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Robust & resilient: How analytics keeps supply chains running

(Image credit: Image source: Shutterstock/ESB Professional)

The rapid spread of Covid-19 has put supply chains under extreme stress as industries struggle to keep up with demand amid growing fears around the world.

With many industries depending on goods and materials from across the globe, we are in challenging times and decision-makers are having to make rapid adjustments to meet supply needs and ensure resiliency.

Few parts of the business landscape feel the effect of a major crisis like a supply chain. A single event can have a domino effect across an entire chain, and many businesses are now navigating unchartered waters. Leaders must act fast to implement measures that ensure demand fulfilment is scalable as sales volumes continue to escalate. But that’s a formidable task with no shortage of problems to solve.

Most business owners and managers understand the importance of responsiveness when it comes to changing circumstances, but crisis scenarios bring about unprecedented challenges and additional pressures. Protecting the supply chain in a crisis requires speed and skill; it’s better to plan ahead than be forced to play catch-up.

Of course, it’s difficult to predict and get ahead of natural disasters and accidents, but the inability to fully understand how to plan for disruptions and the inevitable shortages are the signs and symptoms of a stressed supply chain. Being able to play out scenarios and have a disaster recovery plan is key. Companies can’t afford to wait for problems to develop before taking action, they must prepare for the possibility of extreme disruption to operations.  But to do so, requires data transparency, agility and the prepared recommendations to take actions. As businesses recover from this disaster, the focus for planner into the next year will be to use data to build in transparency first, use that transparency to understand their risk exposure and finally make recommendations to plan accordingly. Analytics can help augment the emergency response to predict what’s happening on the demand end and help keep three major parts of a supply chain moving.

Reshaping demand forecasting

When a large personal care company had to shut down manufacturing sites in South Korea and China, they needed to quickly understand where their running and safety stocks were located in order to keep up with demand. Planners quickly focused on understanding if they could scale up production across other factories to make up for lack of inventory as well as how fast they could ship new products to distribution sites. Data and analytics enabled them to analyse products from manufacturing to end-sources, as well as variables such as the time required to clear customs. This allowed them to create a realistic view of their demand forecast while assessing production impact, raw material forecasting and distribution time.

These sudden spikes in demand can prompt planners to quickly evaluate the level of safety stock inventory and move it in order to mitigate the risk of running out of raw materials or finished goods. Through the analysis of demographics and population densities, planners can easily reprioritise shipping locations as well as utilise drive times and logistic details so that inventory moves to the right locations in time.

To further optimise production during shifting demand patterns, planners and buyers may need to reshape previous forecasts based on alternative suppliers and raw material availability, including current running and safety stock levels. Analysts need to interpret historical dark data from known situations like previous recessions, or crisis data from flood or hurricane emergencies to model and build a forward-looking forecast into recovery. Data science and predictive modelling is the only way to see into the future. It provides deep and actionable insights for teams looking to optimise the production and distribution of equipment, services or other supply-chain processes required.

Reshaping suppliers and activating new resources

Maintaining a flexible supplier framework is critical in times of crisis and supplier failure can put an instant stop to operations if raw materials necessary for manufacturing aren’t ready in time. The consumer-packaged goods (CPG) industry can be particularly susceptible here as raw materials are often sourced from various suppliers and any variances to raw material composition can directly affect the “recipes” and end-product quality. Product scientists from one such CPG manufacturer are required to constantly analyse recipes based on raw material ingredients and utilise an innovative application to enable the operator to input various raw materials conditions which would affect the recipe. Utilising the data inputted via the operator and analysing different product recipe combinations to adjust for new ingredients, the machine can be easily recalibrated to reflect the new variable so that each batch is always on quality.

