Long replenishment cycles and purchasing restrictions are a constant challenge in demand planning. Add volatility to the mix, and companies find they are either overstocked or not ready for delivery. "Gremlin Electronics Inc." (name changed for confidentiality reasons) sought an automated solution to manage both planning and delivery.
UK-based Gremlin Electronics is one of Europe's leading electronics companies. The components used in its products are designed by Gremlin but manufactured in China. Component delivery schedules range from 60 to 150 days as shipping containers from Asia to Britain takes about six weeks. To satisfy the customers’ needs with earliest possible delivery times while keeping stocking costs low, quick reaction and flexibility are essential.
In order to achieve this balance, sufficient stock is required to allow for long lead times and to maintain delivery readiness. Gremlin's clients expect delivery within 24 hours after placing a regular order. Purchasing, however, has to order components much further in advance. If the demand ceases or changes, items become surplus and overstock -- a common challenge for many companies.
If, on the other hand, demand rises, purchasing can't acquire components quickly enough. To reduce just the shipping time, higher air freight charges must be paid. Even lower-cost sea cargo is a bargain only if cargo containers are filled completely. This usually means regular purchasing of surplus amounts. Combining shipments-- that is, aggregating demand from a number of suppliers -- doesn't always solve the problem. Once a container is loaded, orders can't be revised.
Gremlin Electronics’ range of products includes about 3,000 active items annually and about 600 items in either a phase-out or a phase-in process. Planning these phase-ins and -outs is an additional challenge which, if automated, requires appropriate software support.
Intense monitoring of the phase-in process, including immediate correction of planning, is an established part of the routine. Phase-out, which is more difficult, is measured by the residual stocks of phased-out items and in the residual demands that cannot be covered anymore.
Gremlin implemented the Diskover SCO software system to simplify the phase-out task. The system surveys the demand development and proposes items for phase-out. During the process, it calculates remaining stocks and then under-stocks all the components associated with a phased-out item.
How to handle exceptions while 20,000 orders pour in each day
Gremlin's customers are specialized trade and wholesale companies. Demand and material planning with these B2B clients requires a high level of flexibility. Failure to meet expectations can result in penalties. Gremlin processes about 20,000 standard orders a day, most of them for overnight shipment.
A small number are categorized as “exceptional.” These exceptions are sporadic, usually very large numbers of items to be delivered within a defined period. Exception-planning is an important but difficult task for materials planners. Exceptions include marketing campaigns or store openings for which the customer needs large batches of certain items just once. If the customer alters the order after it's placed, stocks inevitably increase. Returns and allocations contribute their fair share to a rising stock-level as well.
Gremlin's main target was to increase availability for delivery while significantly and sustainably reducing costs -- including stock and storage, air freight, penalties, and inevitable depreciation and scrapping.
Gremlin was able to use Diskover SCO's advanced demand- and materials-planning functions in support of its existing ERP system. A specific feature of Diskover SCO is its simulation function. The system simulates the entire supply chain's performance on a daily basis over a certain period of time (usually the previous year) and helps select the best alternative for each situation. Gremlin defines the parameter settings of the ERP/Diskover SCO system by running a variety of "what if" scenarios based on the question: “What logistical performance would have been reached if we had applied the strategies in question over the past year?”
In the second part of this article, we'll look at how Gremlin uses these parameters to optimize materials planning.