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The Only is Constant Change

Normal

By Lora Cecere, Supply Chain Insights

The COVID-19 pandemic tested the global supply chain. While prior risk management disruptions occurred and followed by a new normal, in the COVID-19 pandemic, the only normal is constant change. Like driving on a bumpy road, supply chain leaders will ride the ups and downs facing greater variability day-to-day until the end of 2021. During the pandemic, companies were unable to use their demand planning solutions, according to information based on interviews with 30 supply chain leaders. Companies turned their demand planning solutions off and managed through brute force. The issue? Order and shipment data was out of step with the market and the models were not flexible enough to use consumption data to understand the ever-changing market conditions.

Agile

Proactive

Responsive

Working Well

Resilient

PRE-PANDEMIC

DURING PANDEMIC

Figure 1: Pandemic Effectiveness

Source: Supply Chain Insights LLC, Pandemic Study (Sept-Oct, 2020)

Base: All Respondents – (N=118) Which set of descriptors best describes your supply chain / your typical client's supply chain (for technologist/consultant) as you work through disruption?

1 is strongly disagree while 5 is strongly agree

While experiencing abrupt inventory shifts during the pandemic, Mondelez International initially believed its demand analytics wouldn’t provide as valuable information as its on-the-ground trade activation teams, given the lack of historical data. But, in reality, that wasn’t the case, says Orkun Ozturk, head of IBP Demand and Supply Chain Planning Center of Excellence at Mondelez. “We came to see that statistical forecasting provided at least as much benefit as local market intelligence, if not more, for the markets that we use it in,” he says. As a result, the snack company is digging into what kinds of quick modeling changes can aid in future sudden events, expecting it to help with such scenarios as a competitor losing manufacturing capabilities. While many consumer-facing companies responded to degrading forecast accuracy during the pandemic by trimming production and marketing, those that pursued new data sets, simulations and model development had initial success in better predicting demand, according to Harvard Business Review.

“That helps them rein in costs while not sacrificing growth, and it puts them in a stronger position once markets stabilize. With volatility likely to persist through 2021, many more companies should follow their lead,” the authors advise.

Demand planning as we know it did not meet the COVID-19 test. As shown in Figure 1, companies rate themselves less agile, resilient, and responsive post-pandemic.

As a result, companies are rethinking demand solutions to be more nimble in the use of channel data and flexibility of models. Expect a step change in the building of outside-in processes and improvements in modeling. The redefinition of the demand planning space will connect to a digital twin for what-if modeling.

Volatility and Accuracy

By Simon Ellis, IDC

see-saw

2020 was a challenging year for global supply chains, and we learned a lot about both its vulnerabilities and its resiliencies. Although it became somewhat fashionable to criticize supply chain performance, it was still impressive how well the supply chain adapted to its varied challenges in a difficult year. Although most of the discussions of the supply chain in early 2020 had been focused on supply disruptions — automotive parts factories closing in Wuhan or meat-packing plants closing in the U.S. Midwest — much more concerning but not receiving enough attention were demand disruptions. If demand did not return, it was expected the restoration of supply would not matter. By the second half of 2020, it was becoming clear that the shuttering of many businesses was having a profoundly negative effect on demand (Figure 2) and the performance of many manufacturing categories. Yet a smaller subset of companies saw demand increases — some as a consequence of hoarding behaviors (e.g., paper products), others as the beneficiaries of true demand increases (pet products, backyard supplies). Some businesses were even able to quickly pivot to high growth adjacencies, such as alcohol distilleries to hand sanitizer, clothing and housewares manufacturers to face masks and shields.

Figure 2: How Did COVID Affect the Supply Chain?

43.5% 17.4% 17.4% 13.0% 8.7%

Demand has declined Transportation/delivery delays Factories had to close Suppliers have been unreliable/unpredictable Demand has increased

Source: IDC, 2020 Supply Chain Survey

Accepting that demand has been the biggest overall challenge for companies as a result of COVID-19, it is interesting to see with some detail the kinds of issues demand planners have been dealing with.

Broadly, the problem is simply an increase in volatility and declines in forecast accuracy. Some companies have struggled to understand whether the demand increases they have seen are real or a consequence of consumer hoarding behaviors.

Predictive analytics tools can identify market, cohort and consumer (consumption) dynamics. Supply chain visibility tools can give CGs a better view into both physical inventory and data in the supply chain. Cognitive computing offers capabilities to tie segmentation logic to fulfillment and changes the conversation around short-tail versus long-tail products. Machine learning algorithms can improve the accuracy of forecasting methods and optimize replenishment. Artificial intelligence can help reduce forecasting and human errors, boosting accuracy and preventing lost sales (i.e., due to out-of-stocks).

Demand Planning Toolkit Considerations

Digging Into the

Truth

In Figure 3, almost half of the survey respondents have seen what they believe to be artificial demand spikes in some of their product categories. Others have seen “real” demand increases. The challenge, of course, is to separate one from the other and have the correct inventory plan in place to respond to future demand. Still other companies have seen demand declines, mostly from COVID-related shutdowns that they can neither control nor accurately predict. At least one manufacturer is looking at how they can attenuate their demand forecast based on “black swan”-type events. A prior 2020 IDC report used movie theaters as an example for candy manufacturers. The timing of theatres reopening will significantly affect the demand for candy, yet nobody really knows when or how they will reopen.

48.3% 30.0% 24.1% 18.7% 1.0%

Figure 3: Demand Challenges

Demand has increased, mostly due to consumer hoarding behaviors Consumer demand has declined significantly Actual consumer demand has increased significantly We have not had any demand planning or forecasting issues Something else

Source: IDC, 2020 Supply Chain Survey

Although an unexpected increase in demand is better than a decrease, demand volatility remains a problem either way when too many manufacturers calibrate production to a forecast. It is the classic resiliency “vise.”

It is also important to recognize that the COVID pandemic was not a single, large disruption but rather a series of hundreds, if not thousands, of sequential disruptions. This does not make it any less impactful, but it does give us a way to better address the vulnerabilities that were exposed.

Determine where the cracks were: Was it a supply problem, a demand problem, an inventory problem, or something else? Think about how to better manage demand volatility: Do you have adequate visibility? If you have not pursued end-to-end visibility, now is the time to start. If you have been working on visibility into parts of your supply chain, now is the time to connect those efforts. Assess the agility of your supply chain: Are you overly dependent on one part of the world or on one key supplier?

Be Clear and Dispassionate About What Went Wrong or Right in 2020

Key

Takeaways

Pandemic-related challenges surrounding demand planning’s challenges can expect to continue throughout 2021.

The heart of the problem rests in the increase in volatility and decrease in forecast accuracy.

Anticipate that consensus meetings will overtake forward views in the near term.

Expect a step change in the building of outside-in processes and improvements in modeling.

Be clear and dispassionate about what went wrong and right in 2020 to best determine vulnerabilities.

Caulipower case study