The Crucial Role of Applying Analytics in Your Supply Chain & Operations Strategies

Please provide a brief background of yourself
Hi, thanks for inviting me to do this interview. I have a computer science education and software development background, which, in the last twenty years, has been primarily devoted to supply chain management applications.

This came about through collaboration with my husband, Professor David Simchi-Levi from MIT, who is a well-known supply chain expert. Our collaborations have been both on the business side as well as through some very well-received books, such as Designing and Managing the Supply Chain, which is a textbook.

In our first company, Logictools, we developed software for network design inventory optimization and a few other applications. The company was sold to ILOG in 2007 and is now part of IBM’s business analytics solutions. It is very gratifying to know that our software has been used by over three hundred fifty companies and also by over fifty percent of the AMR, now Gartner, top fifty supply chain performers.

This was a very interesting experience because we could see how companies were using decision-support systems, as they were called at the time, for the first time to solve supply chain problems. Building the models provided a common platform for them to agree on various costs, and the ability to see some unexpected results from the models created a deeper understanding for them of the drivers of costs and tradeoffs in the supply chain.

Currently, I am the VP of Operations of our second venture, which is called OPS Rules Management Consultants. This is a supply chain operations strategy consulting company. We’re collaborating with experienced consulting Professionals and Analytic Experts to provide a data-driven and analytic approach to operations strategy that can lead to long-term transformation.

Many of the ideas we’re promoting are based on David’s recent book Operations Rules, which also explains the name of the company. The book highlights the scientific rules of how supply chains work, as well as provides a new approach to crucial operations topics, such as complexity, risk and flexibility.

What are analytics and why are they important?
In the last few years we’ve seen an increased interest in analytics in many areas of business, Part of the interest is driven by increased availability of data from various sources. Some of it is from enterprise resource planning systems; some of it is the so-called big data that’s emanating from the Internet and social media, e-commerce, and in the supply chain context from sensors and RFID tags.

I think the other reason there’s interest is obviously the capabilities in the analytics themselves have improved quite a lot in the sense that you can solve bigger problems and much faster. So, how do we define analytics, especially the context of supply chain? I would say it’s various mathematical techniques that are used to solve problems, and these problems are, for instance, network design, which can use optimization techniques.

If you’re doing warehouse design, you would use simulation. If you’re doing forecasting, it involves a lot of statistical methods. Here we see that there’s a deep need to match not only knowledge of mathematical techniques but also knowledge of the domain. This is why there are a lot of opportunities but also it’s not a very simple thing to do.

If you were asking why this is important, obviously, this is an area of investment and competitive advantage for many companies, and it allows them not only to understand what their business is doing, but also come up with new solutions and ideas for where they are going. Obviously, analytics is a very, very important component of that, and doing that in a smart way is all to a huge advantage.

Who needs to take analytics into consideration in supply chain and operations strategy?
Before I answer the question, I’d just like to talk about what makes analytics in supply chain operations more important and practical now.

The first one is that complexity has increased for companies due to globalization and the Internet. Companies have more product, more locations, more channels, and more markets as a part of the mix. This creates a lot of new challenges that require new tools. David likes to say that “common sense consulting” is not enough anymore.

The second thing is that there is more data available, as I said before, from ERP systems and other sources. The third is that the computing speed has increased, as well as database technologies that speed up data access, so analytics are much more practical and deployable.

The other thing is that many new methods and techniques that are around but really haven’t been deployed widely, and they can be a huge asset to improve performance. We know that analytics have played an important role in supply chain operations for a long time, applications in demand forecasting, transportation routing, inventory optimization, and network design. This has been around for a while.

There are also areas of new opportunity that can benefit, such as supply chain segmentation, risk management, complexity reduction, manufacturing flexibility, all of which are very important to companies. What we see is that there’s a lot of interest from C-level managers in procurement, supply chain operations in using analytic tools to better understand their operations and provide some much-needed improvement.

The other thing that we see is that they really like these skills to be part of their organization, so they don’t have a lot of interest in just using spreadsheet solutions or people just coming and going; they really want this to be embedded in their long-term strategy.

