The lead-time gap

Most organisations face a fundamental problem the time it takes to procure, make and deliver the finished product to a customer is longer than the time the customer is prepared to wait for it. 

The customer s order cycle refers to the length of time that the customer is prepared to wait, from when the order is placed through to when the goods are received. This is the maximum period available for order fulfilment. In some cases this may be measured in months but in others it is measured in hours. 

Clearly the competitive conditions of the market as well as the nature of the product will influence the customer s willingness to wait. Thus a customer may be willing to wait a few weeks for the delivery of a car with particular options but only a day for a new set of tyres. 

Reducing the gap can be achieved by shortening the logistics lead time (end-to-end pipeline time) whilst simultaneously trying to move the customer s order cycle closer by gaining earlier warning of requirements through improved visibility of 

In many cases companies have an inadequate visibility of real demand. By real demand we mean the demand in the final marketplace, not the derived demand that is filtered upstream through any intermediary organisations that may lie between the company and the final user. The challenge is to find a way to receive earlier warning of the customers requirements. What we frequently find is that, firstly, the demand penetration point is too far down the pipeline and, secondly, real demand is hidden from view and all we tend to see are orders. Both these points need further explanation we will deal with the concept of the demand penetration point first. 

---

This is the basis of the lead-time gap. Figure 4.2 highlights the problem. 

This twin-pronged approach of simultaneously seeking to reduce the logistics lead time whilst extending the customer s order cycle may never completely close the lead-time gap. However, the experience of a growing number of companies is that substantial improvements can be made both in responsiveness and in the early capture of information on demand - the end result of which is better customer service at lower cost. 

The accuracy of short term statistical forecasts can be easily measured but since the goal of the S OP system is to reduce the dependency on the forecast we should also measure the lead-time gap at the individual item level. The aim should be to progressively reduce this gap by a concerted focus on time compression and improved visibility. 

Differences between P-times and D-times are referred to as the lead-time gap. The gap has strategic implications for marketing, product development and process development. P-time can be reduced by a six-stage process control, simplify, compress, integrate, coordinate, automate. 

The only way to avoid this is by strict analysis of the supply chain from the customer order to final product delivery. Definition of the optimized (theoretical) process and sequential work towards a high service level approach allow the identification of gaps, and of opportunities which might not always be the cheapest (ship versus train versus plane) but could be the most effective way to reduce capital costs and shorten planning scope - an important aspect, especially in volatile customer markets with long production processes on the (chemical) supplier side. As in the case of CIP, this needs clear parameters, KPIs, commitment from all players, and regular tracking. The most important parameters are the lead time for all products, optimal lot sizes, replenishment points, and safety inventories. 

Lead time is the gap between the time an order is placed and when it is received. In our discussion, we denote the lead time by L. In the B M example, L is the time between when B M orders phones and when they are delivered. In this case, B M is exposed to the uncertainty of demand during the lead time. Whether B M is able to satisfy all demand from inventory depends... 

Neurofibromatosis type 1, a familial disorder characterized by multiple benign tumors of certain glial cells, is due to a mutation in the gene that codes for one form of GAP that regulates Ras [34], The mutation in GAP that leads to neurofibromatosis renders GAP unable to activate GTPase activity of Ras. This means that the GTP-bound form of Ras remains active for abnormally long periods of time, which leads to abnormal cellular growth. 

Thermal expansion differences exist between the tooth and the polymer as well as between the polymer and the filler. The tooth has a thermal expansion coefficient of 11 x 10-6/°C while conventional filled composites are 2-4 times greater [63, 252], Stresses arise as a result of these differences, and a breakdown between the junction of the restoration and the cavity margin may result. The breakdown leads to subsequent leakage of oral fluids down the resulting marginal gap and the potential for further decay. Ideal materials would have nearly identical thermal expansion of resin, filler, and tooth structure. Presently, the coefficients of thermal expansion in dental restorative resins are controlled and reduced by the amount and size of the ceramic filler particles in the resin. The microfilled composites with the lower filler loading have greater coefficient of thermal expansions that can be 5-7 times that of tooth structure. Acrylic resin systems without ceramic filler have coefficients of thermal expansion that are 9 times that of tooth structure [202-204, 253],... 

The interpretation of the Li abundance gap using a diffusion model has been questioned because of the observed absence of abundance anomalies of heavy elements in F stars (Boesgaard and Lavery 1986 Thevenin, Vauclair and Vauclair 1986 Tomkin, Lambert and Balachandran 1985) where Be has been observed to be underabundant. Such anomalies had been predicted on account of the diffusion calculations in the absence of any mass loss (Michaud et al. 1976, Vauclair et al. 1978b). It has recently been shown that even a very small mass loss was sufficient to reduce considerably any expected overabundance in F stars. On Fig. 2c of Michaud and Charland (1986), it is shown that a mass loss rate of 10 15 Mo yr-1 is sufficient to keep the Sr overabundance, below a factor of 1.5 while Sr would be expected to be more than 100 times overabundant in the absence of mass loss (Michaud et al. 1976). The presence of even a very small mass loss rate considerably limits any overabundance when the radiative acceleration and gravity are close to each other as is the case for heavy elements in stars cooler than Teff = 7000 K. The same small mass loss rate reduces the Li overabundance in stars of Teff = 7000 K or more where Li is supported. As shown in Fig. 4 of Michaud (1986), the same mass loss rate of 10 15 Mo yr 1 eliminates the Li overabundance of a factor of 10 expected in the absence of mass loss at Teff = 7000 K. It has now been verified that the presence of mass loss cannot increase the Li underabundance that diffusion leads to beyond a total factor of 30 underabundance. 

Changes in continuity of memory over time—either an implicit feeling that continuity is present or an explicit checking of memory that shows current experience to be consistent with continuous memories leading up to the present, with gaps suggesting an altered state... 

---