier portal and mandate electronic invoicing. But if the Tier-2 supplier — a small casting or processing unit in an industrial cluster — still runs on paper-based batch processing, the digital signal stops at the Tier-1 boundary. The supply chain is only as digital as its least digital participant.

 This is precisely what TReDS was designed to address. The RBI-regulated invoice discounting platform, now mandatory for companies with turnover exceeding Rs 250 crore from June 2025, brings MSME vendors into the formal digital financial ecosystem. Sectors with the deepest TReDS penetration — automotive and FMCG — show it clearly in their D5 vendor maturity scores. Sectors where anchor buyers have been slower to onboard their vendor base — textiles and steel — carry correspondingly weaker scores on this dimension.

GST added another layer to the same story. Its implementation forced process standardisation across millions of supply chain transactions. But standardisation of tax compliance is not the same as reengineering of operational processes. Many companies standardised their invoicing and left their approval workflows, inventory counting methods, and vendor payment terms exactly as they were. They gained GST compliance. They did not gain process efficiency.

6. The Three-Layer Model

Sustainable supply chain transformation requires three layers to work together, not sequentially but simultaneously.

The first layer is process architecture — mapping the current state honestly, identifying where manual interventions, approval bottlenecks, and reconciliation steps exist not because they add value but because no one ever removed them. This layer produces no technology deployment announcement and no digital transformation milestone. It is also the layer most frequently skipped.

The second layer is technology selection — choosing tools that fit redesigned processes rather than wrapping existing dysfunction in new software. The distinction sounds obvious stated plainly. In practice, most enterprise technology procurement starts from a vendor's product capability, not from a map of the process problem the organisation is trying to solve. The result is technology that is technically implemented and operationally underused. Gartner research consistently shows that 55-75% of ERP implementations fail to meet their original objectives — the primary cause in most cases is process complexity that was not addressed before implementation began.

The third layer is capability building — ensuring that the people operating the process understand both the logic of the redesigned workflow and the behaviour of the technology supporting it. The automotive sector's lead in SDRI scores reflects, among other things, two decades of investment in vendor development programmes, supplier quality training, and process standards propagation through the tier structure. This is slow, expensive, and difficult to measure. It is also why the numbers look the way they do.

7. Conclusion: The Gap That Technology Cannot Close Alone

India's National Logistics Policy targets bringing logistics costs down to 8% of GDP by 2030. The SDRI data shows clearly what stands between today's position and that target — and it is not a technology gap.

The highest-scoring sector carries 23 inventory days and a negative cash conversion cycle of -31 days. The lowest-scoring sectors carry inventory days between 150 and 348, and cash conversion cycles between 130 and 228 days. Both ends of this spectrum have access to the same technology platforms, the same regulatory environment, and the same national logistics infrastructure. The difference is process maturity — accumulated and compounded over years, not remedied by a software deployment.

Three conclusions follow directly from the data. First, the sectors with the worst SDRI scores are not technology-poor. The pharmaceutical sector operates under FDA 

compliance and serialisation mandates. The textiles sector serves global retailers with demanding SCM standards. The problem is not what technology has been deployed — it is the unaddressed process debt sitting underneath every technology layer that has been added. No further technology investment will change the SDRI score until that debt is systematically reduced.

Second, the sectors with the best SDRI scores achieved their positions through process discipline that preceded technology investment. A negative CCC and a 23-day inventory figure are process achievements. Both sectors then deployed technology to scale and sustain those achievements. The sequence is not incidental. It is causal.

Third, the 8% logistics cost target will not be reached by deploying more digital infrastructure on top of unchanged processes. India has already demonstrated it can build the platforms — ULIP, TReDS, Logistics Data Bank, FASTag. What it has not yet demonstrated, at scale, is the discipline to redesign the operational processes that determine whether those platforms deliver their stated benefits.

Hammer and Champy argued in 1993 that the fundamental error of most business improvement efforts is to automate existing processes rather than reconsider them. Three decades later, that error remains the dominant mode of supply chain transformation investment in India. The SDRI data, constructed entirely from publicly available sources, puts a number on the cost of that pattern.

The question worth asking before any supply chain technology investment is not whether the tool is capable. It is whether the process it will run on is worth running faster.

