The Road Back
Transforming Banks for a Digital Future: The Winners, The Losers, and the Strategies to Beat the Odds
The Road Back
Leiner is a cautionary talebut not an unusual oneof a cash-rich company in the midst of a go-go run that allowed itself to become information poor, and eventually suffered for it. It's a dangerous chain: As operations expand quickly, the habit of closely tracking fundamentalsprofitability measurements, capital expenditures, customer service performance, inventory levels and manufacturing capacitydiminishes, and the fear that something will arise to slow the business is foolishly cast aside. Companies undisciplined about data are too often instigating their own slowdownwithout detailed information, supply chain and accounting operations suffer, customer service slips and margins wiltand then find themselves in a losing battle to access the internal data required to get back on track.
"When Leiner's business tumbled, the executive team didn't have enough information to make important decisions about how to salvage the company," says Coles, who was appointed Leiner's interim chief financial officer in October 2001, a position he held until February 2002. "In the average high-growth organization, 20 percent of operational data reports are actually read and 80 percent are ignored. That's bad enough. But Leiner ignored even more reports than that, because so little of its data was valuable. That had to be fixed. At any company, if the right things get measured, the right things get done."
A&M, the lead player in the turnaround, and Leiner's management brought in supply-chain consultants and a cash flow software provider to help in the effort. Their mandate: pinpoint data that was germane to the company's current operations, toss out irrelevant, legacy information, and modernize systems so they provide a minute-by-minute picture of what management needs to know to run operations.
Manufacturing and inventory were among the first problems to be tackled. A&M's team ran a channel profitability analysis to assess earnings margins by account. That generated a long list of products that were being produced for customers who were costing Leiner more money to service than they were contributing to revenue. So the company decided that if these loss-leader customers weren't willing to pay more, Leiner would simply exit those businesses and drop the accounts. This exercise pruned Leiner's customer base by about 50 percent and the number of products it made by 60 percent. "Those sound like quite dramatic reductions, but the impact on sales was only something like 15 percent," says Coles. Which means, Coles adds, that 60 percent of what he calls Leiner's operational complexityproducts that were not making money and taking up significant manufacturing time and expensewas responsible for only 15 percent of revenue. Having eliminated these unprofitable customer relationships, Leiner shuttered three of its five factories, chalking up $40 million a year in savings.
Leiner's turnaround experts then focused their attention on the two remaining plants. The primary problem at these factories, it turned out, was excess inventory, again the result of an information shortfall. When an order was placed, Leiner's MRP system would issue a materials requirements plan blindly, without knowing whether the factories had enough capacity to handle the job and without accurate forecasts about future demand or current customer needs. Leiner's procurement team would then purchase raw materials to fulfill the MRP request, but the amount of raw materials bought almost always exceeded production capacity at some point. The result was excess inventories, which inevitably led to a cash crunch. So to save money, the procurement group would arbitrarily decide which raw materials to buy and which to put off. Ultimately, that meant some products couldn't be manufactured and some customersit could be any one of Leiner's customerswould not get their deliveries on time.
"It was a vicious cycle of overcapacity in the plants leading to too much inventory and then a panicky decision to cut inventory randomly, which led to a drop in production; at any part of the cycle Leiner was losing money," says Bob Murray, president of REM Associates, supply-chain consultants hired by Leiner. "During Leiner's high-growth phase in the 1990s, when vitamin sales took off, the company let its manufacturing database and infrastructure fall apart, instead of shoring them up in a way that would enable Leiner to make as much product as the market demanded, but always make it efficiently and profitably."
Over six months, REM collected production information from historical sales trends, point-of-sale systems at key customers and current manufacturing output, producing more than 17,000 new data elements to update the MRP software. In addition, the data entry component of the MRP system was refined to make sure that it received up-to-the-minute data about customer orders and delivery timelines. With more accurate forecasts about plant capacity, customer replenishment requirements and future demand for specific products, the MRP software could be programmed to limit its requests for materials to orders that were actually going to be filled. Primarily because of this MRP upgrade, Leiner cut its inventories of raw materials and finished goods by $50 million.
During the MRP upgrade, REM learned that corrupted data were poisoning Leiner's product pricing strategies. The company was setting wholesale rates based on the production pace of its fastest machines. But since very few of its products were actually manufactured that quickly, Leiner was underpricing itself and severely crimping its own margins. REM re-created product costing models based on the actual machines used to manufacture them and the models provided data to negotiate new and more profitable terms with customers when sales contracts expired.
"Sometimes companies just stick values into formulas without realizing how critically important data is," say Howard Weisz, an REM consultant. "It's not uncommon to have a team of people making decisions that aren't the best because their data is wrong."
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