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The Beginning of Superior Inventory Management – Accurate Demand Planning and Forecasting
When retailers seek help with issues relating to inventory management, they are usually concerned about an increasing level of out-of-stocks, which are leading to lost sales and customer service complaints, or over-stocks, which are resulting in slow inventory turnover and a build up of dead inventory. In fact, out-of-stocks and over-stocks are actually the flip side of the same inventory management coin.
Any effective initiative to resolve these issues must address the core structural causes of these inventory management problems. Superior inventory management begins with timely, accurate, detailed demand forecasts.
It is critical to differentiate between demand planning and purchase planning. Demand planning is the sales plan from which inventory planning, purchase planning and replenishment parameters are built. It is impossible to plan inventory and purchasing activities or build replenishment parameters without a detailed forecast of what will be sold, how much will be sold, when it will be sold, the channels it will be sold through, and who the ultimate customers will be. And yet, all too frequently replenishment parameters are rolled over, existing purchasing patterns continue, and inventory is allowed to ebb and flow as if on auto- pilot. The result is out-of-stocks and over-stocks as demand changes.
Without highly reliable forecasts, retailers must attempt to strike a delicate balance between carrying too little or too much stock. Frequently, they feel compelled to protect themselves against out-of-stocks and backorders by stocking layers of additional inventory in reserve, unnecessarily tying up valuable resources that could be used in more productive ways to serve customers and grow the business.
Review Historical Sales Data
Accurate demand planning and forecasting begins with a thorough review of historical sales data. It is critical that sales not made from stock, special orders, large closeout sales and any other extraordinary sales be excluded from this historical data. Most demand planning and forecasting software packages will exclude these sales if the forecasting software is fully integrated with order management software, and those excluded orders have been properly tagged or exclusion parameters have been loaded into the system. It’s also critical that lost sales due to out-of-stocks are also factored in so that the history reflects actual demand rather than just sales.
It is important that the planning process drills down to the lowest possible level so that every category, sub- category, style or SKU is reviewed not just for potential opportunities and current sales trends, but also for the potential negative impacts of increased competition, emerging technology, changes in promotional patterns and new product introductions. For distributors and wholesalers this may mean planning at the individual SKU level. Planning can be further refined by breaking key categories and items down by customer type, key customer, and even key customer by shipping location. Important sales trends, both positive and negative can be identified, and important historical events, such as unusual local weather, can be taken into account.
Once the historical sales data has been reviewed and adjusted, the data will frequently be averaged or smoothed to eliminate any remaining fluctuations in the sales pattern. Smoothing, however, can often lead to problems if not done carefully. For instance, using a three week moving average to smooth weekly historical sales may lead to out-of-stocks or over-stocks if sales are typically heavy at the beginning or end of each month. Utilizing monthly historical data rather than weekly data may seem like a reasonable way to simplify the planning process, but may in fact have the unintended consequence of smoothing historical sales in a way that may conceal meaningful sales patterns.
Understand Selling Characteristics
It is imperative to clearly understand the selling characteristics of each category, sub-category, item or SKU. These characteristics will determine the appropriate methodology for developing a forecast, as well as the level of detail required in the forecast. The most obvious characteristic is the degree of seasonality. Items which exhibit little sales fluctuation from month to month throughout the year require a very different forecasting methodology than items which exhibit significant seasonal sales fluctuations.
For seasonal items, most forecasting methods will start with the prior year’s sales by week or month, apply some smoothing technique, and then apply a current trend factor to arrive at a current year forecast for the corresponding time frame. For non-seasonal items, sales by week or month for the most recent weeks or months will be used as a starting point, smoothed and adjusted for the trend factor to arrive at a current forecast. In fact, it is very easy to completely overlook non-seasonal items when forecasting. It may seem sufficient to merely update replenishment parameters. A thorough analysis of non-seasonal items is necessary, however, to identify sales trends which may affect future sales volume, as well as to build an overall sales forecast.
Another characteristic which must be clearly understood is the sales velocity of an item. Sales velocity is defined as the number of orders an item generates over a given period of time. Items with high sales velocities generate a substantial number of orders during a given period of time, which makes their sales volume during that period more predictable than items with low sales velocities, which may only generate orders sporadically.
It is important to note that sales velocity is not the same as sales volume. For example, an item that generates 50 orders of 2 units each over a given period of time will have the same sales volume as an item which generates 2 orders of 50 units each, but the velocity of each item will be dramatically different. Clearly, the sales history of the item which generates 50 orders will lead to a forecast that will be more meaningful in the development of future inventory plans, purchasing needs and replenishment parameters than the sales history of the item which generates only 2 orders.
Many distributors group their items by sales volume using an A-B-C-D system. A items are those items which generate the vast majority of their sales volume, while B, C and D items generate increasingly smaller fractions of their sales volume. As a result, frequently these distributors will forecast and replenish their A items using one methodology, their B items another, and so on. However, while the grouping of A items may be made up primarily of high velocity items, every item will not necessarily be an A item. Conversely, while the grouping of D items will most likely be made up entirely of low velocity items, it is likely that within the B and C groupings that there will be a mix of both low and high velocity items. Utilizing sales velocity rather than A-B-C-D groupings to determine the appropriate forecasting methodology will result in forecasts that will result in fewer out-of-stocks and over-stocks.
