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Cover-Time Planning, or Takt Planning, is presented. It is a system for calculating material requirements and start of purchases and production. Requested production rates of sales items, or alternative prefabricated modules in stock, are “broken down”, exploded, to create the need for components, for all underlying items (articles) in the Bill of Material. Inventory and already ordered replenishments are compared with the item’s desired production rate. How long already made actions are expected to cover the desired expected sales and production rates is compared with the item’s lead time; if a forward future shortage is likely the article is signalled for a refill. With examples is described how make-to-order production is done easily. The method is a type of reorder point system, but with time instead of quantity as decision variable. Unlike a traditional reorder point system increases and decreases of production can be planned. Future work load in various production sections can be estimated. It is described how an “Available-to-promise”-system should be designed and used. Cover-Time Planning (CTP) is a complete alternative to Materials Requirements Planning (MRP). CTP responds faster than MRP, since MRP for each structural level uses a batch size to “break down” and to magnify the need at the underlying level. In the end a large material acquisition needs to be ordered maybe just to build a single end item. CTP uses the end item requirement rates on all structural levels shifted with the lead times, when customer demand changes, the entire production chain react simultaneously.

A simple heuristic for vehicle routing - A variant of Clarke and Wright's saving method (International journal of Production Economics, 157 (2014), 74–79) contains a mistake that must be corrected. ...

We compare two different modes, i.e., dedicated mode and shared mode, used in packaging management in automotive parts logistics. In dedicated mode, every parts supplier uses his own packaging; while in shared mode, packages can be shared among the suppliers. For each mode, we calculate the total costs consisting of transportation cost and inventory holding cost, and prove that the total costs, the transportation cost and the inventory holding cost are all smaller under shared mode. We further illustrate the factors that influence the cost savings of shared mode, i.e., the total cost of dedicated mode minus that of shared mode. In particular, the cost savings are proved to be negatively related to the number of package categories, and are positively related to: (1) the demand gap of packages between areas, i.e., sum of the volume difference of every kind of packages that transported in and out of one area; (2) the failed ratio factor of the returned packages, i.e., the ratio of the packages that cannot be returned to the supplier because of broken, pilferage, misplace, etc.; and (3) the time savings of short distance transportation, i.e., the transportation time consumed between areas minus the transportation time within one area. Finally, numerical examples show that the cost savings can be considerable, and the number of package categories is the most important influence factor.

Efficient collection and distribution of items are important because fewer resources and less energy would be used and this would help the world to be more sustainable. Many goods are always in transit. Efficient collection from storage racks and depots, and efficient distribution to retailers and customers, etc., will decrease transport inventories. The paper presents a variant of the Clarke and Wright's saving method that is suitable for introducing the vehicle routing problem and the importance of efficient vehicle routing. The method uses only the first pair of calculated savings and uses these also when searching for complements or additions to an already decided route. The variant is simple, very fast and finds the best known solution to the Dantzig and Ramser (1959) –problem.

This article provides a short historical overview from Harris and his Economic Order Quantity (EOQ) formula to the Economic Lot Scheduling Problem (ELSP). The aim is to describe the development of the ELSP field from the EOQ formula to the advanced methods of today in a manner that suits master and graduate students. The article shows the complexities, difficulties and possibilities of scheduling and producing several different items in a single production facility with constrained capacity. The items have different demand, cost, operation time and set-up time. Set-up time consumes capacity and makes the scheduling more complicated. Idle time makes the scheduling easier but is bad from a practical point of view since it creates unnecessary costs due to low utilisation of the facility. A heuristic solution method is used on a small numerical example to illustrate different solution approaches. The solution method creates a detailed schedule and estimates the correct set-up and inventory holding cost even if the facility works close to its capacity.

