Why Do Supply Chain Managers Need Predictive Analytics?

Supply chain leaders are aware that technology will have an effect on business in the next two or three years, but as research shows, only less than half (44%) currently have a strategy in place. The change will most likely come from predictive analytics, which is expected to impact the entire business, from demand prediction to after-sales modeling.

This technique has the advantage of enabling real-time decisions based on statistical estimates of future outcomes. It has the potential to enhance strategic thinking and overall performance. The difference is that it offers a proactive approach, compared with reactive actions, as deemed by historical reports.

Usability

Using predictive analytics is expected to have a clear and positive impact on improving the accuracy of forecasts, product tracing and offering growth paths.  A study from 2017 identified and classified the use cases for supply chain analytics based on importance and level of adoption. These included:

• Inventory level optimizations
• Production and sourcing optimizations to reduce costs
• Identifying and resolving quality defects and root causes
• Identifying product cost variances
• Analyzing customer service level performance
• Tracking product traceability
• Analyzing forecast accuracy and more

Although necessary, these could seem a bit too general for daily use. We will strive to examine straightforward ways of using predictive analytics, together with examples.

Demand forecast

Since good business is a trade-off between supply and demand, accurately predicting the market is literally solving the problem. The challenge is to compare the estimates deriving from past predictions with the actual recorded sales. Once in place, the system can become more granular and predict point of sale demand (per channel, per retailer, per store). It is also important to perform a deviation analysis of the predicted vs. recorded levels.

Since demand is never linear, but usually subject to seasonal influences it is of the utmost utility to predict demand for certain peak moments to prevent lack of inventory and lost sales opportunities.

Inventory management

All brick and mortar businesses face the problem of inventory. From a financial perspective, these are costs since it is a matter of money stuck in things which are stored, generating additional warehouse, security and preservation costs.

A zero inventory situation is desirable, but not always possible, so the middle ground means finding ways to keep just enough inventory to ensure the business is running smoothly without incurring additional expenses. Predictive analytics helps estimate the safety stock levels and even offers the opportunity to segment inventory by product.

Shipping planning

Once you know the demand, you can organize your shipping accordingly. To keep a low inventory, you don’t only need the total amount of shipped items, but the frequency required. Predictive analytics developers from InData Labs say that this can help optimize logistics and remove warehouse constraints, minimize handling fees and keep a minimum level of storage costs.

You can break down the planning at the retailer, distributor and channel levels. This approach has a direct impact on in-store availability and the satisfaction level experienced by clients who can always find the item they want, at their preferred retail point, even during peak season.

Predictive pricing

Many producers still use valuation methods based on spreadsheets to create future prices based on past experience to shape future expectations. The downside of this is that it can lead to selling the same item at different rates in different locations, a situation which is no longer desirable in this highly interconnected world. The solution is to create a unitary pricing model which maximizes the revenue without making the buyer feel disadvantaged by factors such as location. The model can estimate the right prices for each season and operate changes accordingly.

Depending on the industry, predictive pricing can go as granular as daily prices or demand-triggered prices. Think about the way airplane tickets or booking prices change based on searches and existing reservations.

Procurement

Finding reliable and low-cost vendors is one of the critical success elements for a business. Securing these partners for the long term is the next logical step and predictive analytics can also lend a helping hand. The data can be used to create models to evaluate vendors regarding quality, cost, and reliability. If done correctly, this should translate to a sharp decrease in the total costs and a notable increase in the security of the supply chain.

Predictive maintenance

What if you could know when a machine would break down and replace all the losses and anxiety associated with such an event with simple, scheduled maintenance? Of course, there already are some recommendations related to replacing spare parts, but those are too general. Through predictive modeling, the estimation can be narrowed down to a few days or even hours.

Knowing beforehand that you will need some spare parts, would allow you to order them to arrive just in time, thus reducing storage costs and downtime.

Post-sales

The cycle is not over once the client has swiped their card. A successful company is built on recurring business. Even service sales or accessories still create additional revenue flows which add up. Predictive analytics indicate which are the best directions to explore and which client groups are more likely to respond positively.

From spreadsheet to AI

A lot of companies are still lingering in the golden age of the spreadsheet. That means they have some sales data in Excel sheets they consult weekly or monthly and are able to answer questions about the past and make some linear predictions about the future. More advanced companies have created visual representations of the existing sources, focusing more on getting answers rather than looking at the data which provides these.

The next step is to integrate external data in visual dashboards and move away from static representations towards predictive analytics.

The final goal is to have a real-time input of data which generates almost simultaneous decisions, proposed by the machine, but supervised and approved by experts.