How should analytics be approached? Part 1
Analytics requires a deep understanding of both the domain—in our case we’re talking about supply chain management and operations—as well as the modeling and mathematical techniques that are required to create and solve models that will provide true, actionable insight into how to improve operations.

Many companies struggle with where to start investing resources in analytics, particularly in strategy, and they may also be concerned with the disconnect between the business requirements and the analytics process. Probably they would also prefer to have this capability in-house so that they can continue this process to support the operations strategy with analytics.

At OPS Rules we have developed a methodology based on the many years of experience that we’ve had building supply chain models. This involves first building a model of the current system, what’s called a baseline, and then validating by comparing the model with results, the details of the business. This is actually a pretty complicated process.

“Many companies struggle with where to start investing resources in analytics, particularly in strategy, and they may also be concerned with the disconnect between the business requirements and the analytics process”Edith Simchi-Levi, VP of Operations at OPS Rules Management Consultants

Once the results are acceptable, we analyze a complete set of improvements using simulation and optimization tools. The results of these models provide a way to analyze the tradeoffs in the system, as well as to provide new ideas for improvement. This is a very creative part of this process, and it allows you to come up with the ideas of what you want to model and the kinds of things you could possibly do in your supply chain or your system.

I think one of the interesting aspects of modeling is that it results of a specific scenario inconsistent with your intuition about the business.

You need to understand where the discrepancy’s coming from, so sometimes the models are just something new and surprising that you probably wouldn’t have been able to come up with without the model, but sometimes the surprise indicates some sort of a problem with the data or the assumptions, and it would require reviewing any discrepancies to see, to really understand them and make sure that the model is reflecting reality correctly.

How should analytics be approached? Part 2
This is the second part of the response to the question how to approach analytics. In the first part I talked about our methodology, so in this part I’d like to actually provide an example of a recent project that we did with PepsiCo Worldwide Flavours on end-to-end with optimization, which were give some concrete examples to some of the concepts I talked about before.

PWF recently went through a reorganization that led to a reassessment of inventory in the manufacturing plant. They have multi-tier network of three plants, four distribution centers in both the U.S. and Canadian markets; they have four hundred fifty finished goods and seventeen hundred eighty-one components and raw materials. This is not something that can be done easily, and this is kind of the complexity of real systems.

Management realized that this complexity of the supply chain it was not enoughto optimize using single echelon optimization methods. They chose to work with OPS Rules and deploy an end-to-end inventory optimization process; sometimes this called multi-echelon optimization.

So, as we discussed before, the initial part of the process is to create a validated baseline model of PepsiCo’s network, and after that is completed, you can also create an optimized baseline. You take the baseline and optimize just that model with the assumptions in the baseline, and that provides another element to the process of understanding the supply chain.

The next step, as I mentioned before, the most creative part, where you plan the various scenarios that will uncover information about the drivers of inventory in the PepsiCo supply chain. Our first discovery through this process was that most of the excess inventory was in raw materialin the plant, so the next question is what to do about that. We devised several scenarios to see what was driving that raw-material inventory.

Some of the ideas included improving demand forecast (that always seems to come up); eliminating some packaging options to reduce complexity (that’s always probably a good idea); and then changing the frequency on the side of production batches and see if that can help with managing inventory; and, finally, assessing the impact of lead times (this is the supply lead times that can impact, obviously, the raw material).

So, we examined what would happen if we changed all these parameters, but, in particular, we looked at the supply lead times and played along with reducing or increasing them. It actually turned out that there wasn’t a huge impact for that.

Then we looked at another factor that is sometimes connected but is pretty intuitive, and that is looking at the actual lead-time variability, so looking at not what the stated lead time, but wherever it really was probably too late or sometimes too early, which obviously makes planning difficult. We discovered that reducing lead-time variability even by a little had a very significant impact on total supply chain costs and reducing inventory.

The implication for this, for Pepsi was not what they expected; they were looking more at the demand forecast and packaging as the drivers. But what we found is that they really need to work with the suppliers to improve performance and better focus on on-time delivery. This wasn’t what they expected, but it was a very good insight and, obviously, a very successful project.

Read the Article: The Overlooked Opportunities of Inventory & Capacity Based Supply Chain Analytics