Acciarini et al.’s systematic review in Technovation argues that big‑data analytics becomes a source of business‑model innovation only when treated as an organisation‑wide capability supported by governance, leadership and change routines. The paper finds strong evidence for operational payoffs such as forecasting and customer‑facing innovation, but highlights persistent gaps in practical capability, governance and the translation of analytics into sustained competitive advantage.
According to the Technovation review by Acciarini and colleagues (2023), organisations that hope to turn the promise of big data into concrete business‑model innovation must navigate a landscape that is technical, organisational and strategic all at once. Their systematic literature review maps where the evidence is strongest — for example, in enhanced forecasting and customer‑facing innovation — and where important gaps remain, notably around governance, practical capability, and how data initiatives translate into sustained competitive advantage.
Big data as strategic muscle, not a bolt‑on tool
Acciarini et al. show that big‑data analytics (BDA) is rarely a single technology problem. Instead, it works when treated as an information‑processing capability that must be embedded across planning, sourcing, manufacturing and logistics. That insight echoes earlier empirical work showing BDA usage increases value creation when firms are technologically ready and led from the top; technological enablers and managerial support are both necessary to convert analytics into better decisions and outcomes.
The operational payoffs most consistently reported are familiar: sharper demand forecasting, faster detection of disruption, more precise transportation and warehousing optimisation, and targeted service innovation. Practical examples in the literature range from route optimisation in parcel networks to predictive maintenance in complex manufacturing — each illustrating how additional, high‑velocity signals can alter the timing and scope of operational choices.
Maturity, integration and the centrality of governance
Several reviews and practitioner notes underline that benefits are contingent on maturity. A four‑stage SCA (supply‑chain analytics) maturity model — from siloed functional use through collaborative, process‑based analytics to an agile, sustainability‑oriented stage — helps explain why some firms see large gains while others see little. McKinsey’s landscape of supply‑chain analytics reinforces this: analytics can be transformational, but only when organisations rework processes, close capability gaps and institutionalise analytics in decision routines.
Organisational issues return again and again in the evidence: data quality and integration, clear governance structures, and change management are decisive. Gartner’s forecasts warn against treating control towers and real‑time visibility as a panacea; without disciplined data governance and supply‑chain technology leadership, these investments underdeliver. Academics add a further caveat: top management support and alignment across technology, organisation and environment shape whether BDA becomes an enduring capability or remains an isolated project.
Dynamic capabilities as the bridge from data to innovation
A reasoned path from data to business‑model innovation is better understood when framed in dynamic capabilities. Drawing on Teece’s influential formulation, the reviewed literature suggests firms must do three things: sense—use data to detect opportunities and threats sooner; seize—mobilise resources to capture the opportunities; and reconfigure—redesign processes and governance to protect the new value. Where firms are able to combine sensing with organisational agility and learning, analytics investments are more likely to generate novel practices, propositions or revenue models rather than transient efficiency gains.
Empirical nuance: context matters
Empirical studies temper upbeat narratives about BDA. Evidence from field surveys shows the effect of analytics on value is moderated by environmental dynamism — fast‑changing markets amplify the returns to sensing capabilities but also demand faster reconfiguration. Other studies caution that “big data” is not a universal cure: poor signal quality, misaligned incentives, and absence of cross‑functional coordination can neutralise analytical sophistication. In short, more data does not automatically mean more value; how data is governed, interpreted and acted upon is decisive.
From risk management to anticipatory logistics
Use cases in the literature illustrate the breadth of potential: predictive risk management to anticipate supplier failure; demand sensing that shortens forecast horizons and reduces inventory; anticipatory shipping models that preposition goods closer to likely demand; and prescriptive optimisation that translates forecasts into operational levers. These are the same categories McKinsey highlights as high‑impact, but its practitioners also flag a persistent shortage of data‑science skills embedded within supply‑chain teams and the need for standardised processes to capture recurring opportunities.
Practical recommendations for executives
Synthesising the body of work yields a practical playbook:
- Treat analytics as a capability, not a project. Invest in routines and governance that make data part of decision‑making, and measure business outcomes rather than technical outputs.
- Build maturity incrementally. Use a staged approach: pilot use cases that demonstrate value, then scale through cross‑functional integration and shared data standards.
- Invest in people and leadership. Close skill gaps not just in data science but in analytics translation — managers who can ask the right questions, interpret outputs and lead reconfiguration.
- Focus on data quality and architecture. Reliable, integrated data sources are preconditions for higher‑order analytics such as prescriptive optimisation.
- Link analytics to dynamic capabilities. Prioritise initiatives that enhance sensing, enable rapid seizing of opportunities, and institutionalise reconfiguration routines so gains endure.
- Maintain scepticism and measurement. Use control groups, A/B tests and rigorous evaluation to avoid the common trap of investing in “shiny” analytics without demonstrable business impact.
Looking beyond the firm: macro and resilience considerations
Finally, the broader context matters. United Nations economic scenarios and recent pandemic lessons emphasise that global demand shocks, geopolitical tensions and climate risks shape supply‑chain fragility. The literature increasingly links BDA to resilience: analytics can shorten detection and response times, but resilience also depends on diversified supplier networks, flexible contracts and strategic buffers. Data helps inform these choices; it does not substitute for them.
Conclusion
The accumulated evidence positions big‑data analytics as a strategic enabler of supply‑chain innovation — but only on condition that firms treat analytics as an integrated capability supported by governance, leadership and organisational learning. Where companies combine technical maturity with managerial ambition and an explicit focus on dynamic capabilities, analytics fuels not only efficiency but also new business models and resilience. Where these elements are missing, the promise of big data remains unrealised. The research consensus is therefore a pragmatic one: invest in people and processes as determinedly as in platforms and algorithms, and measure success by the longevity and novelty of the value created.
Source: Noah Wire Services
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