September 7, 2012
How can a fab shop use customers' sales forecasts effectively for in-house production planning, but at the same time not rely on them too much? It's a balancing act, but if done effectively, it can prove to be highly beneficial for a fabricator.
All job shop manufacturers struggle to balance customer demands for on-time products with the need to minimize finished-goods inventory to protect the balance sheet. And the more a job shop produces custom work, the more difficult the challenge.
To address this challenge, many fabricators engage in sales forecasting. They study a customer’s ordering history, growth rate, and other key factors in hopes of anticipating with some degree of accuracy how much work to expect within a given time frame. When done well, sales forecasting enables job shops to closely align their purchasing, job scheduling, and production processes with customer needs. When done poorly, it can lead to excess inventory, missed delivery dates, and disappointed customers.
In many job shop environments, larger customers—such as big machine tool builders, automotive OEMs, and commercial and defense aerospace companies—often will provide their own sales forecasts. These typically look one to six months ahead, with some very large-volume customers projecting as much as 12 months out. These customer-supplied forecasts enable the job shop to more accurately predict its upcoming workload and dedicate the right amount of labor and resources for the anticipated work volume. These forecasts also give the job shop more time to order parts and raw materials from vendors, especially for parts that require long lead-times.
Unfortunately, customers who provide their own sales forecasts typically represent a small percentage of the customer base, which means the job shop must guess on its own for the vast majority of customers. If management guesses incorrectly, it can expose the company to a variety of financial obligations and liabilities. Overestimating expected sales volume can lead to excess finished-goods inventory, a higher cost of goods sold, and a weak balance sheet. But without this type of advance planning, the job shop may find itself short of the parts, labor, and resources to meet incoming customer demands.
A sales forecast allows the job shop to appropriately allocate labor, materials, and resources to anticipate future customer demand. The challenge is to determine at what point management can consider a forecast solid enough to order materials, plan for labor pool changes, increase capital expenditures, and begin production.
Most sales forecasting is based on a customer’s sales volume history and the type of parts being made. For example, customers that order mature, repetitive parts are much easier to predict than customers who continually order new parts. So studying a customer’s sales history, current growth rate, industry trends, and other key factors plays a critical role in the sales forecasting process.
Many times, however, the greatest challenge comes from trying to determine whether a part will change, at what point, and what kind of changes might occur. Manufacturing parts ahead of a formal release poses increased risk for the job shop. For example, a customer might provide three weeks’ notice to ship parts, yet between material availability and production time it might take the shop six weeks to produce the order. To ensure on-time delivery, the shop makes the parts three weeks before receiving a formal notice from the customer. If the customer elects to change the design of the part, the premade parts become unusable at worst, or can be modified at best.
To limit its exposure, the job shop must somehow balance the ability to anticipate the customer’s needs with protecting its own cost structures and margins. The answer to this conundrum lies in producing parts in relatively small lots. This enables the job shop to respond quickly once material becomes available, and to avoid producing unusable parts or having excess finished-goods inventory on the books.
To produce parts in small lots requires reducing non-value-added costs throughout manufacturing. Depending on part complexity and the number of machines a job requires, setup time often represents the largest non-value-added cost.
To mitigate setup costs, manufacturers often increase lot size, thus reducing the impact the lost non-value-added time has on any one part. However, bigger lot size has the negative effect of dramatically increasing response time while reducing flexibility. Therefore, the ongoing effort to reduce or virtually eliminate setup time is the key to the small-lot effort. The smaller the lot and the less time lost due to machine or process setup, the greater the job shop’s flexibility.
Knowing when to employ the small-lot approach starts with analyzing setup times for each machine in the manufacturing process. The less setup time the process requires, the smaller the lot size can be. If the quantity of a part is sufficient, a machine potentially can be dedicated to making only that one part. By reducing setup time to an absolute minimum, the lost cost per piece becomes very small.
Conversely, if it takes hours or even days to set up a machine, the non-value-added costs become exorbitantly high, forcing the shop to run large lots. It also ties up the machine for long periods of time, eliminating the ability to run other parts on that machine. This reduces the shop’s flexibility and its ability to respond to other customers with short lead-times. That’s why small lots are so critical to reducing cost. The shorter the setup time, the smaller the lot size the shop can run. The smaller the lot size, the quicker it can respond.
Job shops also can reduce material handling time with cellular manufacturing, which can provide immediate access to all resources necessary for production, minimize travel time, and improve product flow.
Suppose a part requires five different machines to produce. Instead of having those machines located in different areas throughout the shop, cellular manufacturing brings them together in one workcell that is dedicated to making the type of parts that require all or most of the machines in the workcell. As soon as one machine completes its process, the part immediately goes to the next machine in line, without having to be transported across the shop floor or waiting until the next machine becomes available. Having the machines more readily available and minimizing the lost time from excess part movement significantly reduce throughput time, making it more cost-effective to run small lots.
Cellular manufacturing works well with customers who frequently order the same part, and especially those who order large numbers of a part but take delivery in smaller quantities. A customer may order 5,000 parts but want only 100 delivered per day. With cellular manufacturing, the shop can cost-effectively make 100 parts at a time rather than making all 5,000 at once and have the extra parts reside in inventory.
The downside to cellular manufacturing is that the machines in the cell are no longer available for parts that require only one or two of the machines. Because of that, cellular manufacturing works best with parts that require all or most of the machines in the cell.
Another approach for improving response times involves bringing operations done by poor-performing vendors in-house. Outside vendors that serve job shops typically have very little control over their schedules, so it can be very difficult to predict their availability and turnaround time. They often struggle with quality issues too. If a vendor can’t deliver quality services consistently and in a timely manner, a shop may want to bring those processes in-house.
Before jumping into small-lot production, job shops first should determine their most important priority: effective in-house production planning, meeting the customers’ demand for finished goods, or deferring to the shop’s overall need for inventory reduction and sufficient inventory turnover. Clearly, the primary objective is to get customers what they need when they need it. But at what point does it put too much strain on inventory or become too disruptive to the planning process? This is the riddle that sales forecasting attempts to solve.
Many job shops use an automated enterprise resource planning (ERP) software system to perform the sales forecasting function. These systems offer features that make it easier to predict future sales and then schedule workflow according to those predictions. Regardless of whether a shop does it manually or electronically, a good sales forecasting system should include the following:
If a job shop chooses an ERP system, it should make sure the planning module fully integrates with the forecasting module. This shows how a sales forecast will affect the existing schedule. The system should allow “scenario planning,” whereby management inputs different forecasts into the system to compare the results. It should also be able to make schedule changes on short notice and regenerate the schedule as many times as necessary.
Balancing customer needs with internal demands for inventory control is a delicate task. With accurate sales forecasting, job shops can dedicate a percentage of available resources to prepare for the expected work volume. This requires having tight controls in place to reduce cost—through small-lot manufacturing and other approaches—while increasing the company’s flexibility and ability to respond to incoming orders on short notice. The quicker a company can react to a new demand, the less inventory it needs to carry, and the more it can protect the balance sheet.
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