Low velocity items may include supplementary items, which may be necessary to complete a given customer order for high velocity items, such as specialty ceramic tile trims to go with standard field tile. Low velocity items may also include complementary or customer convenience items, which are stocked so customers can purchase all of their needs in “one stop”. For low velocity items which exhibit an irregular sales pattern the forecast may reflect smoothed historical sales data, but that forecast would be less meaningful for actual replenishment than the average customer order quantity. As a result, the replenishment parameters would likely be calculated based on maintaining enough quantity in stock to support a given number of orders at the typical or usual sales order quantity.
Bottom Up versus Top Down Planning
As SKU’s are rolled up into sub-categories, and then into categories, the resulting planned sales increase can be evaluated in the aggregate at the total company level. This “bottom up” planning must be done in units. Regardless of what the actual unit of measure is, the obvious purpose of developing any demand plan or forecast is to provide the information necessary to build replenishment parameters, plan purchasing activities and issue actual purchase orders to vendors.
As the demand plan is being developed, however, unit plans must also be “dollared out.” As management assesses the overall market environment and the strategic opportunities and risks for the company, they will likely establish a financial budget, critical for cash flow forecasting, from the “top down”, which will be stated in dollars. As managers develop and roll up their forecasts, they must be careful that their “bottom up” unit plan remains in line with the financial “top down” dollar plan, and be prepared to adjust the unit plans accordingly.
Frequently, “bottom up” unit plans will forecast a sales increase significantly greater than the company’s “top down” financial budget. The reason for this is that in the course of building a “bottom up” unit plan far more items or categories are likely to be planned up than planned down. The natural tendency is to plan sales increases, especially in organizations with multiple buyers who are evaluated on their ability to generate sales increases with their items, categories and departments. Clearly, every item, category or department is not going to generate an increase, and companies which discourage their buyers from forecasting sales decreases are building in potential inventory problems right from the very beginning of the process.
Forecasts Need To Be Continually Updated
While demand planning and forecasting are generally thought of as a process that takes place at the beginning of each year or selling season, superior inventory management requires that forecasts remain dynamic and be continually updated to reflect the most current market conditions and sales trends. It does little good for a company to have taken the time to carefully forecast demand for the upcoming season or year, only to open the door to out-of-stocks or over-stocks by failing to update those forecasts on a continual basis. Static forecasts which have not been updated will invariably lead to faulty purchasing decisions.
Updating forecasts may be as simple as carefully monitoring the sales trend and updating the forward periods accordingly. In other cases there may be leading indicators that can be utilized to continually adjust the forecast. For those items or categories where customer orders are booked well in advance of actual ship dates, advance bookings may be able to be used as a leading indicator. In order for this to be an accurate indicator, however, prior year orders must be cross referenced between the period in which the order was booked, and the planned and actual ship date. Without a fairly sophisticated order management system to track this information, and very careful assessments of individual factors which may be impacting the timing of the placement of orders this year versus last year, utilizing advanced bookings to make significant adjustments to the forecast may by itself lead to variances between planned and actual sales, resulting in out-of-stocks or over-stocks.
A far more accurate leading indicator of sorts is, in fact, the demand forecast of a company’s customers. In fact, the closer any forecast is to the ultimate point of sale the more accurate and timely it will be.
Vertical information sharing throughout the supply chain is at the cutting edge of efforts to improve forecasting accuracy. The Collaborative Planning, Forecasting and Replenishment Committee is made up of retailers, manufacturers, and solution providers dedicated to this effort. It was formed to create collaborative relationships between buyers and sellers through shared information and co-managed processes. The Committee states that by “integrating demand and supply side processes CPFR® will improve efficiencies, increase sales, reduce fixed assets and working capital, and reduce inventory for the entire supply chain while satisfying consumer needs.” This group has developed a set of guidelines for developing business processes that enable collaboration across a number of buyer/seller functions.
The potential of collaborative forecasting is to finally fully rationalize the supply chain so that unnecessary inventories can be completely eliminated rather than inevitably building up with the company in the chain with the least economic leverage. In a supply chain where information is not shared, but, in fact, is closely held, it is inevitable that inventory risk will be pushed back by the companies with the greatest leverage onto the companies with the least. But the mere presence of excess, unnecessary inventory anywhere within the supply chain inflates costs for every member of the chain, and ultimately weakens the chain.
Measure and Analyze Variances Between Forecast and Actual
Finally, once a forecast has been developed, it is critical to measure its accuracy. It’s important to recognize that a forecast is just that, a forecast. There will always be variances between forecasted and actual demand. By measuring and then analyzing those variances, the factors that contribute to variances can be identified and strategies can be developed to account for them, so that future forecasts are that much more accurate, and variances minimized.
The greatest challenge to finally achieving superior inventory management, and maximizing the return on inventory investment, lies in developing accurate forecasts. Much work has been done over the past ten to fifteen years to rationalize processes in the supply chain, and eliminate unnecessary inventory. This has led directly to truly astounding cost saving and productivity gains. But for all the gains that have been made on the supply side of the inventory equation, the greatest opportunity for additional gains today is on the demand side. Not only does superior inventory management begin with accurate demand planning and forecasting, but making the commitment to developing accurate forecasts, continually updating them, and measuring their accuracy against actual sales also offers independent retailers the greatest opportunities today to maximize their return on inventory investment.
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