We explore the issue of supply chain coordination by considering trade credit and its risk. It shows that, in a retailer–manufacturer system, the manufacturer may deliver less than the retailer׳s order quantity when the payment is delayed, and the manufacturer׳s risk aversion makes this result hold in a wider range. These findings are different from the common sense believed in the retailer–manufacturer coordination literature, which suggests the manufacturer to entice the retailer to enlarge the lot size. The manufacturer׳s decision of decreasing the order quantity prevents the supply chain from operating in the optimal situation. In order to coordinate supply chain, we propose a modified quantity discount based on both order quantity and advance payment which means the manufacturer offers quantity discount if the retailer pays part of the payment in advance and enlarges her order quantity. The ranges of advance payment ratio and the quantity discount are derived. Numerical examples are used to demonstrate the conclusions.

Organisations focus on reducing costs in their supply chains to increase net income. In order to reduce costs a company needs to know how to measure Supply Chain Cost (SCC). This paper is concerned with SCC and how measurements of SCC are and can be used in industry. The paper describes a suggested model for measuring SCC. Representatives from 30 companies in 10 different business sectors are interviewed about how they measure costs in their supply chains compared against this model. The focus is also on identifying the difference between SCC based on estimated standard cost compared to actual cost. A case study describes and shows the difference between measuring SCC based on calculated standard cost and measuring it based on actual cost. Our studies show that general thorough cost and supply chain analyses in many companies can be improved and further developed.

This paper is a part and contribution to develop flexible and robust supply chain forecasting systems under changing market demands. It suggests new tools and models to evaluate forecasting error measurements. The paper especially studies slow moving or intermittent demand, when for items the forecasting time periods often have zero demand. For the difficult to forecast intermittent demand the Croston forecasting technique is mostly regarded as a better choice than single exponential smoothing. These two methods, Croston and single exponential smoothing, together with two modifications of the Croston method, are discussed and evaluated with real intermittent data. The apprehended performance of a forecasting technique is dependent of the chosen measurement of forecast errors. The main purpose is to examine and evaluate different forecasting error measurements. Traditional measurements of forecast errors are studied, mean absolute deviation ( MAD ), mean square error ( MSE ), etc. together with new suggested error and bias measurements: “periods in stock” ( PIS ) and “number of shortages” ( NOS ). PIS considers the time aspect of the forecast error, NOS considers the development of cumulated forecast error ( CFE ). A complementary measure for descriptive statistics of time series is also suggested, mean average change. The conclusion, through principal components analysis ( PCA ), is that a single measure of forecast errors does not present the total different dimensions of the errors; complementary error measures must be used.

This article presents a simulation study over a small supply chain, where the amount of work-in-process (WIP) is restricted. The supply chain consists of five linked machines, or production facilities, with stochastic operation times. A number of test cases are made where the number of jobs in the machines and the buffer areas are restricted. The restrictions are designed both in the Kanban way, linked to every machine, and in the CONWIP way, connected only to the total production line. But no Kanban-cards and -cells are involved in our study, just restricted inventories between the machines. With the same amount of limited WIP, CONWIP-control compared to Kanban-control presents a higher throughput rate, less time between jobs out, but the jobs stay on average longer in the system. The stochastic operation times cause that the upstream machine sometimes consumes the jobs in a rate that the downstream machine does not catch up with, therefore all available storage room temporarily are not used. Kanban- and CONWIP-control presents the same amount of average outflow per time unit with the same variation in operation times and with the same amount of real average WIP. But Kanban-control causes a lower utilisation of present available storage room and storage equipment than CONWIP. The user of Kanban and CONWIP can only control maximum WIP and not average WIP; average WIP is a consequence of existing variations, so the difference is important. The coefficient of variation of the lead-times increases when WIP increases; this is very difficult to handle in practical applications. Restricted WIP that shortens the lead-time and decreases its variation is more important than if it is a “push” or “pull” system. Finally, it is argued that CONWIP-control is to prefer over Kanban-control in theory, but in practice there is a lack of CONWIP installation guidelines.

