In Part One, we discussed how dealer inventory management (DIM) software enables OEMs to allocate stock across dealer networks on a global scale without compromising local part availability. From incentivizing dealers to forecasting parts demand across multiple echelons, both OEMs and dealers benefit from the technology.
Now, we'll talk about how OEMs can leverage their investment in the Internet of Things (IoT) to enhance DIM software's forecasting capabilities. Here are four examples of what happens when OEMs integrate IoT data with their DIM technology.
The supply and demand associated with a particular part, whether it be a driveshaft or travel motor, determines the price for that part in the market. OEMs who combine capabilities from their parts management and parts pricing solutions are able to set prices and decide on promotional pricing and periods while accounting for price elasticity.
At the same time, OEMs using dealer inventory management software can anticipate and forecast demand based on pricing and make sure dealers have sufficient stock. To gather more detailed demand data, OEMs may collect SKU information from part RFID tags across multiple locations, assuming such IoT technology is in use.
Certain market conditions are easier to predict than others. If the price of a commodity your organization depends on goes up and down seasonally, that can be built into a forecasting software. But what if, by connecting your assets to your inventory and then dealer network, you can anticipate a need for service ahead of time, and make sure the local dealer has the parts that are likely required?
Let's say a sensored asset, such as a large server, sends a signal to you whenever the asset's temperature exceeds an acceptable threshold. You know that when these signals occur, there is a 50% chance of failure occurring within one week. Knowing this, an OEM can ship a temperature control component to the local dealer so a tech is ready when that failure is detected. This decreases (and, in some cases, eliminates) downtime for the end-user, which is an ideal service outcome.
For a dealer thinking about inventory replenishment, if 50% of demand can be predicted ahead of time, this demand can essentially be removed from a part forecast, so parts can be ordered just in time, saving the dealer the cost of warehousing and managing that inventory over time.
More than that, when dealers connect their inventory management software to their appointment systems, OEMs can have probabilistic systems that determine which parts might be needed. For example, if there are five appointments one week for automotive brakes checkups, there is a 50% chance that a certain number of brake pads are going to be necessary.
OEMs and manufacturers can develop part forecasts by analyzing historical performance and calculating future needs based on market pressures. With advanced dealer inventory management systems, dealers and OEMs can work through “unsure” and “more sure” types of forecasts.
For example, if a dealer had 12 appointments last month that required 10 parts, the dealer can’t look at just one facet over the other (i.e. appointments versus parts) but rather, weigh those factors against each other. Advanced parts management solutions can model the costs of carrying safety stock against the cost to service-level agreements and customer relationships caused by not carrying the part. Then, fixed and firm demand can be reconciled with the overall forecast.
As more systems are able to integrate into one dashboard, dealers and OEMs can set role-based visibility so each member of their respective organizations can get the information necessary to do their work in one simple interface. With that, new IoT-powered service solutions are able to integrate systems of systems, closing the loop on the manufacturer-dealer-user relationship.