From ski socks to paddling helmets, mountaineering boots to the latest in backpacks, Mountain Equipment Co-op (MEC) outfits millions of Canadian outdoor enthusiasts each year.

Headquartered in Vancouver, MEC is Canada’s leading retailer for outdoor clothing, gear and services. Founded in 1971 by six mountain climbers unable to purchase the gear they desired from conventional retailers, the company now serves its three million members – roughly one in 10 Canadians — through 12 bricks-and-mortar stores, a website, catalog and telephone sales.

Those members also participate in the success of Canada’s largest consumer cooperative, so the company constantly considers improvements in efficiencies.

MEC has been partnering with JDA Software Group for nearly two decades, beginning with its implementation of JDA’s Merchandise Management System. MMS, says the co-op’s CIO Georgette Parsons, “is our core transactional system,” used to do base inventory management, determine how warehouses are managed and generate purchase orders. JDA financials hook onto MMS, as does the JDA warehouse management system. “MMS is our core back-end system and manages all our POS records and any inventory transactions,” Parsons says.

MEC later added Advanced Warehouse Replenishment, a system developed by E3, a company subsequently purchased by JDA. AWR helped automate product replenishment from vendors to the warehouse or distribution center, allowing the co-op to create accurate demand forecasts and help staff focus on profitable inventory purchasing at the SKU level. Inventory costs were reduced without affecting high service levels, Parsons says.

As time passed and the company grew, however, “it made sense to … get away from any ‘manualness’ in distribution,” Parsons says. “The last manual piece in the process was the reports our people pored over to determine how much product to send to stores.” MEC implemented JDA’s Advanced Store Replenishment, which provided the additional benefit of allowing the co-op to eliminate mountains of paper reports.

“Our service levels, a key metric in our business, have improved quite significantly with ASR,” Parsons says. “We can correlate improvements in service levels with improvements in sales and also see if we have any imbalances.”

Using ASR, the co-op found that service levels were a little lower for its web store. “We were actively able to address the situation, which has resulted in very strong improvements in sales on our website,” Parsons says. “This is a result of having the tool that enables us to see the difference in inventory availability and service level for each location.” Another benefit of ASR is a reduction in out-of-stocks.

Segmentation analysis
Customer loyalty is a valuable commodity for MEC, and 97 percent of its members say they would recommend the co-op to others. It implemented JDA’s customer relationship management (CRM) solution when it launched its e-commerce site in 2001. MEC keeps only one record for each co-op member, regardless of whether that customer shops in one of its stores, online or by phone. It made sure that it invested more on the front end and “went through a lot of pain” to ensure e-commerce would be integrated with the backroom system from the first, Parsons says.

The Canadian co-op uses segmentation analysis to categorize its members into groups. “Outdoor Enthusiasts” are really into outdoor sports, shop frequently and spend more money. “Outdoor Participants” are also loyal but shop fewer departments and spend less money. “We find this is really useful for targeted e-mails,” Parsons says. “We have a very complete picture of our members.”

JDA’s solution sets begin forecasting by considering the behavior of an item and the way it sells according to historical patterns, says Wayne Usie, the company’s senior vice president for retail. “You have slow movers, high-velocity items, fast movers — they all have different characteristics. Behaviors are evaluated from past performance, and then mathematical algorithms are used to create an accurate forecast going forward of what you believe you’re going to sell.”

Demand forecasting
The process is necessary to obtain a seasonally-adjusted demand forecast, giving the best possible prediction of what retailers think they are going to sell. Forecasts can be made by item, by store or by groups of items or stores, depending upon the behavior of the specific items.

“The retailer will consider what the item has been forecasted to sell, without constraints,” Usie says. “Then the fulfillment or replenishment side determines the constraints and optimizations that need to be performed to meet the best order today, maximizing inventory investment.” This phase takes into account whether a full truckload must be purchased or if certain vendor requirements must be met, such as purchasing more volume to receive a better price per unit.

POS data history is used to feed ASR’s forecasting engine, and there are issues that must be considered before determining what should be picked at the warehouse and how frequently, including the minimum presentation amount that must be on the store shelf, frequency of deliveries and whether split caseloads are possible.

Mountain Equipment recently struck a deal with JDA to migrate to a new Java-based POS product that will give it additional CRM and enhanced POS capabilities. “It looks very intuitive for cashiers and is a huge step forward for us,” Parsons says.