Dr. Nada R. Sanders is Distinguished Professor of Supply Chain Management in the D’Amore-McKim School of Business at Northeastern University in Boston and is also a sought-after speaker and business consultant. She is the author of several books, including Supply Chain Analytics (Prospect Press 2018); Big Data Driven Supply Chain Management (Pearson 2014); Foundations of Sustainable Business (John Wiley & Sons 2014); Supply Chain Management: A Global Perspective; and co-author of Operations Management, now in its 7th edition.
Compendent caught up with Dr. Sanders to learn more about her work and the future of business operations and how businesses and workers can adapt to the changing workplace.
What first got you interested in supply chain management?
When I was working on my PhD dissertation I had data that linked companies with their customers and their suppliers. I worked on a forecasting project and became convinced that to get the best results, companies needed to link their customer forecasts with their production processes and supplier orders. It seemed logical that these decisions should be made together and that the forecasts of customer orders would drive other business decisions.
I was able to develop a method that improved the customer forecasting process as well as company performance. This was just before the term ‘supply chain management’ came into vogue. Then my dissertation advisor called and said “There is this new thing called supply chain management everyone is talking about – but you are actually doing it!” That was the beginning.
You wear many hats: professor, speaker, author, consultant. Which role do you enjoy the most and why?
I enjoy all of them equally as they complement one another. Each role brings knowledge and benefits that enhance my ability to do a great job in the other roles.
As a professor my job is to create new knowledge through research, and then disseminate that knowledge through teaching and writing. Most people don’t understand that doing research is key to being on the cutting edge of your field and we can then bring the latest knowledge to the classroom. Research, however, can be theoretical and is best when it is combined with consulting – as it links theory to real-world experiences. Together they provide knowledge that combine practical experience with latest theoretical advancements.
These roles also enhance being a good speaker and author. Research gives one the latest knowledge, consulting the latest practical experience, and teaching helps to develop speaking and writing skills. Having knowledge is not enough. One has to learn how to convey that knowledge in a way that an audience can understand, be moved, and be inspired.
The same is true of writing. I believe speaking and writing need to be such that an audience – or reader – can easily understand what is being presented, how it relates to them, and get content that they can use. There is an art to this process and it is developed over years of teaching. All the roles combine and enhance each other.
We hear a lot these days about “big data” playing an integral part in business decision making. In what part of the supply chain do you think big data has the most potential to make an impact?
Big data is part of life today. It is everywhere. Analytics algorithms and AI are doing miraculous things such as diagnosing melanoma and driving cars. What is unique about supply chains, however, is that a supply chain – and a business – is a system. That is very different compared with improving just one process.
For a business, and its supply chain to be efficient and/or profitable, it must be managed as a system. Otherwise a problem in one area of the system – such a shortage on the supply side or a surge in demand on the customer side – will create problems in other areas.
Think of a supply chain as a garden hose of water. You have to make the water flow through the hose smoothly and even something as simple such as a knot in the hose will slow down the water flow. Well, a supply chain works the same way. The key is to have the flow of products, information, and financials flow smoothly and uninterrupted. Therefore, big data and analytics lead to success when they are equally applied along the entire business and supply chain.
An enterprise will fare much better if it uses lower levels of analytics and big data, but uses them consistently across the enterprise. Often, companies invest in analytics on one side of the supply chain – such as in customer-centered applications – but are then unable to meet customer expectations as the system cannot deliver on the promises made.
Is it possible for the average small business owner (say, the owner of a local service company) to harness big data to improve their operations?
Yes, most definitely. The key is to start gradually building capabilities that connect elements of the business. Most organizations follow a journey in their implementation effort that builds big data capabilities over time.
There are four stages of evolution or maturity along this journey. The first and most basic stage is digitizing and structuring the data. It consists of the steps that ensure the data are generated, such as from customer sales, and organized in such a way that can be used by end users. These techniques include “scrubbing” the data to remove errors and ensure data quality, placing data into standard forms, and adding metadata that describe the data being collected. This is the first stage – having good and clean data in digital form.
The second stage is making the data available to all. It can be a powerful driver of value in and of itself, and it can also be an important step in integrating datasets to create more meaningful business insight.
The third stage is applying basic analytics, which essentially covers a range of methodologies, such as basic data comparisons and correlations. Here relatively standardized quantitative analyses are used, such as descriptive analytics. These do not require customized analyses or deep analytical skills.
The last and highest level is applying advanced analytics, such as predictive analytics, automated algorithms, and real-time data analysis that can create radical new business insights. However, not every business needs this last stage – especially not small firms. Small firms can make tremendous gains by simply having data in digital form and using simple descriptive analytics to better understand their customers and markets.
Are there instances in which big data can do more harm than good in a business situation?
I am a forecasting expert. For decades I have warned companies of the problems of bad data and have encouraged them to make sure that their data is clean before they use it for decision making. The more data we have the greater the chance there will be problems with the data – and that is certainly the case today.
We often have a situation of GIGO – garbage in and garbage out. I have seen this many times and it creates huge problems for companies. The results mislead decision making and it then becomes very difficult to pinpoint the source of the problem. Company leaders must ensure that their data is clean, ‘scrubbed,’ free of biases, and that the events from the past – represented in the data – will continue into the future. This is one of the most important things companies can do. In fact, if they cannot be assured of the quality of their data they are better off not using it and starting slowly in the analytics implementation journey.
In your latest book with co-author John Wood, The Humachine, you talk about artificial intelligence in business. Do you think all the talk about AI replacing humans in the workforce is something the average workers should be concerned about?
Everyone in the workforce should be concerned about AI in business. It is no longer “business as usual.” Machines are indeed taking our jobs. It began slowly with teller machines and automatic checkouts. Now we see it everywhere, from automated personal assistants, driverless cars, and drones delivering packages. Familiar jobs are going away.
The good news is that more jobs are coming and they are coming very quickly– new jobs and new versions of old jobs. The secret is to be ready. And most people in the workforce – at all levels– are not ready. They are thinking of old jobs and old skills.
Here is something we all need to understand. Machines are great at repetitive tasks, like crunching vast amounts of data efficiently and precisely. But, machines cannot replace human skills. For generations we have stressed the importance of technical skills, and yes, these skills are important. But in the process we have forgotten to cultivate human skills – the very skills that cannot and will not be replaced by machines, such as communication, negotiation, critical thinking, creative problem solving. Yes, a machine can optimize your stock portfolio, but a client wants a human to explain risks and outcomes, and assure financial security. A bot can place a purchase order, but to iron out terms of contract, you need human negotiation.
Workers today need to be developing and cultivating uniquely human skills. These are the skills that we have forgotten, and these are the very skills workers will need to be ready for the jobs of the future.
To learn more about Dr. Sanders and her work, visit her website: https://www.nadasanders.com/
