Spares Pooling: Machine Learning To The Rescue
Spare parts. Every original equipment manufacturer (OEM) has to maintain an inventory of them. Machines break, pieces of equipment fail, and customers need spares to make repairs without delay.
In today’s on-demand global market, spares replenishment must work like magic: prompt, cost-effective, and seamless. Otherwise, prolonged downtime due to equipment failure can have serious direct and indirect impacts, for example, dysfunctional surgery suites in hospitals, flight cancellations, production delays, unfulfilled orders, or poor quality leading to product recalls.
Ensuring the right parts are on hand when needed
Having a sufficient and efficient supply of spare parts is a vexing inventory-management scenario. Forecasting parts-failures at the points of demand and planning for replenishment are essential, as parts are very expensive and costly to keep in stock. Meanwhile, warehouse management teams must routinely juggle multiple, complex scenarios with contradictory insights, forecast failures, and inventory based on complicated predictive analytics.
Demand and consumption forecasting is done at the stockkeeping unit (SKU) level by taking into account the way they handle failures in KPIs. Doing so can minimize the costs of downtime. The key is to apply a risk-ranking system estimates the consequences of a failure to the business, the probability that a failure will occur, and the risk the business is willing to accept.
In terms of a repair strategy, a lot depends on the OEM’s policy on parts replaceability versis repairability. Availability of onsite skills within the maintenance team and time to repair are crucial factors in these decisions. Sometimes, it is more cost-effective to replace the unit and to repair the old unit at one location. But stocking whole units is far more expensive than individual SKUs.
Calculating the efficient safety stock levels at the SKU level must take into account the predictability of demand, delivery lead times, delivery accuracy, obsolescence, transportation costs, and more. The strategy must include reorder points (ROP) and reorder quantities (ROQ), as well as cost-based optimization of order quantities and order cycles at the supplier or product level. Combining automated routine orders with order suggestions for specific important items as well as automatic exception management must also be considered.
Pooling dramatically changes the stocking strategy
Most OEMs have multiple warehouses with millions of dollars’ worth of inventory. If they were to stock at least one piece of each part in all these warehouses, the cost would be prohibitive. This is where parts pooling comes in.
Pooling builds on data that proves aggregation reduces variability and uncertainty. This implies that demand variability will decrease by aggregating spares across locations.
For example, low demand from one warehouse location offsets the demand at another warehouse location. This decline in variability allows a reduction in safety stock and reduces average inventory. When executed well, it is an excellent approach to reduce inventory at individual warehouses without impacting customers.
Shared and pooled warehousing have the potential to transform spares management of small and midsize businesses in competitive markets. This is quite valuable as business networking strategies and collaboration in logistics continue to gain momentum.
Easier said than done
Location-base parts pooling requires adding many highly complicated variables to the current set of variables. In this case, predictive modeling is no longer sufficient. It is time to move to artificial intelligence (AI) and machine learning models that take into account multiple variables and blend them to enable better decision-making in two critical areas:
- Warehouse location and product flow: Decisions on whether to have many warehouses close to customers or to have more centralized locations can balance the advantages of aggregated demand and lead times. Proximity to customers and other factors may make maintaining more warehouses more advantageous.
- Transportation: Managing parts pooling across locations means accounting for the geographical area along with logistics like availability and reliability of the ground transportation network, flights, or water corridors.
Within months of implementing warehouse pooling optimized through machine learning, most OEMs’ customer KPIs improve while simultaneously minimizing the capital invested in spares.
AI and machine learning models merge cost, operations, and criticality analysis for the relevant scenarios while factoring location criteria at a granular level to automate actions and enable better decisions. Optimization enable OEMs to reduce inventory without impacting customers by using a pooling model.
To reduce risk, OEMs can start by implementing an ML model with a few fixed locations and fewer optimization criteria, like the cost of parts, shipping costs, lead times, and the probability of failures. As the system matures, they can improve this model through a dynamic pooling model capable of handling more factors.
Lenient spares inventory management is a strict no-no as the costs of non-availability can be disastrous. Machine learning-enabled systems are set to transform the way warehouses operate. But before OEMs take the plunge, they must make sure they have the data and talent they need.
See some real-world examples of how machine learning is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits, in the study “Machine Learning In The Real World.”