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