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Case: New product introduction, how much is the first order?

Source: Shanghai Xianyun Automation Technology Co., Ltd. Release time: 2019/12/11 15:10:45 Clicks: 1434

The case companies have a strong fan group. Traditionally, the fan economy has been adopted. Although the brand economy is transitioning, the fan economy is still an important part of revenue. In order to maximize fan income, case companies continue to launch new products, basically a new product is launched every week.

The company follows a mid-to-high-end, differentiated line, with many varieties and small batches. The first batch of launches generally ranges from several hundred to several thousand. A variety of small challenges are that forecasting is difficult to make, either surplus or shortage, which is fully reflected in case companies.

No, there is a new product launched in April. It sold out three to four thousand in a few days, and quickly returned the order. When the second batch arrived, it was already in June. The "hot" brought by the explosion It is almost dissipated, and it has to be warmed up again. If there are explosive models, there will naturally be slow sales. For example, there is a product. In order to reduce the cost and quantity, the first batch of thousands of items was purchased. As a result, only a few hundred were sold, and huge resources for promotion were not used. Very high.

Demand uncertainty is high, and the risk of the first order is high, which puts great pressure on demand forecasting. Prediction risk is high, and no one is willing to take it. In the end, it can only be the boss's decision.

The boss makes the prediction, naturally the reason for the boss to make the prediction: he is the * experienced, * authoritative person, and * can bear the consequences of the failure of the prediction. However, as the business has doubled in succession, more and more new products, more and more things, bosses are getting busier, farther and farther away from consumers and front-line operations, and less and less time can be spent on demand forecasting. Many times, you can only shoot your head to make predictions, and its disadvantages are becoming more and more obvious. "I hope that the big boss understands the difference between scientific methods and head pats, especially one or two people's head pats," said an employee of the case company in a questionnaire survey.

The boss certainly understands the difference between scientific decision making and claps of head. No boss in the world would say that scientific decisions are bad, let me shoot my head. But the question is, when we lack effective mechanisms, can't effectively integrate cross-functional wisdom and information, and a group of people can't make effective decisions, what can the boss do without relying on his own head? Therefore, the problem here is not to change the behavior of the boss, but to improve the ability of collective decision-making, find better methodologies to organize data, integrate judgments, and make more accurate predictions.

This methodology is the Delphi * decision method. When the data is very limited, there are many unknown factors, and the uncertainty of the decision is very high, Delphi method is a very good choice. * As early as the end of World War II, the United States Air Force used this method to gather people in related fields to judge the trend of new technologies and guide the development of new weapons. In the 1950s, RAND further optimized this methodology, such as predicting the possible consequences of using nuclear weapons [1]. There are many variants of this methodology, but the key is similar: *, anonymous, multiple rounds of feedback and corrections, until consensus is reached after *, or a predetermined threshold is reached, such as how many rounds are repeated, as shown in Figure 1.

*: This is an early decision, and the data is very limited, so it depends on judgment. Who has the judgement? *. However, the more * it becomes, the narrower the contact surface is-if you want to master a field, you can only choose to dig a little deeper. The development of new products and technologies involves a wide range, and any one of them cannot be fully covered, so please judge from various aspects to increase the accuracy of prediction.

Anonymity: This is to avoid the influence of authority, title, position, personality, reputation, etc., to avoid strong functions affecting weak functions, and strong people affecting weak people. You know, the boss sits there, no matter how democratic the company is, everyone will consciously or unconsciously follow the boss's mind. Strong functions were present, and their arms were thick and their fists were large, and they were in charge after 80%. Not to mention the celebrity halo. In the prediction of new products, anonymity is to omit titles, names, and sometimes even functions when summarizing forecast data. This also allows everyone to make their own judgments more confidently and independently.

Back to back: This is to further reduce the game between functions and functions, and between people. Everyone sat together to discuss. The process of expressing their opinions was actually a game. Each person represented his own function. What he said and what judgment he made depended largely on other functions and what others took. Position, and habitually hiding, such as unwilling to speak first-speak like negotiations, whoever shows the card first, who is passive in the game. Everyone makes judgments back-to-back, and collects and organizes them by special persons, which helps reduce mutual influence and game with each other, and makes it more likely that everyone will make objective judgments as *.

