Exploring demand sensing: Johnson and Johnson with MIT

AI demand sensing.jpg

In the second part of our report on demand sensing, Neil Ackerman and Nitza Pierce focus on the research carried out at Johnson & Johnson with MIT Center for Transportation and Logistics

To help determine if demand sensing can work, Johnson and Johnson looked to a team of MIT students for a deeper dive into the current capabilities, the value it could provide and what would be needed to take advantage.

It’s clear that a full implementation of demand sensing across a major business is no small feat. The team at MIT Center for Transportation and Logistics spent a year mapping out the forecasting process to help determine if demand sensing was a worthwhile ambition. Their results were organized around the three demand sensing approaches, since each idea is so different. As the MIT team learned, each can be implemented separately or concurrently. They refer to the three approaches as follows:

1. Latency Reduction

This is a reduced cycle time between forecasts, and is the simplest of the three. Historically, most companies have used forecast cycle times of a month (or longer). To achieve demand sensing, this approach is used to model demand more frequently—weekly, or even daily—depending on the processing power you have, and how readily the organization can respond to new information. Then the system uses smart technology behind it to change the forecasts itself.

2. Downstream data integration.

This includes expanding downstream supply chain information in a demand model. As we know, predicting demand by analyzing past sales and forecasting into the future is most common. This approach aggregates and analyzes point-of-sale data from different regions, markets, brands, and distribution channels, to better understand consumer behavior.

3. Measuring the impact of demand-shaping actions

This both records and determines the impact of so-called ‘demand-shaping’ events, such as new product launches, price changes, promotions, or forward-buy arrangements. This approach creates an additional source of data from which new insights can be gained. There are obvious benefits here for CPG companies, but only if they can improve their data and processing capability. The old-school ERP systems are not going to work here.

Know thyself: transform your framework and close gaps

One interesting insight from the research was the variation of results. Like there is no one-size-fits-all approach to customers, there was no clear common approach that improved forecasting accuracy, but the main discriminator came down to the quality of data sets. Recognizing constraints within data quality and operational process will be a key factor to successful demand sensing. How accurate is the data you’re using to construct these insights to make decisions? How quickly can you react to the new information or do we need an alternative?

Automation will be crucial to using the real-time insights and new pools of information we now have access to, but we also need to focus on the other changes that need to happen with our available resources. The human aspect is often forgotten when we focus our attention on automation, but the collaboration between our people, processes and technology is the only way to innovate effectively. Advancing our analytical capabilities will expand what the people can do without being bogged down by the almost mindless mechanical activities that consume time.

Weed out your poor performing SKUs and identify gaps and best strategy quickly, to reveal hidden costs and understand what is impacting the current portfolio. Know if there are opportunities to improve sales or if moving towards withdrawing a product will fit the business better. Analyze the parameters to advance the insights, rather than analyzing data just to get insights.

Unlocking optimization

At our first supply chain related jobs, we all had the privilege of working with a couple of extremely talented planners. We would often ask how they were supposed to improve their accuracy without a crystal ball. It is amazing that the technology available is bringing us closer to one that can be used across the end-to-end value chain.

Predictive demand will be the key to everything. It will allow you to operate your machines more efficiently, schedule your employees, know what to produce, how to market, to name a few benefits on the never-ending list.  

This is the beginning – 99% of all organizations are not even close to executing demand sensing.
— Neil Ackerman, J&J

At J&J, we are continuing to develop the science and rolling it out into new sectors. We want to challenge the status quo because through being innovative in supply chain, by integrating more analytics, we will gain a competitive advantage in the market and create better health experiences for patient and consumers.

This is the beginning – 99% of all organizations are not even close to executing demand sensing. At J&J we have a lot of talented people and resources, but this is also about our mission to make the world a healthier place, and that means we have to stay at the cutting edge of technologies to make that happen.

Improved insights will soon be customary, but acting on that information, and achieving our strategic vision faster, is the next jump to winning the customer. So how do we change and embrace a digital transmission? Solutions for prediction, leaner operations and better forecasting will control the operation flow to the function of your inventory, and anything less than flawless opens the door for someone else to do it better.

Being ready today is not enough - being prepared for tomorrow will be the only way to keep your spontaneous customers and meet their high expectations. If you don’t, someone else will.

 
 

ABOUT THE AUTHORS

Neil Ackerman is the Senior Director, Enterprise Supply Chain Global Planning and Innovation, for Johnson & Johnson across all segments including Pharmaceuticals, Biomedical Devices and Consumer Products. He is responsible for accelerating supply chain innovation and enablement of advanced planning processes and technologies worldwide. His team is critical in bringing value-based prototyping to life.

Nitza Pierce is the Senior Manager, Advanced Planning, for Johnson & Johnson as part of the team accelerating supply chain innovation worldwide across the Pharmaceuticals, Biomedical Devices and Consumer Products segments. Prior to her current role, Nitza has held multiple positions within end-to-end global supply chain.

Tim Coulthardsupply chain, AI