Why decision intelligence is essential for overcoming supply chain constraints
The current supply chain disruption is one of the many types of crises the marketplace has faced over the years. Even before COVID-induced challenges had cargo ships anchored off of ports across the globe and store shelves barely stocked, supply chain leaders have been in a race to keep up with changing consumer demands, a shifting competitive landscape, and technological advances.
Yet, as the development, reach and success of businesses has become highly dependent on tightly linked supply chains, the structure of those connections has become increasingly fragile and intricately connected.
Over the last two years, an unprecedented supply chain crisis has unfolded. With networks spanning multiple continents, global supply chains have broken down. From COVID-19 and the war in Ukraine to a sideways freighter that blocked the Suez Canal for a week and a growing list of environmental disasters, the upheaval has created a new benchmark for business-as-usual. A survey from the UK Office for National Statistics showed that 40% of businesses in the wholesale and retail trade industry reported global supply chain disruptions at the end of the first quarter this year.
This disruption is closely tied to a failure of foresight and planning built into supply chain systems.
Asking the right questions
Many companies tackling supply chain disruption see themselves as “data-driven,” when in fact, most are not. A Gartner report shows that less than half of organizations have actively started to build a roadmap for supply chain digitization transformation, despite it being a key priority for most leaders. Another survey showed only two-thirds of supply chain organizations felt the strategy and execution of their supply chains were well aligned.
Business intelligence (BI) and analysis tools were the promised future, where business users could easily access and transform huge volumes of corporate-wide data to predict business outcomes and future demand. However, the reality is that traditional BI solutions and ERP systems are static and can only provide a snapshot of the present or past.
Monitoring current trends and known supply and consumption rates cannot provide accurate market insights and account for sudden and unexpected shocks in the global economy. Predictive and prescriptive analytics — essential to decision intelligence — are capabilities far beyond their reach. Legacy BI tools — which are often little more than dashboards and as old as 30 years — and traditional analytics can’t tell you what’s coming next, much less what to do about it.
These systems are out of step with the needs of larger enterprises. Many such businesses produce goods that combine ingredients or parts from multiple vendors — whether sourcing resins for use in plastics, individual parts for manufacturing cars, or chemicals for drugs and therapies. These large enterprises have deadlines to hit to source these items, create their product and ensure it reaches customers.
Supply chain blind spots can be tremendously disruptive to this process. They stop firms from setting expectations with end-users, damaging the company’s reputation when deadlines are missed and goods arrive later than promised.
These gaps come down to businesses asking the wrong questions when managing their supply chains. Too many firms still think only in terms of descriptive (what has happened and what the present state is) or predictive (what will happen) analytics. Even then, limitations on simplistic, mass-market analytics mean that predictive analytics often has low accuracy.
Reliance on business intelligence leaves organizations hamstrung, able to ask only the basic questions, not tot use data to think farther ahead and strategize new approaches in order to adapt to, or even avoid, disruption.
Decision intelligence rests on prescriptive analytics
Such foresight comes from adding a prescriptive analytics layer to a firm’s supply chain management. This layer answers the question “what should happen” and becomes the basis for generating decisions, not just insights. This approach elevates the level of analytic inquiry, using machine learning and optimization models to propose a course of action based on data, analytics and business models.
Ultimately, this can dramatically transform how companies manage the flow of goods throughout their supply chains because it resolves the question how to proceed to achieve the targeted outcome.
Imagine you are a factory owner who makes chairs. Descriptive analytics gives you a detailed view of, for example, how many chairs you made last month. Predictive analytics would let you think ahead and forecast likely demand for those chairs next month.
Prescriptive analytics brings it all together, providing the owner with intelligent, AI-driven insights like “How many chairs we should make in June, given the price of wood and customer demand next month.”
In this new world of high prices, fluid customer demand, and continued difficulties in the movement of certain goods, having these insights at business leaders’ fingerprints helps them make the informed and rapid decisions necessary to stay ahead of the curve. Prescriptive analytics also equates to significant iterative improvements in customer experience, allowing firms to be honest and upfront with their customers — transparently updating them as to where the order is and when it will be delivered. In today’s economy, such trust is a precious commodity.
What does this technology look like for businesses?
Decision intelligence and the future of the supply chain
Taking a new approach to supply chains relies on a new vision for data in an organization. Data is the engine of growth and the source of intelligence that will allow businesses to get a grip on their supply chains.
This means drawing on data from a wider variety of sources than ever before. Businesses need more actionable, real-time data from across their supply chains. They need to quickly and securely access multiple data sources across on-premises data centers and multiple clouds. To plan for future shocks, businesses need to learn from this historic moment and feed this information into predictive and prescriptive analytics modeling.
This must be combined with a range of external data, and tapping into complex unstructured data from non-traditional sources like social media. Such a broad terrain of data creates the scope for more comprehensive and impactful analytics. And as many leaders know now, but it bears repeating here, this data needs to be high-quality, avoiding instances of duplication or multi-source that slow analytics down.
The key, then, is bringing it all together. Many leaders recognize the huge potential of their growing volumes of data, but their organizations struggle to access all their data and analyze it efficiently at scale. According to some estimates, more than 80% of available business data is not used to make decisions. Organizations also need the ability to put the insights from analytics into the hands of business users without the high barrier for entry that can come with data science.
When managing supply chains, firms can ill afford this lack of flexibility. Decisions need to be made fast, with regularly consideration of predicted outcomes that might extend months or even years into the future. We should not forget that many issues with customers come back to supply chains: delivery times wrong, promises made that could not be kept — problems that pile up and encourage customers to look elsewhere.
Decision intelligence is the answer to these major shortcomings. Identified by Gartner as a top growth technology for 2022, it ensures that anyone in the organization can easily prepare data, carry out analytics bespoke to their needs, and receive accessible insights that can be acted upon by anyone in the business. This flexibility is powered by augmented analytics, deploying artificial intelligence and machine learning to (in Gartner’s words) “augment how people explore and analyze data in analytics and BI platforms.”
A new tomorrow
Supply chain management solutions based on decision intelligence and real-time prescriptive analytics models are potent instruments in the fight against the supply chain crisis. Such systems can improve overall processes throughout the enterprise and build resilience into demand forecasts. They can reduce costs associated with overstocking, inventory stockouts, and product obsolescence — even in the face of widespread crises.
So much is possible. Consider a world where a business never runs out of cotton, as it is equipped with prescriptive insights that look at trends in cotton prices and usage by your business and identifies the optimum time to buy. Or a firm where an AI performs a constant analysis of the supply of equipment across your supply chain and sends you an automated warning when you are at risk of running out. All this and more is possible, ensuring businesses and their supply chains are ready for anything — whether that is the day-to-day or the next crisis.
Avi Perez is CTO and cofounder of Pyramid Analytics.
Source : venturebeat.com