0422-1.png Figure 1: Diagram of Delphi * decision method

Source: Delphi Method, Dr. Phil Davidson, University of Phoenix, http://research.phoenix.edu/content/research-methodology-group/delphi-method

Another important feature of Delphi's decision-making method is that it has multiple rounds of circulation: each * anonymous, back-to-back makes judgments, which are collected and organized by a special person, and provided to all members to become the basis for the next round of judgment. On the basis of this, people who adjust their decision-making will generally be more consistent. In terms of forecasting, the standard deviation of the predicted value is smaller and the dispersion is smaller; so again and again, * finally reaches a certain degree of consensus, such as the average or The median is used as the final prediction [2].

* The essence of the judging method is not difficult to understand: "Three stinky tanners who have passed Zhuge Liang", everyone understands this truth; the real challenge lies in the following aspects. To the desired effect, and finally returned to the old way of the boss making plans, the boss patted his head.

First, the wrong * was selected. In terms of demand forecasting, * is defined as an in-depth understanding of the product in some way. For example, designers are familiar with whether this product is targeted at the general public or niche, product managers are familiar with the relationship between this product and other products (alternative or complementary, independent), e-commerce managers are familiar with promotion plans and consumer preferences, and purchasing managers are familiar with cost components. And * MOQ-most of this information is at the specific product level, which can significantly affect product demand. But in practice, people often determine * at the professional level, and that * team becomes the chief technology officer, marketing director, product director, etc. These people are * yes (at least nominally correct) in their respective fields, but they are often not product-specific *, have limited knowledge of specific products, and are not the best candidates to make product forecasts; they have high positions and heavy responsibilities He is immersed in various affairs and often does not have enough energy to invest in specific new product predictions. The judgment of specific products is often lower than the average level, which instead reduces the overall judgment ability and reduces the efficiency of decision-making [3].

Second, make * judgment the same as * slap your head. * Judgment still has to follow the decision-making process of "starting with data and ending with judgment", but the data is relatively small and more unstructured. For product-level *, they focus on their own field, often lacking information at the overall level. For example, is the relationship between this product and existing products competitive or complementary? What is the sales volume of existing related products? How about the demand forecast and actual sales of similar products in the past? What is the company's sales target for this new product? We cannot simply think of sales targets as demand forecasts, but the former is indeed a big consideration for the latter. This information needs to be organized by organizers and provided to each field * to minimize their learning curve, reduce the number of cycles, and reach consensus as soon as possible. Otherwise, Delphi's judgment method is nothing more than turning one person's head into multiple people's heads without changing the nature of the head and systematically improving the quality of decision-making.


Figure 2: Several reasons for Delphi's failure

Third, there is a lack of feedback mechanisms, lessons learned and no experience, and the quality of decision-making cannot be continuously improved. * Judgment method is easy to be treated as a one-shot deal, but it is not: we have been introducing new products. One-shot deal is often done, and it has become a regular behavior. It needs continuous improvement to improve the accuracy of new product prediction. The key here is to form a closed-loop feedback mechanism. New products are on the market. With sales, we have to compare demand forecasts with each specific * forecast, and see where they are right and where they are wrong; what is wrong with a certain *? The other * has always been low, why? This is an important task for organizers: they need to collect these data and build a database to truly form collective wisdom and experience and improve the accuracy of future new product predictions. But in reality, many companies make * judgment method a one-shot deal, without feedback and summary, it is easy to form lessons, no experience, large randomness, low accuracy, and then return to the boss to shoot the head, or strong functions. It's up to the state.

For case companies, we have identified a specific product to be piloted to introduce Delphi * judgment. This product is in the late development stage, and the first order quantity needs to be determined. This product is also a key development object of case enterprises, which can get the attention of cross-functional teams.

