The new supply chain reality: Visibility is no longer enough
Imagine getting the following email:
“Your phone fell out of your bag into the street. Yesterday.”
You’d be glad to get the info, because you’ve been turning your home upside down looking for your phone, but you’d also be frustrated because it would be too late to do anything about it.
Knowing just isn’t good enough anymore – we want to be able to foresee and prevent problems before they happen, which is why we now rely on so many predictive platforms. For example, most of us turn on a map app like Google Maps or Waze when we get into the car in order to avoid traffic, road work, and other hazards. Even if we know the way and how long the drive usually takes, predictive navigation apps help us make the right decisions for a specific journey.
Moving forward to predictive analytics
Manufacturers, distributors, 3PLs, and all members of a the supply chain now understand that supply chain visibility, i.e., finding out about problems after they happen, is no longer good enough. For example, accurate inventory planning is critical for retailers in order to avoid lost sales, stock-outs and poor service reviews.
In the past, retailers based their planning on historical averages and static lead times. But the rise of e-commerce over the past decade has made market trends way more complex and variable, and often include multiple sales and delivery channels.
Retailers have responded to the uncertainty by buffering and increasing their inventory. They know that if they can’t provide the consumer what they want ASAP, the consumer will go to another seller that can.
The problem with big inventories is that they’re extremely expensive.
In fact, the annual “State of Logistics Report” from the Council of Supply Chain Management Professionals (CSCMP) valued the total business inventory at $2.49 billion in 2016, up from $2.47 billion in 2015.
The only way for retailers to minimize their inventory without hurting sales and service is to predict and respond to supply chain issues before they happen. The global supply chain is impacted by a wide range of factors including weather, traffic, suppliers, consumers, carriers, and ports. Predictive platforms (like Contguard) tap into sensors, radar, satellites, smartphones, and other devices that make up the IoT to gather real-time Big Data.
Machine learning algorithms then assess this data to make predictions and recommendations for retailers to respond to chain behaviors before they happen.
Weathering the storm with predictive analytics
Let’s look at a situation where extreme weather is expected to impact a certain area. In such a case, predictive analytics will alert retailers and suppliers about the expected weather conditions, directing shipments away from routes that could be impacted, and assessing which products are likely to be in demand as a result of the weather.
Suppliers can then send extra inventory in the affected area, or promote similar products that appeal to the same need.
Retailers will also receive recommendations on whether to alter prices or seek alternate suppliers if the supply lines are impacted. Real-time predictions can turn the problem into an opportunity.
Predictive analytics are also more precise than predictions based on past behavior. While traditional solutions can tell you that a certain voyage takes twenty days on average, they can’t give you a specific time prediction for a particular shipment based on time-sensitive factors like port congestion on the date of arrival.
Contguard’s predictive analytics enables agility
Contguard’s predictive analytics algorithms are able to foresee delays and possible issues based on a variety of factors, accurately predicting the arrival time of each and every shipment. This then enables agile supply chains, which allow retailers to meet consumer needs without stockpiling enormous inventories.
McKinsey & Company assessed 10 supply chain capabilities, in 250 companies, plotting them on a scale of one to five for agility scores. The research found that companies with the more agile supply chain practices had service levels 7% points higher and inventory levels that were 23 days lower than their less agile peers.
Similarly, research on “Next Generation Supply Chains” by PwC found that those companies rated as supply chain leaders averaged 15.3 inventory turns per year, while less-agile companies achieved only 3.8 turns.
Contguard provides predictive analytics and concrete recommendations for improvement before problems occur in the supply chain. Through careful analysis of the multitude of data points generated through the IoT along the supply chain, together with proprietary algorithms, Contguard is helping its clients to reduce costs, increase revenue and improve service.