Stefan Althoff, senior product manager with Ortec, offers his view on the steps involved in the evolution of supply-chain optimization software -- and talks about the challenges that accompany that trend.
SCB: What is state of the art in supply-chain optimization today? What can systems and companies do now that they couldn't in the past?
Althoff: Today, they're mostly busy organizing and aligning between the different functions. But it still all happens between the four walls of the company itself. The difficulty and challenge lies in reaching out to supply-chain partners, to optimize across the whole process.
SCB: How do you define supply-chain optimization?
Althoff: It’s about trying to see what resources we have, what needs there are to be fulfilled on the supply and demand side, and how we can use all of our resources in the most efficient way, either to get cost-to-serve down to a minimum, or provide the greatest service for our customers.
SCB: Isn’t optimizing with outside supply-chain partners the greater challenge? How well along are companies toward achieving that goal?
Althoff: It’s a huge challenge for those companies. Everything around optimization depends on the data you have available. Data quality is very important, and not every party in the supply chain has access to everything that’s relevant for the optimization, or has much insight into it. You also have to get carriers on board, and align with them on the same processes and level of data quality.
SCB: What are some other pain points in supply-chain optimization?
Althoff: Most of the time, it's the alignment between functions. Then, when it comes to the details, it's trying to ensure that whatever's planned is executed properly. When it comes to execution, there are many factors that make it necessary to adapt and change. That kind of adaptability isn’t provided by a lot of optimization software today.
SCB: We hear a lot of talk about the application of artificial intelligence to the supply chain. Can A.I. be of aid in supply-chain optimization?
Althoff: Definitely, because it takes into account the unpredictability and human factors of execution. It’s tell us, "The most optimal plan according to mathematics would be this, but due to the effects of what happened in the past when you were executing, we suggest instead that you execute like this, because it's more predictable."
SCB: What might a supply-chain optimization solution of the future look like? How might it further evolve and improve?
Althoff: One key is to take everything that’s under the supply-chain process, from end-to-end, both within the four walls and outside the organization. Assume that you have a change in the data and need to redo an optimization. You need to be open to sharing the data, ensuring its quality and trying the new methodologies which come with machine learning or A.I.
SCB: For all the available technology, though, you seem to be suggesting that it's more of a business-process problem as companies attempt to optimize their supply chains. Would that be fair to say?
Althoff: I think that's a fair statement. There are lots of vendors who have been proven to be good in the technology. But in the end, the company needs to adapt to it, and the mindset has to change. That's an important factor.
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