Finding a good product, we further identified the new product prediction team around this product, including the following positions:

  • Product manager. The company adopts integrated product development (IPD). The product manager acts as the project manager and is fully responsible for the product. It plays a key role in the launch of new products, including the demand forecast of new products.
  • Designer. This position is responsible for the design of the product, and is familiar with the positioning of the product, such as the basic model or the popular model, and the impact of specific design on the demand, such as the choice of color and accessories.
  • Store manager. This position is responsible for the sales of major online stores, is familiar with consumer demand patterns, has the ability to make horizontal comparisons between different products, and is familiar with the new promotion plan.
  • Data engineer. Actual plan manager. This position is responsible for dataization, familiar with the demand history of various products, and can help demand forecast based on historical data.
  • Head of R & D. The person in charge of research and development is particularly familiar with the product and the user. She is very active in community groups such as WeChat group and QQ. She is familiar with user needs and knows a lot about users.
  • Head of sales. Although directly participating in new product development is less, but experienced, familiar with the feedback of the customer service team, can help make horizontal comparison of multiple products.
  • Supply chain leader. Familiar with product cost, minimum MOQ, supplier's ladder quotation, etc. I am also familiar with the commonality and delivery of key raw materials, and can help demand forecast from the supply side.
  • General manager. Founders are irreplaceable, especially in small and beautiful but fast-growing case companies. The general manager is deeply involved in product development, enterprise operations, pricing decisions, etc., and has extensive experience, and has always played a key role in the demand forecast of new products.

After determining the product and the * team, the organizer summoned the * team together, explained the Delphi's * judgment method, displayed a sample of the product, and started the * team's new product prediction process of "starting with data and ending with judgment".

First of all, what exactly do we want the team to predict? Seeing this comic in a WeChat group, the doctors went on strike in the streets, but the raised signs were crookedly written with prescription-like words, and no one could see what their appeal was. You know, this is teasing doctors who prescribe prescriptions like Tianshu [4]. In the * decision of new product introduction, what do we want these * to predict? The case companies said that the products were new and the first orders were forecasted to be rampant. This is unclear. There are two problems:


Figure 3: Unclear appeal, another kind of garbage in, garbage out

First, the forecast has two dimensions, quantity and time. The first order has only quantity and no time. How long is the first batch of products to be sold out? We keep our time open, and * members have to make their own assumptions; if the assumptions about time are different, the predictions of * members must be different, lack consistency, there is no comparability, and they become garbage in and garbage out.

Second, the "first order quantity" asks for supply, that is, how many orders are placed with the supplier; and most of the team * are familiar with demand, but cannot make a good judgment on supply: the first batch How much goods are produced is also affected by production capacity, procurement cycle, scale efficiency, etc. For example, if the production capacity is limited, the order quantity may be reduced; the longer the supply cycle, the larger the order quantity; * The higher the minimum order quantity, the first order quantity May be bigger.

After discussing with the case company, we decided to ask the team two questions: (1) How much do you think might be sold within 30 days of the new date? (2) In addition, how many raw materials (long-cycle materials) do you think can be prepared so that we can quickly reverse the order once it needs to be replenished? We particularly remind that we hope that these raw materials can be consumed within 3 to 6 months, in order to control the risk of stagnation of raw materials.

Question 1 actually asks about the 30-day forecast of the new demand. There is time and quantity, and the limits are very clear. In the last new stage, the sales, design, and product management functions of the * team were deeply involved, and they had certain experience in the past new products, and were able to make certain prejudgments for the next new products.

Question 2 asks about the forecast for the second and third months. The entire supply chain cycle of the case company is approximately 3 months: 1 month for raw material procurement, 1 month for processing into semi-finished products, and 1 month for processing into finished products. Due to the high uncertainty of Shangxin, case companies usually adopt long-term unified procurement of raw materials to obtain certain scale benefits, but only process part of them into finished products to control the inventory risk of finished products. From the beginning of the new one, the sales volume on the first day is very valuable. Decide whether to quickly process the remaining materials into finished products (this will be discussed later).

We understand that the first problem is relatively more intuitive and should be able to get good judgments; the second problem is relatively more difficult to make judgments. * The team needs to better understand the entire supply cycle in order to improve the prediction and management of long-cycle materials ( It was found in specific cases that * we judged the second question not very well).

Identified the specific questions to ask. What information is known next and can be provided to the * team to shorten the learning curve and improve the quality of decisions? You know, * decisions are not about clap your head; they are making judgments based on past experience. And the past experience, in fact, a lot of it has been condensed in the data, such as sales, we can aggregate such data, unified to the * team. This is especially helpful for functions that do not deal with data often, such as design and product management. Otherwise, they will just clap their heads.

