Supply Chain Whiplash
Much has been written about today’s unprecedented manufacturing business environment and it’s timely and warranted. Even before the late January news out of China, uncertainty was compounded by the direction of the Brexit spin-off, near trade wars with major trading partners piling tariffs on top of tariffs, and an oil war that resulted in a short race to the bottom. Who would have thought the business environment would change even further?
Within recent memory, the market was reaching heights that were not thought possible, but considering the recently mandated lockdown, most businesses are forced to radically change focus. Since mid-2019, Manufacturing was in a slight downturn but still focused on market share and shareholder value, that focus has changed to preserving liquidity and maintaining cash flow. This extreme pivot has changed business focus, definitely for the long run and maybe for the better.
There are many solutions that drive businesses to improve supply chain agility such as improved demand forecasting, risk modeling, robotic automation, dashboard deployment, and a preventative measure – predictive maintenance. But when we think of manufacturing supply chains, one of the first things we think of is inventory in the form of raw materials or finished goods, as inventory has been king and also a crutch.
Inventory solves a whole number of manufacturing challenges by acting as a buffer to decouple demand and supply. As demand surges, finished goods inventory quickly makes up for missed promises to customers. When supply wanes, raw material inventory again steps in to pick up the load to keep the manufacturing process steady and predictable. Inventory has lulled the supply chain processes such as demand planning and raw material sourcing into a once a month activity. We no longer have the luxury of once a month management, a much more agile process is demanded.
As China went into deep lockdown in February, an unnamed global Tier I automotive structural materials supplier reported zero invoices for the month of March. The ripple effect of occurrences like this are significant. A recent McKinsey report stated that the lockdown had profound effects on Chinese demand in the first quarter: a 21% decrease in retail sales, a 90% decrease in passenger car sales, a 60B RMB decrease in consumer spending, and a 40% drop in smartphone sales.
As factories return to work from what was first a demand shock, they are now poised to face a supply shock that inventory will not cure. Lessons from China are emerging, we see a whiplash effect on supply as companies at best have only 65% of employees returning to factories that are running at non-optimum levels, trucking and transportation that is geographically limited, and an estimated 2 million ocean shipping containers that are in non-optimized locations causing havoc obtaining and shipping them at fair prices. As companies seek to quickly replenish their supply chains, as they have been living off their safety stock, they have pivoted from ocean shipment to air, now doubling air shipment costs.
To sum this up, factories can no longer rely upon their tried and true supply chain as they have in the past. The need for agility is even more important than in the past. Agility involving insight provides the business the power to make real time decisions that weigh options with opportunities.
The Response – Supply Chain 4.0
Industry 4.0’s shift has been enabled by the proliferation of inexpensive process sensors tailored to the specific use, robust edge computing devices allowing repetitive autonomous decisions, cloud computing performing both analytics and storage, and soon to come – 5G, which opens the lanes of the data superhighway, freeing manufacturing from the chains of hard-wired connections.
Operations have embraced its digital transformation as predictive maintenance is now enabled in almost every manufacturing industry with strong success reaping nearly 50% reductions in machine downtime. It’s time for Supply Chain 4.0 to reap these same benefits. Some of the actions that manufacturing supply chains are doing to address the need to improve agility are:
1. Agile Demand Forecasting In its simplest definition it is the forecasting of future buying events. Forecasting, once a one-dimensional process-based singley upon historical buying patterns, will largely benefit from big data and advanced analytics as well as from the automation of knowledge work.
Big data now has the ability to leverage predictive analytics to transform demand forecasting into demand planning which analyzes data from tens to hundreds of data sources (as does predictive maintenance) considering both internal sources (ERP, SCM or MES systems) but also from external sources (market trends, weather, public holidays, consumer pricing indexes, etc.) to provide real-time insights into inventory, replenishment plans and ultimately a distribution plan that leads to a picture of demand at the factory floor.
As the supply chain now has some kinks in it, connecting insights from supply through demand will improve customer satisfaction, lower shipping and expediting costs, and improve cash flow once tied up in excess or unneeded inventory.
2. Model Risk Outcomes: Modeling risk in business was once a qualitative guess, now modeling can leverage enterprise data to deliver quantitative assessments from a larger and more diverse data set. Demand assessments, once singular numbers are driven by singular events, can now be presented as probabilities predicting a number of scenarios that can offer a range of solutions through targeted discussions, including upside potential and downside risks in sales and operations planning.
Digital twin Monte Carlo simulation has long been used by the oil and gas industry to identify vulnerable equipment, under capacity storage facilities and other supply chain weaknesses, it can now establish optimum supply chain choices as business conditions dynamically change. This methodology has the potential to be applied to consumer buying demands, an analysis of the strength of the supply and distribution chain, and assessments of equipment or processes on the factory floor that can cause disruption in the flow of goods.
3.Robotic Automation: Considering worker safety social distancing demands, robotic factory deployment is a viable solution. Traditionally, we think of robots that are applied to jobs that are dangerous, require heavy lifting, and are repetitive. The older generation of robots were programmed with step-by-step processes in order to do the job. Today’s robots are trained in a collaborative process where they work beside human workers augmenting their skills. Robotic deployment has seen double-digit efficiency growth in pick and pack, and now it can satisfy worker safety requirements considering social distancing needs.
But robotic implementation can be highly costly and is considered to be a fairly high maturity use case. Other automation can be applied to businesses such as leveraging scanners, beacons, or other telemetry devices to improve the accuracy of inventory counts, location of supply chain fleets in real-time, or the monitoring of inventory over many geographies, time zones, and commercial outlets. In speaking recently to a heavy equipment manufacturer, she said, “one of my biggest challenges is finding the parts in the warehouse or factory floor when we need them”.
4. Dashboard Deployment: The old adage (Knowledge is Power) is very relevant to today’s work-from-home supply chain staff. Once business silo’s were broken down only by the number of water coolers the office had, today’s environment is even more challenging with people working from home trying to balance work and the informal office disruptions. With the need to minimize workers in plant settings, remote access to information is critical. Visualization and workflow tools provide remote workers with the ability to react to changes and collaboratively manage supply chain efficiency.
By leveraging structured and unstructured enterprise data from operations (data historians, sensors, beacons) and combining it with business process data ( ERP, SCM, MES) companies are able to reap the benefits of creating the dashboards at any level of granularity that is needed. Drill-down capabilities allow for intense inspection of the variables at any level that the organization needs for actionable insights.
5. Operational Excellence: This might sound condescending, but applying enterprise analytics to your Operations solves problems before they start, allowing a focus on supply chain activities external to your business. Industry 4.0 is successfully leveraging enterprise data to deliver actional benefits in process optimization, yield or throughput improvement, and one that is top of mind – equipment predictive maintenance. Smoothing internal material or parts consumption is that hidden benefit to those working the supply chain
Plan for Next Normal:
Why would you plan to go back to the old normal? The unpredictability that the pandemic is causing can be used as a competitive advantage as supply chains settle. These times are providing us the opportunity to fundamentally rethink supply chain design, reducing the potential for future vulnerabilities. Also, from a big data perspective, in addition to relying on more real-time data, the planning process itself can be run more often and on larger datasets resulting in planning on a shorter time horizon promoting the much-needed supply chain agility.
To learn more about my perspective of Cloudera’s Manufacturing Operations and Supply Chain solutions visit us at https://www.cloudera.com/solutions/manufacturing.html