Supply chain disruption will continue into 2022 and is likely the “new normal.” To adapt, company leaders should tap Chief Data Officers, or CDOs, who are uniquely positioned to shore up critical decisions at key chokepoints in supply chains.
Many of these decisions – from understanding difficult-to-track customer demand to procuring materials from a network of uncertain capacity – suffer a lack of insight into predictable outcomes and time-sapping, inefficient analytic development processes. All stand as barriers to meeting financial objectives at a time of escalating customer expectations that shows no signs of slowing.
Analytics automation unlocks the potential of influences hiding in external data such as weather forecasts, economic indicators, geopolitical events and more, to discern important changes in customer demand and potential disruptions in supply lines. More timely insight based on these signals leads to an experience that customers recognize as in-tune with their needs during lingering uncertainty. They in turn express gratitude with their wallets.
This is the very definition of a Customer Centric Supply Chain. While the term is not new, it’s more relevant than ever to any supply chain participant with a “customer.” That means any manufacturer, retailer, logistics or shipping company. Long-time operating models like just-in-time, or JIT, and lean manufacturing are ineffective when customer demand shifts unexpectedly, and suppliers cannot be relied upon with confidence.
The relationship between demand planning and procurement lay especially exposed as points of failure. Consider problems faced by demand planning rooted in old ways of forecasting based on orders and shipments, and how procurement leaders suddenly must care more about CX than saving money.
“In a few short months (of COVID), consumer purchasing habits, activities, and digital behavior changed dramatically, making pre-existing consumer research, forecasts, and predictive models obsolete.”
Harnessing the Power of External Data, McKinsey Digital
“So, what can procurement teams do to up their agility game? Turns out, customer-centricity is key.”
Decision Improvement Focus
Most organizations don’t have time to re-engineer supply chains like Walmart. However, any organization can make material improvements to the quality and timeliness of decisions made every day across their supply chains that equates to meaningful business value. Focusing here points the way to how supply chain processes may need restructuring in the future.
This is an insight CDOs must impress upon their C-level peers, including those leading Supply Chain, Information Technology (IT), and Finance.
Research shows CDOs are caught between the technical concerns of building data-driven capabilities and ensuring alignment with C-suite priorities. It’s the root cause of high turnover in the CDO position, at a time when few organizations can afford to recruit, hire, and onboard new analytics leadership.
Where CDOs create value is highlighting opportunities to improve the business, given how decisions unfold today. In most organizations, there are teams of analysts and data scientists embedded in supply chain processes, and not always under the direction of a centralized analytics function led by the CDO.
Even with dotted reporting lines between these quantitative subject matter experts, there are a lot of benefits to implementing more formal connections that carry specific expectations and accountabilities. Governance is easier to implement to ensure alignment with policies for the ethical use of data and scale the application of higher value analytic methods like Machine Learning, or ML. It becomes possible to rationalize and measure company investments in analytics overall, based on transformation priorities, use cases, skillsets, headcount, and business outcomes.
Starting Confidently with Less Risk
“Low hanging fruit” describes a best practice for achieving value with analytics that focuses initially on the highest value and most quickly executed use cases. Look around in your organization, and there are innumerable decisions across your supply chain just like this characterized by:
- Time sapping manual efforts requiring oversight and maintenance by overburdened staff.
- Too much time spent assembling the right data to support the targeted decision versus charting out a path to additional use cases and business value.
- Limited involvement by business stakeholders with the most domain expertise but who depend on the insight to do their job.
- Latency, or too much time, between the creation of an insight and ability to inform the best decision possible.
- Inwardly focused thinking, from the data sources to the way decisions impact the end customer. Outside-in thinking needs to prevail.
While your business can operate under these circumstances, the probability of success in this new world goes up appreciably when you address them proactively in a structured manner.
Each of these points lends itself well to analytics automation.
Too much manual effort: The enterprise applications which run supply chains, like ERP and SCM, are focused less on flexibility than repeatability. In other words, they support business process execution well, but lack the open-endedness that the most mature analytics organizations exploit to lead their markets.
These leaders have a huge head start, in terms of how analytics is funded, organized, and deployed. They usually have data science professionals focused on the highest value outcomes while others in the organization embrace individualized and less complex use cases deployed at scale.
Analytics automation subverts these limitations via “low or no-code” methods of enabling staff with varying skills to address any element of analytics development. In practice, this allows everyone to spend less time building and maintaining one-off analytics by capturing work once and automating it in an auditable and governed manner. This approach also supports analytic asset reusability, which speeds time to value for successive use cases.
While sometimes contrasted with simple tools like spreadsheets, there is virtually no comparison to what analytics automation offers CDOs and their supply chain constituents. The same can be said for BI, or Business Intelligence tools focused on presenting visuals as opposed to developing the underlying analytics. All in fact, often play roles.
The simplest way of understanding the difference is the extent to which any technology helps you grow both analytics maturity and business value across descriptive through prescriptive methods.
No time to look ahead: CDOs should educate their peers about the value of a portfolio approach to analytics use cases across the business. This means looking at analytics excellence as a journey, not a destination, single project, or technology investment. Enabling analysts, data scientists and any quantitative role to spend less time on data manipulation means they have more time to think about how analytics applies to other areas of the business in need of improvement.
Little collaboration with the business: Following on from the above point, requirements must be business-driven, not imagined, or assumed by those least familiar with the targeted business process or decision. When those developing analytics cannot collaborate with peers in the business, there is almost no chance the analytics support the best decision possible.
Analytics automation presents both on-premises and Cloud deployment options, which allows CDOs to oversee a scalable and cost-effective upskilling process that exposes the value of data driven decision making to those in non-quantitative roles.
A CDO thus directly impacts the organization’s “Analytics IQ,” which becomes measurable by aggregating the business value of use cases. That’s a metric every CDO would like to share with his or her peers, the board, and investors.
Poorly timed decision making: Executives overseeing supply chain processes are under more stress than ever. Decisions must be made quickly and confidently, against a backdrop of almost total uncertainty. Therefore, buying more time to consider every option before placing bets is among the greatest benefits a CDO can offer the business. Analytics automation applied well reduces the latency of analytics supporting the most important supply chain decisions. This has the effect of recruiting more company business leaders to sponsor work in other areas equally impacted by decision quality and latency.
Inside-out thinking: Supply chain leaders and CDOs should take a page from the marketer’s handbook to overcome deficiencies in JIT and Lean thinking. Doing so requires embracing an outside-in approach to improving supply chain decisions that impact customer experience outcomes like this:
There is little chance of succeeding when insights are based solely on inwardly focused, operational data. Analytics automation in the Cloud makes it possible to quickly spin-up a variety of use cases leveraging external data sources.
CDOs and their teams may recognize language in this post but struggle to apply the learnings due to competing priorities and a lack of resources. CDOs should know how analytics automation is used productively by many well-known organizations across the world to overcome such challenges.
Below are links to recorded presentations and interviews conducted over the past year with Alteryx customers.