This blog posting represents the second of a four-part market education series, in collaboration with supply chain planning and service parts technology provider ToolsGroup.
In our initial posting in this series, we declared that one of the most promising line-of-business areas that will benefit from Internet of Things (IoT) enabled technologies applied to supply chain management will be equipment services management, especially service and spare parts management.
A longstanding challenge in service or replenishment parts planning and management has always been the ability to forecast item-level demand when such demand is sporadic or sudden. Now consider the opportunities to have demand-driven or predictive failure data and information emanating directly from the physical equipment.
But with any major business transformation, there are always foundational capabilities that come first. In the specific area of IoT enabled equipment and services management, a foundational capability is usually the need for a robust, responsive, and analytically-driven service parts planning (SPP) capability.
Yet an unfortunate reality is that many manufacturing and services organizations with lower levels of process maturity have not recognized the differing process and decision-making needs required for responsive and effective SPP. Considering a leap to an IoT enabled service management business model will likely expose this weakness.
What Makes Service Parts Planning Different?
Three fundamental differences often found in SPP are the following:
- Contracted service levels and customer contracts determine the overall parts distribution and required service response network. When there is either equipment downtime, caused by a failing part, or when equipment consumables are suddenly out-of-stock, equipment is no longer generating value for end-customers. There is very little tolerance for inventory back-orders since non-performing equipment results in downtime costs that can far outweigh the cost of the replacement part.
- Service parts component demand is often manifested in intermittent or lumpy demand signals, caused by actual equipment operational conditions or changes in operating environment. That means planning in an environment of long-tail demand, parts that exhibit larger numbers of variability, lumpy or seasonality focused demand patterns. Traditional forecasting or demand planning techniques are often ineffective in planning parts demand in such environments. That’s because SPP is far more concentrated in individual item-level planning as contrasted to product family or aggregated planning techniques. SPP planning models feature higher stock keeping unit (SKU) counts and associated long-tail demand planning computations than traditional supply chain planning models. Algorithms that capture actual parts demand, or plan for future demand need to be far more sophisticated in item level and shipping location mathematical modeling.
- Service parts networks require the need for multi-echelon and multi-tiered inventory stocking strategies tied to more predictive parts demand. Long-tail demand can be best managed by planning that factors item level and shipping location simultaneously. SPP must therefore be able to effectively manage and optimize inventory within such multi-echelon stocking environments.
A Path to the Future
Three to five years from today, even more equipment will be acquired by “services by the hour” payment methods, saving on front-end capital equipment costs for equipment operators. Physical objects such as complex equipment, engines, motor vehicles and other forms of equipment will be communicating operational performance and service needs via IoT enabled data and information flows. For equipment manufacturers, the opportunities are new lines-of-business and incremental multi-year top-line revenues flowing from such models.
The good news for IoT enabled service management processes is that the equipment itself can provide more proactive or prescriptive indications of when a part is scheduled to fail, as well as actual maintenance data related to parts failure. Such capabilities will provide added intelligence and more accurate parts demand information that will provide additional service uptime and operational cost savings for customers and service parts providers. In addition, the ability to link the physical equipment and operational data related to equipment with a robust SPP environment adds important benefits in the ability to capture and plan more accurate, and more predictive information related to service parts or consumable parts needs and requirements across a service management network.
However, the savviest businesses recognize that the end-goal is not IoT per-se, but in building the foundational people, process and technology capabilities that can best leverage the digitization of supply chain management and decision-making processes. An IoT front end isn’t much good without an equally responsive back end planning system.
Businesses that recognized the critical differences in more effective service parts management and made the initial foundational investment in more responsive SPP process capabilities will be far better positioned to harvest the benefits of smarter and more efficient network wide inventory levels, more timely decision-making and most important of all, more responsive service and satisfaction levels for equipment customers.
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