In the case, we decided to provide two types of information: (1) information about similar products, such as sales during different periods; (2) specific information about the product, such as product positioning, minimum MOQ of raw materials, and supplier Ladder quote.

Similar product information: Last year, there were 6 similar products, when were they listed, how many were produced in the first batch, how many were sold in 30 days, how much were the first month sold out, and how many were sold in three and six months. This gives the * team more information on horizontal comparisons (see Figure 4). The organizer originally provided similar products from the previous year, but there were two problems: (1) it was a long time ago, and the sales data of the new period was incomplete; (2) there were too many products in two years, which easily caused information overload and affected * Member effective judgment. Organizers also want to provide four-quadrant classification of each product (the level of output and the level of sales), as well as the return rate. If there is also a problem of information overload, they will be removed.


Figure 4: The historical sales volume of the product can be referred to (case confidential corporate information is obscured)

Information about this product: Is the product positioning high, medium or basic (by volume)? Is the relationship with existing products complementary, cannibalizing, or independent? These will affect the prediction of the product. In addition, the forecast also requires purchasing related data, such as the minimum MOQ of the main materials, the supplier's stepped quotation, whether there are special processes, such as dyeing, surface treatment, etc., and the corresponding additional costs (with the minimum MOQ) related).

The organizer prepared the key background information, edited it into a page of A4 paper size, and convened a meeting to explain the methodology of Delphi's judgment method, display product samples, and distribute basic background information to everyone. Forecasting process.

In the first round, each * returned to his office, analyzed the existing data, collected more information, made independent, back-to-back judgments, scanned the QR code, and passed the questionnaire star (www.wjx.com) Fill in the following information online (Figure 5):

(1) New product forecast: What is the sales forecast for the last 30 days? How much raw material inventory should be prepared; (2) Reasons based on; (3) Suggestions for further improvement of the methodology, such as what useful information has not been provided and which suitable people have not been invited to the * team, etc.

After the questionnaire *, we asked each * to fill in their name and other information. This is mainly to urge the people to complete the task carefully, and also help the organizer to track the results of each judgment, with a view to improving cyclically. Here, the organizer clearly stated that the results will be fed back to the * team in an anonymous way so that the * people will have no worries and make * good judgments.


Figure 5: Questionnaire design for the first round of judgment

The organizer summarizes the results of the first round, such as the predictions made by each of the eight cross-functional *, what are the reasons, and additional information that can be provided (based on the first round of * feedback), and distributed uniformly To the * team. Hold a simple team meeting to make sure everyone understands the results of the first round. Note that the discussion is not for everyone to judge who is right and who is wrong. Otherwise, the strong function may affect the weak function, and the strong person may affect the weak person, thus affecting the objectivity of the second round of prediction.

The methodology of the second round is the same as that of the first round. Each * member decides independently and back-to-back, whether to modify his forecast result in the previous round, and state the reason.

The organizer summarizes the results of the second round. If the results of the second round are still quite different, enter the third round. It is hoped that * three more rounds, the * team can reach a considerable consensus on the prediction of the new product, and the organizer * finally takes the average or median method to determine the * final new product forecast.

For the new 30-day sales forecast, after two rounds of forecasting, the results of 7 digits * (one of them * vacations, only after the first round is eliminated) are significantly convergent, expressed in standard deviation and dispersion, that is, These two values are significantly reduced-the dispersion is reduced from xxx to xxx in the first round, and the standard deviation is reduced from xxx to xxx, as shown in Figure 6.

0422-6.png Figure 6: After two rounds of predictions, the dispersion of * predicted values has dropped significantly, and the predicted values have more convergence

However, for the prediction of additional materials, after two rounds of judgment, the dispersion of the prediction is still very large.

