Avoiding the capacity cost trap: Three means of smoothing under cyclical production planning

This is the author accepted manuscript. the final version is available from Elsevier via the DOI in this recordCompanies tend to set their master production schedule weekly, even when producing and shipping on a daily basis—the term for this is staggered deliveries. This practice is common even when there is no marginal cost of setting a new schedule. We argue that the practice is sound for companies that use the ubiquitous order-up-to (OUT) policy to control production of products with a significant capacity cost. Under these conditions, the length of the order cycle (time between schedule updates) has a damping effect on production, while a unit (daily) order cycle can cause significant capacity costs. We call this the capacity cost trap. Developing an analytical model based on industrial evidence, we derive capacity and inventory costs under the staggered OUT policy, showing that for this policy there is an optimal order cycle possibly greater than unity. To improve on this solution, we consider three approaches to smoothing: either levelling within the cycle, deferring excess production or idling to future cycles via a proportional OUT policy, or increasing the length of the cycle. By deriving exact cost expressions we compare these approaches, finding that smoothing by employing the proportional OUT policy is sufficient to avoid the capacity cost trap.Norwegian Research CouncilBIA programm

Inventory performance under staggered deliveries and autocorrelated demand

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordProduction plans often span a whole week or month, even when independent production lots are completed every day and service performance is tallied daily. Such policies are said to use staggered deliveries, meaning that the production rate for multiple days are determined at a single point in time. Assuming autocorrelated demand, and linear inventory holding and backlog costs, we identify the optimal replenishment policy for order cycles of length P. With the addition of a once-per-cycle audit cost, we optimize the order cycle length P∗ via an inverse-function approach. In addition, we characterize periodic inventory costs, availability, and fill rate. As a consequence of staggering deliveries, the inventory level becomes cyclically heteroskedastic. This manifests itself as ripples in the expected cost and service levels. Nevertheless, the cost-optimal replenishment policy achieves a constant availability by using time-varying safety stocks; this is not the case with suboptimal constant safety stock policies, where the availability fluctuates over the cycle

Service levels in make-to-order production: 3D printing applications

This is the author accepted manuscript. The final version is available from Palgrave Macmillan via the DOI in this recordConsumer 3D printing services offering to print customer’s models on demand must achieve high service with the available capacity. While the bulk of production tends to come from in-house capacity, overtime is also viable for managing demand peaks. This chapter shows how 3D printers can manage their order book releases to deliver on time, while keeping production costs low. Applying order book smoothing to a numerical case reveals a cost–service trade-off that is not convex, as typically seen in inventory models, but of sigmoid type. This results in two attractive configurations: Atrocious service at a minimal cost, or near-perfect service at a reasonable cost

Implementation scenarios for 3DP

There are many ways to implement 3D printing to gain a commercial advantage. This chapter provides a detailed examination of the various business models that are currently being employed within the industry, focusing on home, retail, and outsourced manufacturing bureaus. Subsequently the work considers prospects for future approaches to implementation, exploring a range of potential opportunities that may be employed competitively in the near future