Methodology Note

The SDRI is a composite index constructed from publicly verifiable data. D1 and D2 are fully quantitative, derived from consolidated financial ratios for FY2025 of BSE-listed sector representative companies accessed through screener.in. D3, D4, and D5 are qualitative quantitative hybrid scores based on documented evidence from annual reports, regulatory filings, and government reports. Individual company names are intentionally omitted; the index reflects sector-level patterns observable across multiple companies within each category. The index is designed to be replicable and updatable annually. The author acknowledges that sector-level SCM digital readiness indices do not currently exist in India at this granularity — the SDRI is a first order construct intended to stimulate further empirical research.

References and Bibliography

Books and Foundational Works

Brynjolfsson, E. and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company.

Cunningham, W. (1993). The WyCash Portfolio Management System. ACM SIGPLAN OOPS Messenger, 4⁽²⁾, 29–30.

Daugherty, P.R. and Wilson, H.J. (2018). Human + Machine: Reimagining Work in the Age of AI. Boston: Harvard Business Review Press.

Goldratt, E.M. and Cox, J. (1984). The Goal: A Process of Ongoing Improvement. Great Barrington: North River Press.

Hammer, M. and Champy, J. (1993). Reengineering the Corporation: A Manifesto for Business Revolution. New York: HarperBusiness.

Kane, G.C., Phillips, A.N., Kiron, D. and Buckley, N. (2019). The Technology Fallacy: How People Are the Real Key to Digital Transformation. Cambridge: MIT Press.

Westerman, G., Bonnet, D. and McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Boston: Harvard Business Review Press.

Academic Journals

Ray, S. and Das, S. (2007). The Indian Economy: A Study of its Susceptibility to Fuller Capital Account Convertibility. Management and Labour Studies, 32(4), XLRI Jamshedpur. DOI: 10.1177/0258042X0703200404

Das, S. (2009). Corporate Reporting Framework (CRF): Benchmarking Tata Motors against AB Volvo and Exploring Future Challenges. Decision, April 2009. IIM Calcutta. ISSN 0258-042X.

Mentzer, J.T. et al. (2001). Defining Supply Chain Management. Journal of Business Logistics, 22(2), 1 25.

Terziovski, M., Fitzpatrick, P. and O'Neill, P. (2003). Successful Predictors of Business Process Reengineering in Financial Services. International Journal of Production Economics, 84(1), 35–50.

Government and Regulatory Sources

Department for Promotion of Industry and Internal Trade (DPIIT), Government of India (2023). Logistics Ease Across Different States (LEADS) 2023 Report. Ministry of Commerce and Industry.

Ministry of Finance, Government of India (2023). Economic Survey 2022–23. New Delhi: Government of India.

Reserve Bank of India (2023). Trade Receivables Discounting System (TReDS): Regulatory Framework and Guidelines. Mumbai: RBI.

World Bank (2023). Connecting to Compete 2023: Trade Logistics in the Global Economy — The Logistics Performance Index and Its Indicators. Washington: World Bank Group.

Industry and Consulting Reports

JP Morgan (2024). Increasing Efficiency: Working Capital Index 2024. New York: JP Morgan Payments.

NASSCOM (2024). Future of Work Report 2024: AI Adoption in Indian Enterprises. New Delhi: NASSCOM.

CII–Kearney (2023). India Manufacturing Competitiveness Report. New Delhi: Confederation of Indian Industry.

Mynd Fintech (2025). Supply Chain Finance Highlights FY2024–25: A Year of Innovation, Inclusion and Acceleration. Gurugram: Mynd Solutions.

Panorama Consulting (2023). 2023 ERP Report. panorama-consulting.com.

Financial Data

All sector financial ratios (Inventory Days, Days Payable, Cash Conversion Cycle, ROCE) sourced from screener.in — Consolidated Annual Reports, FY2014–FY2025. Data drawn from BSE-listed sector representative companies across automotive, FMCG, pharmaceuticals, steel and metals, and textiles. Individual company names are withheld by the author.

About the Author

Subhrajyoti Das is a Data and Analytics Leader with 18+ years of experience spanning supply chain management, procurement analytics, and enterprise data engineering. He holds a BTech from Bengal Institute of Technology, Kolkata; an MBA (Finance & IT) from ICFAI, Kolkata; and is a certified Six Sigma Master Black Belt (SSMBB) from Benchmark Six Sigma. He has previously published in Decision, the quarterly journal of IIM Calcutta (April 2009, ISSN 0258-042X), and in the Quarterly Journal of Management and Labour Studies published from XLRI Jamshedpur (November 2007, ISSN 0304-0941).