Predictive Analytics are Your Supply Chain’s Best Secret Weapon
Published on Feb 8, 2023
Companies all over the world have begun using predictive analytics to optimize their supply chains.
Predictive analysis can help plan production to meet consumer demand forecasts, make warehousing and inventories more efficient, and save time and money by rerouting shipments before blockages occur. It can also create fleet maintenance schedules that minimize downtime, and introduce dynamic pricing that keeps companies competitive and fattens the bottom line.
This article will delve into the secret weapon of predictive analytics and how it can help streamline your supply chain.
What is supply chain predictive analytics?
Predictive analytics is simply taking data about the past and using it to predict future trends. It is similar to the algorithms used on online video and social media sites to present relative content to users.
When applied to the supply chain, however, predictive analytics is used to make logistics networks, factories, and warehouses more efficient by giving management the time needed to prepare for future trends... well in advance.
How is it different from other types of supply chain analytics?
In addition to predictive analytics, supply chain professionals use descriptive, diagnostic, and prescriptive analytics.
Descriptive analytics is historically collected data. This is the information that tells what was produced, how long it took to be delivered, what routes were taken, and essentially every other piece of quantifiable data that was collected from the supply chain. Descriptive analytics forms the basis for predictive analytics.
Diagnostic analytics tracks what went wrong and what was done right in the past. This information is also brought into predictive analytics so that potential problems can be avoided before they even have a chance to begin.
Prescriptive analytics is used to optimize the supply chain to run more efficiently in the future. It builds on descriptive, diagnostic, and predictive analytics by using subsets of data in those categories to measure the likelihood of various scenarios based on historical data.
It then gives its recommendation as to the course of your supply chain strategy.
How does predictive analytics give you an advantage?
Predictive analytics can bring many advantages to supply chain management. They can be grouped into 5 major areas; predicting demand, optimizing inventory, planning logistics, scheduling maintenance, and dynamic pricing.
Predictive analytics can find recurring patterns in supply and demand, and show supply chain professionals when to prepare for a surplus of orders or a slow season. This could take into account past market demand, as well as economic forecasts.
In tandem with predicting demand, predictive analytics can help keep inventories at healthy levels. This is especially important for perishable or seasonal items. Companies can save by not over-stocking, and keep customers happy by maintaining adequate inventory.
Unforeseen blockages due to traffic congestion, bad weather, or road maintenance can seriously affect a supply chain and profit margins. Predictive analytics can spot conditions that have led to delays in the past and suggest alternate routes or alternative carriers.
Predictive analytics can warn maintenance teams when a vehicle is due for inspection, and help plan regular maintenance so that the fleet always has enough vehicles available to meet demand. This is one of the most common and easily implemented uses of predictive analytics.
Dynamic pricing is regularly used to increase margins in the hospitality, passenger aviation, and ride-hailing industries. It can also help the logistics industry by looking at projected demand, expected fuel costs, and other factors; when margins are high, you may wish to offer a discount in order to attract new customers, when margins are tight and demand is expected to be high you may be able to raise prices.
How can you use predictive analytics?
The strength of your predictive analytics will come from the quantity and quality of data that you feed into it. This data could include distributor reports, data from your supply chain management software systems, inventory data, maintenance records, and any other data routinely collected by your company, your suppliers, or partners. This data will be in many formats, from slips of paper to spreadsheets, it will all need to be converted to a form that can be used by the predictive analysis algorithm.
This data-gathering model also uses a large pool of public data. This includes things like weather reports and forecasting, consumer spending, traffic data, industrial production records, and more. Finally, the data must be presented and visualized in a way that is meaningful, actionable, and as reliable as possible.
Supply chain predictive analysis software can be purchased, usually with some customizable features and setup assistance. Some logistics companies and manufacturers prefer to hire a software development company to create a custom predictive analysis system just for them.
Predictive analysis has been used by large corporations with complex supply chains for years. Amazon uses it to predict customer demand and make production and delivery orders in advance. Apple uses it to manage production and inventory. As computing costs continue to go down, AI and algorithmic software come more within reach of medium-sized supply chains.
In the future, it will likely become an industry standard, but for now, early adopters of predictive analytics will have a secret weapon that gives their company a leap ahead of the competition as we inch closer to Industry 4.0.
If you're looking for help in implementing or integrating predictive analytics in your supply chain,Redwood's reporting and analytics team can help, while making sure it's connected to all the correct data sources and providing easy-to-use reporting along the way.