A SupplyChainBrain analysis and industry surveys from Gartner, Deloitte and McKinsey reveal a striking success gap: despite record budgets and executive urgency, most supply‑chain transformations fail because organisations digitise old practices and ignore people, data and governance. Experts say leaders must prioritise behavioural change, consolidated data platforms, customer segmentation and supplier diversification before scaling technology and AI.
Despite record budgets and executive mandates, many supply‑chain transformation programmes are failing to deliver. The pattern has become stark: organisations pour money into digital tools and automation, senior leaders declare transformation urgent, yet operations remain trapped in reactive habits. According to a recent SupplyChainBrain analysis by Rich Schmidt of Publicis Sapient, persistent myths about risk, technology and customer service are distorting strategy and wasting resources. Viewed alongside industry studies from Gartner, Deloitte and consulting firms, a clearer picture emerges of why so many changes stall — and what leaders must do differently.
Transformation’s alarming success gap
Gartner’s August 2024 press release found that 76% of logistics transformations fail to meet critical budget, timeline or KPI targets, based on a survey of 306 logistics professionals. The research highlights a common cause: leaders treat transformation as a programme of systems and directives rather than as a people‑centred change in how work is organised. Gartner argues that treating resistance as a resource and engaging teams improves outcomes, while top‑down urgency without buy‑in often backfires.
That empirical finding echoes the critique advanced by Schmidt: investments alone are not enough if they simply digitise old practices. Deloitte’s analyses reinforce this point. Its industry work on post‑pandemic supply chains warns that many firms implemented temporary fixes during the crisis instead of redesigning end‑to‑end processes, producing what Deloitte calls “random acts of digital” — isolated optimisations that leave fundamental fragilities untouched. In a separate Deloitte survey of more than 1,000 executives, 77% reported experiencing an adverse supply‑chain event in the prior 12 months and 44% expected more shocks in the following 24 months, underlining that volatility is persistent rather than transitory.
Myth 1 — “The COVID crisis is behind us”
The most dangerous misconception is that pandemic disruptions are historical. Surface indicators such as eased port congestion or lower spot rates can create a deceptive calm. As Schmidt writes, “the new reality demands acceptance that volatility is now the baseline.” Deloitte’s research concurs: many firms still rely on stop‑gap responses and single‑geography supply sources, which leaves them brittle when the next disruption hits. Practical responses that industry leaders are adopting include deliberate supplier diversification, scenario planning embedded in forecasting, and dynamic safety‑stock policies that reflect demand and lead‑time volatility rather than fixed rules.
Myth 2 — “Technology drives transformation”
Organisations continue to believe that a new ERP, an automated warehouse or an AI dashboard will fix structural shortcomings. But technology, by itself, is an enabler; it does not change incentives, governance or cross‑functional decision‑making. Both Schmidt and Deloitte warn that when companies fail to address organisational silos, poor data practices and unclear decision rights, technology merely automates inefficiency at greater scale and cost. The pragmatic sequence, according to industry guidance, is to clarify outcomes, align processes end‑to‑end, and then select technology that enforces — rather than masks — the new ways of working.
Myth 3 — “More AI use cases equal better results”
The rush to pilot generative AI and machine‑learning models has produced many colourful pilots and few scaled successes. McKinsey’s July 2024 guide for data leaders highlights the limiting factor: data readiness. Around 70% of top performers report difficulties integrating data into AI models because of poor quality, governance gaps and insufficient training datasets. The lesson is consistent with Schmidt’s point that AI without high‑quality, unified data, documented business rules and clear accountability will generate recommendations that users do not trust or act on. Successful programmes start by fixing data platforms, building decision workflows and proving value with a small number of high‑impact use cases before scaling.
Myth 4 — “Customer service means always saying yes”
Treating every customer exception as urgent is an invitation to operational collapse. Harvard Business Review long ago challenged the assumption that delight creates loyalty; its research shows that reducing customer effort is more predictive of repeat business than sporadic “wow” moments. That insight underpins Schmidt’s recommendation to segment customers by cost‑to‑serve and to apply differentiated service models: proactive, high‑touch support for strategic accounts and standardised, low‑effort fulfilment for transactional customers. The combination of analytics, empowered frontline decision‑making and clear service policies reduces firefighting and improves profitability.
Myth 5 — “Sustainability compromises profitability”
Far from being a cost centre, sustainability can unlock efficiency and commercial advantage when embedded into procurement and product design. McKinsey’s work on sustainable value chains shows that two‑thirds of an organisation’s ESG footprint lies with suppliers, and that top performers often capture 5–10% cost reductions through waste‑cutting, packaging optimisation and supplier collaboration. Practical levers include internal carbon or cost‑to‑serve tradeoffs, supplier scorecards that couple ESG metrics with performance incentives, and digital twins to model carbon and cost outcomes of sourcing choices. The upshot: sustainability can be a route to operational reinvention rather than a constraint.
What successful transformations actually do
Across these critiques, common prescriptions emerge. First, leaders must treat transformation as behavioural and organisational change, not only a portfolio of projects. Gartner’s research shows that collaborative change approaches that enlist frontline teams reduce failure risk. Second, data and governance are foundational: before broad AI roll‑outs or analytics programmes, firms must consolidate data, define business rules and create measurable decision routines. Third, redesign work with economics in mind — segment customers, measure cost‑to‑serve, and apply differentiated service models — to stop rewarding exceptions. Fourth, build resilience into the supply‑chain architecture through supplier diversification, geographic balance and scenario planning rather than relying on inventory or ad hoc fixes. Finally, integrate sustainability into sourcing and operations early; McKinsey and Deloitte both show that environmental improvements often align with efficiency gains.
A final, practical note from the industry
Rich Schmidt and multiple consulting studies converge on a single point: courage is required to unlearn comforting assumptions. Leaders must be willing to slow down some headline initiatives to shore up the foundations that make them valuable. That means investing in people, governance, data engineering and change‑management capacity as the priority workstreams — and only then scaling the technology and AI that will multiply their benefits.
If the past three years teach anything, it is that transformation is not a one‑off programme but a continuous capability. Organisations that replace myths with disciplined foundations — and who involve the people who do the work in designing the future state — stand the best chance of turning investment into durable advantage.
Source: Noah Wire Services
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