In this paper, a heuristic method is presented which gives a novel approach to solve joint replenishment problems (JRP) with strict cycle policies. The heuristic solves the JRP in an iterative procedure and is based on a spreadsheet technique. The principle of the recursion procedure is to find a balance between the replenishment and inventory holding costs for the different items by adjusting the replenishment frequencies. The heuristic is also tested according to an extensive test template and shows pleasing results. It also performs well in comparison with many other heuristics. Validerad; 2007; 20061117 (ysko)

This paper considers the scheduling of several different items on a single machine, in literature known as the economic lot scheduling problem, ELSP. One of the characteristics of this problem is that the demand rate is deterministic and constant. However, in a practical situation demand usually varies. In this paper we examine if a deterministic model can be used if demand is stationary stochastic. A dynamic programming approach from Bomberger (Manage. Sci. 12(11) (1966) 778) and a heuristic method from Segerstedt (Int. J. Production Econom. 59(1–3) (1999) 469) are used to calculate lot sizes for four items. The production of these items is simulated with different variations in demand rates. Our conclusion is that a deterministic model of this kind can be used in a practical situation where the demand rate is stationary stochastic, but the models must be complemented by a decision rule; which item to produce and when to produce it. In our tests the heuristic method and the dynamic programming approach perform rather similarly with respect to costs and inventory levels, but the dynamic programming approach results in more backorders when there is small variation in demand rates. This study indicates that the model used for determination of lot sizes is of less importance than the decision rule used for identification of the item to produce and when to produce it.

This paper considers an inventory control system, primarily for a finished goods inventory. The purpose is to create a procedure that can handle both fast-moving items with regular demand and slow- ...

To be a part of a supply chain network a company competing on the global market must mostly show short lead times and deliver at the right time. Is this really possible for a company placed in a rural or sparsely populated area? Pajala, a village and a municipality in the north of Sweden, has 7500 inhabitants in an area of 7900 km2 which means one inhabitant per km2. The number of inhabitants in the municipality of Pajala is decreasing. To stop this problem, Pajala needs more work opportunities and therefore they need more successful companies. Approximately 20 different conveyers/suppliers transport goods to and from companies in Pajala. But most of the conveyers/suppliers transport goods only once a week. Companies in Pajala, to be able to compete and be a part in a supply chain and a chain that competes with other supply chains, should have the opportunity to send and receive goods any day they want for shortening the delivery times to customers. One solution to this may be co-distribution. Co-distribution means that different conveyers/suppliers transport their goods in only one truck that goes for example in this case to and from Pajala. A preliminary study has been performed in Pajala to investigate if co-distribution has any benefits in rural areas. We find that the companies receive and distribute goods with a low frequency and that the trucks carrying loads to and from Pajala are often loaded less than 50%. This implies that to lower transportation costs and increase delivery services some sort of co-distribution is necessary to strengthen the capability of competing for the companies in Pajala. We have here studied specifically Pajala but we are convinced we would find almost the same result if we had studied other municipalities in rural areas of Sweden.

This paper presents an heuristic method for deciding what items and what order sizes to produce in a capacity constrained facility. The available inventories on hand decide in what order the production should be performed for avoiding shortages. The recursion procedure finds the same solution to the Bomberger (Management Science 12 (11) (1996) 778–784) problem as Doll and Whybark (Management Science 20 (1) (1973) 50–55).

This paper presents the main results from case studies in 10 Swedish manufacturing companies concerning their product costing. The main purpose of the study is to find out what models of product co ...

This paper presents a mathematical formulation of a capacity constrained multistage inventory and production control problem. It argues that most practical multistage inventory and production control problems can be summarised in the formulation. The model has a strong connection to the model of Billington, McClain and Thomas (1983) and is also inspired of a paper from Bard and Golany (1991). The revised Billington/McClain/Thomas model is formulated in a dynamic programming recursion. A numerical solution to a simple problem is presented. A scheduling problem concerning capacity constrained models are enlightened. Further, the paper points to differences and relationships between this capacity constrained model, MRP, Kanban and Cover-Time Planning.

This paper presents Cover-Time Planning, a method for calculation of material requirements. Cover-Time Planning resembles MRP but it lacks its automatic time-phasing ability. Basically Cover-Time Planning is a reorder point system based on demand rates and time instead of quantities.

This paper presents a model for inventory control where variation in lead times is allowed. No reorder point is computed. The mean value and the variance for the time between withdrawals, the order ...