The measurement and optimisation of supplier performance is critical to meeting procurement needs, and complex supply chain environments require a pool of alternative suppliers to draw upon to avoid stoppages down the line. Analytically savvy businesses are:

  • Empowering procurement functions to assess risks by analysing goods supplied from alternative suppliers to quickly identify pre-approved part or material substitutions and activate the product or material redesigns;
  • Utilising analytic forecasting to ensure that in-market sourcing groups diversify suppliers to reduce dependence on a possible single high-risk facility or part;
  • Evaluating what-if scenarios to source pre-approved alternative tier two and three vendors or product substitutions to cover shortfall and activate product redesigns based on currently available resources;

Reshaping logistics and time to delivery

An American fast-food chain with over 4,000 stores in the US, were keen to unburden their supply chain and turned to applied analytic spatial tools to determine which of their supply vendors were closest to each store location. This was particularly critical in anticipation of a product recall situation, something that had previously been a challenge as a franchise organisation reliant upon franchisees to properly track their produce. Their development strategy and analytics team set out to track produce and provide a secondary produce option in such an in order to protect their consumers. In employing sophisticated analytic software, they converted what was a four-person, 41,000-hour process into a single, repeatable workflow.

When working on redistributing current inventory and materials from possible quarantined areas, or moving safety stock to feed high demand areas, its crucial to be able to quickly analyse and secure additional means of transportation based on individual product lead times as supply and capacity fluctuate.

From understanding carrier options and analysing spatial components -- such as drive time variables and trade areas to carry out effective rerouting of deliveries -- data from geospatial sources, including real-time satellite positioning, tracking and geofencing can make sense of a company’s complex, distributed supply chain. Understanding where 100,000 trucks are moving raw or processed materials across continents can lead to better insights around potential bottlenecks or transportation hubs to improve deliveries or reduce downtime.

Raw materials can be impacted because they’re stuck in long lines at borders. Predictive analytics can model scenarios and help create workflows to deliver real-time estimations of inventory and forecasts for spare capacity or shortfall. Often classed as measures of inefficiency within a just-in-time industrial process, in times of crisis analytics can help show where excess parts are retained, acting as a bridge or buffer before new supplies are needed.

Building robust supply chains

Planners and buyers are left scrambling to find solutions for short-term shortages and massive delays while looking to avoid future risk. But to do so requires harnessing an abundance of data from countless sources, and many variables must be analysed to provide the crucial insights needed to anticipate where the gaps might show-up.

Critical decision-making requires good information and good data. Consequently, data and analytics should serve as stabilisers to help companies model prospective business scenarios so they can enable the necessary evaluation, adaption and course-correction in response to market conditions.

However, the value of data is only realised when it is ubiquitous. Without data access across the whole organisation, key leaders are left handicapped. Organisations can only make better decisions based on data and analyses to deliver insights at the speed required when that information is available and accessible.

We know that many successful companies have a capable community of “citisen data scientists” – analytically savvy data workers adept at using technology to find useful patterns and meaningful correlations. Just by drawing on their cognitive skills and the powerful analytic solutions available, businesses stand to derive considerable value. With the right tools, this cohort can analyse current disruptions in real-time while identifying steps to improve resilience for better continuity in the future.

Transformative data-driven responses

Running an efficient supply chain means accounting for multiple internal and external factors that can impact inventory. Companies are expected to sustain production even amid operational breakdowns, limited resources and no clarity on what the world will look like in the near- or long-term. Those who were already strategically applying agile, advance analytics to their supply chain planning will fare better in this crisis than others.

The stability and resilience of repeatable analytics provides improved efficiencies across the entire supply chain through the enablement of capabilities that can quickly calculate or reroute a fleet, identify supply shortages or predict staffing needs. But to gain insights at the speed of crisis, requires a solid, robust foundation of analytics. Analytic platforms need to be able to work with a wide variety of data and sources to achieve these kinds of outcomes — whether this is data held in proprietary internal systems, external vendor websites or real-time tracking telemetry. That is why organisations need to invest in self-service analytic platforms that will empower them to create data transparency for all, enabling functional departments with their unique business models to use experienced business users as citisen data scientist to help apply the lessons learned from this crisis and plan for the future.

The power of analytics extends far beyond one organisation’s ability to weather the storm but can serve as a critical driver in reaching a global solution. In parallel to reacting to short-term business implications, leaders also need to implement a robust analytic strategy that leverages predictive insights to identify and prepare for future vulnerabilities.

Alan Jacobson, Chief Data and Analytics Officer, Alteryx

Alan Jacobson, Alteryx’s Chief Data and Analytics Officer (CDAO) will explain how the increasing adoption of data science and analytics technologies is leading to the a change in the CDO role.