As shown in Figure 7, although the picture on the left looks more "convergent", this is misleading: the first round of predictions has many values from 0 to 3000, and the second round is mainly focused on 1000 and 3000- -3 digits * predict 1000, and 3 digits predict 3000. This should not be a coincidence, it looks more like a considerable part of the people are not at the bottom of their hearts, just "get together" and see who has good reason in the first round of predictions, and that person is the one: the person who predicted the first round of 3000 said that based on The cost and the differentiated design of this product, the target of this product should be the basic model and high sales volume; the one who predicted the first round of 1000 said that this design is "contrasting colors" with other, "not pleasing in the summer "It is expected to be pushed only in the spring and autumn seasons", and there are a number of similar products on the line from March to May-this model is obviously low sales and lacks stamina. These two reasons seemed to be very good, and everyone followed suit and became two factions. However, the gap between 1000 and 3000 can be too large, so we have not reached an agreement on this issue.

Of course, this also reflects that the case companies have not reached a consensus on the positioning of the product.

As a case designer, I think we asked the wrong question: * The main experience of product managers, designers, R & D leaders, etc. among members is before and after the new one. As for the new one, how much should be filled? Experience, of course, cannot make high-quality judgments. My suggestion is not to allow the * group to predict the subsequent stocking, but directly to the supply chain to estimate the demand for the next two months based on the 30-day sales forecast ---- we will talk about later, the new period The correlation between sales volume and subsequent normal sales volume is very high.


Figure 7: After two rounds of predictions, the predictions for additional stocks are still very different

This case is not over. As an internship case, after the actual sales data of the product comes out, we need to further summarize and analyze it to see how accurate the prediction is and summarize the experience to improve it. For example, rewards * accurate *, the group shares successful experiences, summarizes lessons learned, etc., so that more employees participate in learning, such as product managers should be deeply involved, everyone should be familiar with this methodology. This is considered a closed loop completion.

In addition, as part of the methodology, companies must decide (1) what products are suitable for the Delphi method? (2) Which function is responsible for maintaining this methodology and the processes behind it?

The Delphi method requires cross-functional participation, multiple rounds before and after, plus preparation of basic information and multiple rounds of data analysis. It requires more resources and belongs to "heavy weapons", so it cannot be abused. Companies must define appropriate products, such as greater uncertainty, more new features, and fewer similar products.

In the same way, the Delphi method needs the responsibility function to maintain. Overall, this is a decision-making process that is part of the requirements plan and can be maintained by the planning function. Of course, in many companies, especially small and beautiful companies, the demand planning for new products is often the responsibility of the product manager, and this process can also be responsible for product management. However, the problem is that product management is relatively decentralized. For example, there are multiple product managers, and the degree of overlap between some products is not high. It is difficult to find the best interface. The best practices are difficult to solidify and spread. The planning function, although naturally centralized, often has limited impact and it is difficult to effectively promote cross-functional collaboration. It may be possible to try this: As part of the integrated product development of new products (IPD), Delphi method is overall responsible for product management, but the planning function is responsible for organization and execution. This is the same reason that the product manager is responsible for the entire product development, and the design is responsible for the specific design work.

[1] Avella, JR (2016). Delphi panels: Research design, procedures, advantages, and challenges. International

Journal of Doctoral Studies, 11, 305-321. Retrieved from http://www.informingscience.org/Publications/3561

[2] The median is used to avoid the influence of extreme values on the average. For example, Gates and I have an average wealth of $ 49 billion, but you know it's all Gates money. This is also to reduce the game, such as a function deliberately inflated. Of course, when the dispersion of the data is small, the average value should be closer to the truth than the median, which is more meaningful mathematically.

[3] I asked a company why the return period is long? The answer was mainly slow decision-making and long material procurement cycle. Long cycle materials are easy to understand. Decision-making is slow, of course, among those decision-makers: the higher the position, the more they lack product-level information, and they are required to make product-level decisions, such as demand forecasting. Of course, they are not assured, so procrastination prevails. As a result, it is too late to make predictions; the predictions are wrong, and there is no way to correct them as soon as possible.

[4] I saw a related doctor strike on The Telegraphs in the UK, and another picture of this angry doctor holding a sign. It was clearly written on it, and I knew that it was in the WeChat group. This picture was taken by PS. Original: Doctors' strike: the NHS exists to serve patients, not keep doctors happy, by JAMES KIRKUP, www.telegraph.co.uk.

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