Seven-Minute Synopsis on From Buzzwords to Business:
The Reality of Demand & Supply Planning
We all have good intentions to watch a webinar recording, but things come up. We’ve taken all the key points from the event and summarized them for you. After reading this you may even want to watch the event.
In today’s business environment, the complexities arising from volatile consumer demand require a higher degree of planning efficiency. Leveraging analytics enables quick response to demand changes, delivering a consistent customer experience across channels. A discussion around how companies can simplify data management, streamline common planning processes, and provide analytics with sophisticated workflow creation and deployment features.
National Executive Retail & CPG Solutions
Dr. Peter Turney
President & Co-Founder
CT Global Solutions
Principal Analyst Consultant,
CT Global Solutions
Director Global Manufacturing Industry Marketing Practice
How would those in the early stages of advanced analytics adoption benefit?
“I know everybody, they get into analytics want to hit the big home run and have a big win... What I found when I was driving that adoption to confidence it was the little wins. Right away the little wins were things like triggers, was there a cost change? Okay, if there is a cost change these are the steps that will happen and automate it... Those triggers were the earliest part of the analytics that people really gravitated to in the organizations I was in. Once they have confidence that triggers were working, they started getting confidence in the automation and optimizations. It went all the way to the CEO’s the CFOs because then they start seeing the value and the trust that some of these early small wins were having so that they start trusting the big predictive analytics that comes later... Start off with something simple then we can start layering in a lot of other models that can bring wins and confidence.”
What does machine learning really do? & The benefits of analyitcal methodologies
“There are two forms of AI machine learning that really affect folks in supply chain, one is supervised machine learning and the other is unsupervised machine learning. Unsupervised machine learning for example if you have a pool of data and you want to know who the cigarette smokers are, segregating the data into slices and dices so you can analyze them further. Supervised machine learning are things like predictive analytics and forecast accuracy... If you want to get into causal models and you want to get into things like dummy variables and events then you need more advanced models. The most obvious one for supply chain is one of inventory, service levels and profitability. The tighter you get your forecast when you pass that over to your inventory planners and supply planners, the better inventory you will be able to carry.”
Can you provide examples where you’ve seen demand planning impact an organization?
“In the career that I’ve had, I spend more time with companies that are missing pieces that they should implement to really have great success. A lot of companies have great systems but then they find something that isn’t quite providing them with the solutions. I’ll give an example; we are working with a large CPG company. Recently their inventory levels went from 3 billion to 7 billion overnight and you can understand the negative impact that had not just on their balance sheet but on the profit and loss statement as well... I think companies will choose to focus on those areas where they have the biggest need. It will depend on the industry and their specific requirements. The stakeholders who will be making decisions, it will be important to their success.”
How do we manage supply issues, changes in customer demand and deal with exceptions?
“We’re so quick to look at our historical time frames of 1 year, 2 years and seasonality graph. Recently, with advanced compute power we have the ability to look at so many timeframes all at the same time and add demand sensing aspects to it. We can start reacting to it before a product arrives. Is my demand in the last two weeks shifting so much that I’m going to end up with excess or is there so much demand I can’t wait for regular re-order points?”
“What we’re dealing with today is disruption, I don’t want to overuse that word. We have to move not only from demand sensing and demand shaping but also apply that to supply planning. Really an instantaneous almost supply response to changes in demand and changes in supply. It used to be most products were easily forecastable, now most products are not.”